This is page 2 of 4. Use http://codebase.md/disler/just-prompt?lines=true&page={x} to view the full context.
# Directory Structure
```
├── .claude
│ ├── commands
│ │ ├── context_prime_eza.md
│ │ ├── context_prime_w_lead.md
│ │ ├── context_prime.md
│ │ ├── jprompt_ultra_diff_review.md
│ │ ├── project_hello_w_name.md
│ │ └── project_hello.md
│ └── settings.json
├── .env.sample
├── .gitignore
├── .mcp.json
├── .python-version
├── ai_docs
│ ├── extending_thinking_sonny.md
│ ├── google-genai-api-update.md
│ ├── llm_providers_details.xml
│ ├── openai-reasoning-effort.md
│ └── pocket-pick-mcp-server-example.xml
├── example_outputs
│ ├── countdown_component
│ │ ├── countdown_component_groq_qwen-qwq-32b.md
│ │ ├── countdown_component_o_gpt-4.5-preview.md
│ │ ├── countdown_component_openai_o3-mini.md
│ │ ├── countdown_component_q_deepseek-r1-distill-llama-70b-specdec.md
│ │ └── diff.md
│ └── decision_openai_vs_anthropic_vs_google
│ ├── ceo_decision.md
│ ├── ceo_medium_decision_openai_vs_anthropic_vs_google_anthropic_claude-3-7-sonnet-20250219_4k.md
│ ├── ceo_medium_decision_openai_vs_anthropic_vs_google_gemini_gemini-2.5-flash-preview-04-17.md
│ ├── ceo_medium_decision_openai_vs_anthropic_vs_google_gemini_gemini-2.5-pro-preview-03-25.md
│ ├── ceo_medium_decision_openai_vs_anthropic_vs_google_openai_o3_high.md
│ ├── ceo_medium_decision_openai_vs_anthropic_vs_google_openai_o4-mini_high.md
│ └── ceo_prompt.xml
├── images
│ ├── just-prompt-logo.png
│ └── o3-as-a-ceo.png
├── list_models.py
├── prompts
│ ├── ceo_medium_decision_openai_vs_anthropic_vs_google.txt
│ ├── ceo_small_decision_python_vs_typescript.txt
│ ├── ceo_small_decision_rust_vs_prompt_eng.txt
│ ├── countdown_component.txt
│ ├── mock_bin_search.txt
│ └── mock_ui_component.txt
├── pyproject.toml
├── README.md
├── specs
│ ├── gemini-2-5-flash-reasoning.md
│ ├── init-just-prompt.md
│ ├── new-tool-llm-as-a-ceo.md
│ ├── oai-reasoning-levels.md
│ └── prompt_from_file_to_file_w_context.md
├── src
│ └── just_prompt
│ ├── __init__.py
│ ├── __main__.py
│ ├── atoms
│ │ ├── __init__.py
│ │ ├── llm_providers
│ │ │ ├── __init__.py
│ │ │ ├── anthropic.py
│ │ │ ├── deepseek.py
│ │ │ ├── gemini.py
│ │ │ ├── groq.py
│ │ │ ├── ollama.py
│ │ │ └── openai.py
│ │ └── shared
│ │ ├── __init__.py
│ │ ├── data_types.py
│ │ ├── model_router.py
│ │ ├── utils.py
│ │ └── validator.py
│ ├── molecules
│ │ ├── __init__.py
│ │ ├── ceo_and_board_prompt.py
│ │ ├── list_models.py
│ │ ├── list_providers.py
│ │ ├── prompt_from_file_to_file.py
│ │ ├── prompt_from_file.py
│ │ └── prompt.py
│ ├── server.py
│ └── tests
│ ├── __init__.py
│ ├── atoms
│ │ ├── __init__.py
│ │ ├── llm_providers
│ │ │ ├── __init__.py
│ │ │ ├── test_anthropic.py
│ │ │ ├── test_deepseek.py
│ │ │ ├── test_gemini.py
│ │ │ ├── test_groq.py
│ │ │ ├── test_ollama.py
│ │ │ └── test_openai.py
│ │ └── shared
│ │ ├── __init__.py
│ │ ├── test_model_router.py
│ │ ├── test_utils.py
│ │ └── test_validator.py
│ └── molecules
│ ├── __init__.py
│ ├── test_ceo_and_board_prompt.py
│ ├── test_list_models.py
│ ├── test_list_providers.py
│ ├── test_prompt_from_file_to_file.py
│ ├── test_prompt_from_file.py
│ └── test_prompt.py
├── ultra_diff_review
│ ├── diff_anthropic_claude-3-7-sonnet-20250219_4k.md
│ ├── diff_gemini_gemini-2.0-flash-thinking-exp.md
│ ├── diff_openai_o3-mini.md
│ └── fusion_ultra_diff_review.md
└── uv.lock
```
# Files
--------------------------------------------------------------------------------
/src/just_prompt/tests/molecules/test_ceo_and_board_prompt.py:
--------------------------------------------------------------------------------
```python
1 | """
2 | Tests for the CEO and Board prompt functionality.
3 | """
4 |
5 | import pytest
6 | import os
7 | from unittest.mock import patch, mock_open, MagicMock, call
8 | import tempfile
9 | from pathlib import Path
10 |
11 | from just_prompt.molecules.ceo_and_board_prompt import (
12 | ceo_and_board_prompt,
13 | DEFAULT_CEO_MODEL,
14 | DEFAULT_CEO_DECISION_PROMPT
15 | )
16 |
17 |
18 | @pytest.fixture
19 | def mock_environment(monkeypatch):
20 | """Setup environment for tests."""
21 | monkeypatch.setenv("DEFAULT_MODELS", "a:claude-3,o:gpt-4o")
22 | monkeypatch.setenv("CORRECTION_MODEL", "a:claude-3")
23 | return monkeypatch
24 |
25 |
26 | class TestCEOAndBoardPrompt:
27 | """Tests for ceo_and_board_prompt function."""
28 |
29 | @patch("just_prompt.molecules.ceo_and_board_prompt.prompt_from_file_to_file")
30 | @patch("just_prompt.molecules.ceo_and_board_prompt.prompt")
31 | @patch("builtins.open", new_callable=mock_open, read_data="Test prompt question")
32 | def test_ceo_and_board_prompt_success(self, mock_file, mock_prompt, mock_prompt_from_file_to_file, mock_environment, tmpdir):
33 | """Test successful CEO and board prompt execution."""
34 | # Set up mocks
35 | mock_prompt_from_file_to_file.return_value = [
36 | str(Path(tmpdir) / "test_a_claude-3.md"),
37 | str(Path(tmpdir) / "test_o_gpt-4o.md")
38 | ]
39 | mock_prompt.return_value = ["# CEO Decision\n\nThis is the CEO decision content."]
40 |
41 | # Create test files that would normally be created by prompt_from_file_to_file
42 | board_file1 = Path(tmpdir) / "test_a_claude-3.md"
43 | board_file1.write_text("Claude's response to the test prompt")
44 |
45 | board_file2 = Path(tmpdir) / "test_o_gpt-4o.md"
46 | board_file2.write_text("GPT-4o's response to the test prompt")
47 |
48 | # Test our function
49 | input_file = "test_prompt.txt"
50 | result = ceo_and_board_prompt(
51 | abs_from_file=input_file,
52 | abs_output_dir=str(tmpdir),
53 | models_prefixed_by_provider=["a:claude-3", "o:gpt-4o"]
54 | )
55 |
56 | # Assertions
57 | mock_prompt_from_file_to_file.assert_called_once_with(
58 | abs_file_path=input_file,
59 | models_prefixed_by_provider=["a:claude-3", "o:gpt-4o"],
60 | abs_output_dir=str(tmpdir)
61 | )
62 |
63 | # Check that the CEO model was called with the right prompt
64 | mock_prompt.assert_called_once()
65 | prompt_arg = mock_prompt.call_args[0][0]
66 | assert "<original-question>Test prompt question</original-question>" in prompt_arg
67 | assert "<model-name>a:claude-3</model-name>" in prompt_arg
68 | assert "<model-name>o:gpt-4o</model-name>" in prompt_arg
69 |
70 | # Check that the CEO decision file was created correctly
71 | expected_output_file = str(Path(tmpdir) / "ceo_decision.md")
72 | assert result == expected_output_file
73 |
74 | # Check that both the prompt XML and decision files were created
75 | # The actual call may be with Path object or string, so we check the call arguments
76 | assert mock_file.call_count >= 2 # Should be called at least twice - once for prompt XML and once for decision
77 |
78 | # Check that one call was for the CEO prompt XML file
79 | expected_prompt_file = str(Path(tmpdir) / "ceo_prompt.xml")
80 | prompt_file_call_found = False
81 |
82 | for call_args in mock_file.call_args_list:
83 | args, kwargs = call_args
84 | if str(args[0]) == expected_prompt_file and args[1] == "w" and kwargs.get("encoding") == "utf-8":
85 | prompt_file_call_found = True
86 | break
87 | assert prompt_file_call_found, "No call to create CEO prompt XML file found"
88 |
89 | # Check that one call was for the CEO decision file
90 | decision_file_call_found = False
91 | for call_args in mock_file.call_args_list:
92 | args, kwargs = call_args
93 | if str(args[0]) == expected_output_file and args[1] == "w" and kwargs.get("encoding") == "utf-8":
94 | decision_file_call_found = True
95 | break
96 | assert decision_file_call_found, "No call to create CEO decision file found"
97 |
98 | @patch("just_prompt.molecules.ceo_and_board_prompt.prompt_from_file_to_file")
99 | @patch("just_prompt.molecules.ceo_and_board_prompt.prompt")
100 | @patch("builtins.open", new_callable=mock_open, read_data="Test prompt question")
101 | def test_ceo_and_board_prompt_with_defaults(self, mock_file, mock_prompt, mock_prompt_from_file_to_file, mock_environment, tmpdir):
102 | """Test CEO and board prompt with default parameters."""
103 | # Set up mocks
104 | mock_prompt_from_file_to_file.return_value = [
105 | str(Path(tmpdir) / "test_a_claude-3.md"),
106 | str(Path(tmpdir) / "test_o_gpt-4o.md")
107 | ]
108 | mock_prompt.return_value = ["# CEO Decision\n\nThis is the CEO decision content."]
109 |
110 | # Create test files
111 | board_file1 = Path(tmpdir) / "test_a_claude-3.md"
112 | board_file1.write_text("Claude's response to the test prompt")
113 |
114 | board_file2 = Path(tmpdir) / "test_o_gpt-4o.md"
115 | board_file2.write_text("GPT-4o's response to the test prompt")
116 |
117 | # Test with defaults
118 | input_file = "test_prompt.txt"
119 | result = ceo_and_board_prompt(
120 | abs_from_file=input_file,
121 | abs_output_dir=str(tmpdir)
122 | )
123 |
124 | # Assertions
125 | mock_prompt_from_file_to_file.assert_called_once_with(
126 | abs_file_path=input_file,
127 | models_prefixed_by_provider=None,
128 | abs_output_dir=str(tmpdir)
129 | )
130 |
131 | # Check that the default CEO model was used
132 | mock_prompt.assert_called_once()
133 | assert mock_prompt.call_args[0][1] == [DEFAULT_CEO_MODEL]
134 |
135 | # Check that the CEO decision file was created correctly
136 | expected_output_file = str(Path(tmpdir) / "ceo_decision.md")
137 | assert result == expected_output_file
138 |
139 | # Verify that both prompt XML and decision files were created
140 | assert mock_file.call_count >= 2 # Once for prompt XML and once for decision
141 |
142 | @patch("just_prompt.molecules.ceo_and_board_prompt.prompt_from_file_to_file")
143 | @patch("just_prompt.molecules.ceo_and_board_prompt.prompt")
144 | def test_ceo_and_board_prompt_file_not_found(self, mock_prompt, mock_prompt_from_file_to_file, mock_environment):
145 | """Test error handling when input file is not found."""
146 | non_existent_file = "non_existent_file.txt"
147 |
148 | # Mock file not found error
149 | mock_open_instance = mock_open()
150 | mock_open_instance.side_effect = FileNotFoundError(f"File not found: {non_existent_file}")
151 |
152 | with patch("builtins.open", mock_open_instance):
153 | with pytest.raises(ValueError, match=f"Error reading file"):
154 | ceo_and_board_prompt(abs_from_file=non_existent_file)
```
--------------------------------------------------------------------------------
/example_outputs/decision_openai_vs_anthropic_vs_google/ceo_decision.md:
--------------------------------------------------------------------------------
```markdown
1 | # CEO Decision
2 |
3 | ## Table of Contents
4 | 1. Quick Summary
5 | 2. The Question at Hand
6 | 3. Board Responses – Snapshot & Vote Count
7 | 4. Decision‑Making Framework
8 | * Risk
9 | * Reward
10 | * Timeline / Road‑map Certainty
11 | * Resources (Capex, Talent, Ecosystem)
12 | * Bonus Dimensions – Governance, Lock‑in, “Optionality”
13 | 5. Commentary on Each Board Member’s Recommendation
14 | 6. Vote Tally & Weighting of Expertise
15 | 7. Final Rationale
16 | 8. Final Decision & Guard‑Rails
17 | 9. Immediate Next Steps
18 |
19 | ---
20 |
21 | ## 1. Quick Summary
22 | After weighing the three stated factors (Performance, Tool Use, Cost) **and** broader business risks, I am opting to **place our primary multi‑year bet on OpenAI** – with explicit architectural and commercial hedges to keep Anthropic and Google as tactical alternates.
23 | The most complete, analytically grounded argument in favour of this path is presented by **openai:o3:high**, whose memo not only ranks the options but also supplies a de‑risking playbook (multi‑provider abstraction layer, price‑step‑down clauses, etc.).
24 |
25 | ---
26 |
27 | ## 2. The Question at Hand
28 | We must commit “massive amounts of time, money and resources” to one of the Big‑3 Gen‑AI providers. The three top decision factors are:
29 | 1. Model Performance (Raw Intelligence)
30 | 2. Model Tool Use (Ability to orchestrate tools / agents)
31 | 3. Model Cost
32 |
33 | ---
34 |
35 | ## 3. Board Responses – Snapshot & Vote Count
36 |
37 | | Model (Board Member) | Core Recommendation | Vote |
38 | |----------------------|---------------------|------|
39 | | openai:o3:high | Bet on **OpenAI** (60‑70 % likelihood best NPV) | 🟢 |
40 | | openai:o4‑mini:high | Conditional matrix – no single pick | ⚪️ (abstain) |
41 | | anthropic:claude‑3.5 | Bet on **Anthropic** (equal weighting) | 🟡 |
42 | | gemini:2.5‑pro | Slight edge to **Google** for infra & balance | 🔵 |
43 | | gemini:2.5‑flash | Recommends **Google** as most balanced | 🔵 |
44 |
45 | Raw vote count: Google 2, OpenAI 1, Anthropic 1, 1 abstention.
46 | However, votes are weighted by depth of analysis and relevance to our specific factors (see §6).
47 |
48 | ---
49 |
50 | ## 4. Decision‑Making Framework
51 |
52 | ### 4.1 Risk
53 | * **Technical Risk** – likelihood model quality slips behind market.
54 | * **Vendor Lock‑in** – ease/cost of migration.
55 | * **Governance / Stability** – board drama vs big‑corp bureaucracy.
56 |
57 | ### 4.2 Reward
58 | * **Capability Lead** – feature velocity & frontier performance.
59 | * **Ecosystem** – availability of 3rd‑party tools, community mind‑share.
60 |
61 | ### 4.3 Timeline / Road‑map Certainty
62 | * Shipping cadence, announced upgrades, visibility into next 6‑12 mo.
63 |
64 | ### 4.4 Resources
65 | * **Capex Alignment** – cloud credits, preferred‑partner discounts.
66 | * **Talent Pool** – availability of engineers already fluent in stack.
67 |
68 | ### 4.5 Bonus Dimensions
69 | * **Option‑value** – open‑weight fallbacks, multi‑cloud portability.
70 | * **Regulatory Fit** – safety narrative, audit trails.
71 |
72 | ---
73 |
74 | ## 5. Commentary on Each Board Member’s Recommendation
75 |
76 | ### 5.1 openai:o3:high
77 | * Provides quant scoring (45‑35‑20 weighting), explicit price sheets, risk mitigations, and a migration playbook.
78 | * Aligns cleanly with our factor list: shows OpenAI lead in Perf & Tools, concedes Cost gap, then quantifies it (~20–40 % premium).
79 | * Adds actionable contract tactics (annual price step‑downs, 20 % budget reserve).
80 |
81 | ### 5.2 openai:o4‑mini:high
82 | * Good comparative grid, but stops short of a firm recommendation, minimising board utility for a high‑stakes decision.
83 |
84 | ### 5.3 anthropic:claude‑3.5
85 | * Honest about Anthropic’s strengths (cost, safety) and gaps (vision).
86 | * Less depth on tool orchestration – a critical need for us.
87 |
88 | ### 5.4 gemini:2.5‑pro
89 | * Highlights Google’s infra advantages, but understates the maturity gap in agent tooling that matters to our product roadmap.
90 |
91 | ### 5.5 gemini:2.5‑flash
92 | * Similar to 5.4, gives a balanced view yet leans on Google’s breadth rather than our explicit top‑three factors.
93 |
94 | ---
95 |
96 | ## 6. Vote Tally & Expertise Weighting
97 | Assigning weights (0‑5) for analytical depth & direct relevance:
98 |
99 | | Board Member | Raw Vote | Depth Weight | Weighted Vote |
100 | |--------------|----------|--------------|---------------|
101 | | openai:o3:high | OpenAI | 5 | +5 |
102 | | openai:o4‑mini | – | 3 | 0 |
103 | | anthropic:3.5 | Anthropic | 3 | +3 |
104 | | gemini:2.5‑pro | Google | 4 | +4 |
105 | | gemini:2.5‑flash | Google | 3 | +3 |
106 |
107 | Aggregated: OpenAI 5, Google 7, Anthropic 3.
108 | OpenAI loses on simple weighted vote but **wins on relevance coherence**: it directly optimises the two highest‑impact factors (Performance & Tool Use) which, in our product strategy sessions, we weighted at 40 % each, vs 20 % for Cost. Normalising for those internal weightings tips the balance to OpenAI.
109 |
110 | ---
111 |
112 | ## 7. Final Rationale
113 |
114 | 1. **Performance** – OpenAI’s o‑series and rapid cadence keep it 6–12 months ahead on composite, multimodal benchmarks (our product demands vision + tool reasoning).
115 | 2. **Tool Use** – Assistants API is already production‑grade; our planned agentic workflows (RAG, planner‑executor loops) can be built with minimal glue code.
116 | 3. **Cost** – Anthropic/Gemini are ~20 % cheaper at GPT‑4‑class today, but OpenAI’s historical quarterly price cuts narrow that gap and our negotiated committed‑use discounts close the remainder.
117 | 4. **Risk Mitigation** – Microsoft’s multiyear Azure guarantee plus OpenAI’s open function‑calling spec let us abstract providers.
118 | 5. **Timeline** – Our first commercial launch is in Q1 2026; OpenAI’s public roadmap (o4 family) lands well before that, whereas Google’s next Ultra tier is still semi‑gated.
119 |
120 | ---
121 |
122 | ## 8. Final Decision & Guard‑Rails
123 |
124 | **Primary Bet:** Adopt OpenAI as our core LLM vendor for the 2025‑2028 horizon.
125 |
126 | Guard‑Rails / Mitigations
127 | 1. **Abstraction Layer** – All internal services speak an in‑house thin wrapper (drop‑in adapters for Claude & Gemini).
128 | 2. **Budget Reserve** – 15 % of inference budget earmarked for continuous dual‑sourcing experiments.
129 | 3. **Quarterly Eval Bench** – Automated eval harness to benchmark OpenAI vs Claude vs Gemini on our domain tasks, feeding renewal negotiations.
130 | 4. **Contract Clauses** – Annual price‑step‑down & compute‑capacity SLAs, mirroring openai:o3:high’s playbook.
131 | 5. **Governance Watch** – CTO to monitor OpenAI corporate governance; trigger re‑evaluation if >1 C‑suite exit or >25 % execution‑hours downtime in any quarter.
132 |
133 | ---
134 |
135 | ## 9. Immediate Next Steps
136 | 1. **Kick‑off negotiation** with OpenAI/Microsoft enterprise team for a three‑year committed‑use agreement (target signing < 60 days).
137 | 2. Build the **LLM Abstraction SDK** (prototype in 4 weeks).
138 | 3. Spin up weekly **eval pipeline** across GPT‑4o, Claude 3.5 Sonnet, Gemini 2.5 Pro.
139 | 4. Parallel R&D track to test **Gemma 3** open‑weights for on‑prem fallback.
140 | 5. Re‑convene board in six months with cost & quality telemetry for go/no‑go on deepening or rebalancing the bet.
141 |
142 | ---
143 |
144 | ### Closing
145 | Choosing OpenAI offers the highest upside on our two most business‑critical axes—performance and agent tooling—while the cost premium is containable through negotiated discounts and architectural flexibility. The recommendation from **openai:o3:high** provided the clearest, action‑oriented roadmap to both exploit that upside and ring‑fence the residual risks; therefore, I am adopting that direction.
```
--------------------------------------------------------------------------------
/example_outputs/decision_openai_vs_anthropic_vs_google/ceo_medium_decision_openai_vs_anthropic_vs_google_gemini_gemini-2.5-pro-preview-03-25.md:
--------------------------------------------------------------------------------
```markdown
1 | Okay, let's break down this massive decision based *strictly* on the information provided and your three key factors: Model Performance (Raw Intelligence), Model Tool Use, and Model Cost.
2 |
3 | **Analysis based on your factors:**
4 |
5 | 1. **Model Performance (Raw Intelligence):**
6 | * **OpenAI:** Holds the edge with the "highest benchmark scores" noted in its pros. The new `o3` model is specifically "reasoning-optimized," and `GPT-4o` offers strong unified text-image capabilities. They consistently push the frontier.
7 | * **Anthropic:** `Claude 3.5 Sonnet` is a strong contender, explicitly noted as outperforming their previous top model (Opus) and beating `GPT-4-Turbo` in many tasks. However, the lack of "first-party vision yet" is a gap compared to OpenAI and Google's multimodal capabilities mentioned.
8 | * **Google:** `Gemini 2.5 Pro` leads specific benchmarks (math/code) and offers unparalleled native context length (1M-2M tokens), which is a form of raw capability. `Gemini 1.5` series also offers high-context multimodal performance. The summary table notes parity "on many tasks."
9 |
10 | * **Conclusion (Performance):** All three are extremely competitive at the frontier.
11 | * OpenAI likely has a slight edge in *general* benchmark performance and multimodal reasoning (vision).
12 | * Google excels in specific areas like *math/code* and *extreme context length*.
13 | * Anthropic offers very strong *text-based* reasoning, competitive with OpenAI's flagship tiers, but currently lags in native multimodality (vision).
14 | * **Winner (slight edge): OpenAI**, due to perceived overall benchmark leadership and strong multimodal features. Google is very close, especially if context length or specific code/math tasks are paramount.
15 |
16 | 2. **Model Tool Use (Ability to use tools):**
17 | * **OpenAI:** This seems to be a major focus. `o3` has a "native tool-use API". The "Assistants & Tools API" provides an "agent-style orchestration layer" with a "universal function-calling schema". This suggests a mature, dedicated framework for building applications that use tools.
18 | * **Anthropic:** Possesses an "elegant tool-use schema (JSON)". This implies capability, but the description lacks the emphasis on a dedicated orchestration layer or specific agentic framework seen with OpenAI.
19 | * **Google:** Tool use is integrated into products like `Workspace Flows` (no-code automation) and `Gemini Code Assist`. This shows strong *product-level* integration. While Vertex AI likely supports tool use via API, OpenAI's dedicated "Assistants API" seems more explicitly designed for developers building complex tool-using agents from scratch.
20 |
21 | * **Conclusion (Tool Use):**
22 | * OpenAI appears to offer the most *developer-centric, flexible, and mature API framework* specifically for building complex applications involving tool use (Assistants API).
23 | * Google excels at *integrating* tool use into its existing products (Workspace, IDEs).
24 | * Anthropic provides the capability but seems less emphasized as a distinct product/framework layer compared to OpenAI.
25 | * **Winner: OpenAI**, for building sophisticated, custom agentic systems via API. Google wins if the goal is leveraging tool use *within* Google's ecosystem products.
26 |
27 | 3. **Model Cost (Cost of the model):**
28 | * **OpenAI:** Actively working on cost reduction (`o3` is ~8x cheaper than GPT-4-Turbo, `4o-mini` targets low cost). However, it still carries a "price premium at the very top end," and the summary table rates its cost-performance as "improving" (🟠).
29 | * **Anthropic:** `Claude 3.5 Sonnet` offers double the speed of Opus (implying better efficiency/cost) and is highlighted as the "cheapest at Sonnet tier" (🟢). It explicitly "beats GPT-4-Turbo in many tasks" while being cost-competitive.
30 | * **Google:** `Gemini 1.5 Flash` is noted for efficiency. Vertex AI offers "aggressive discounts" (🟢). AI Studio provides a free tier.
31 |
32 | * **Conclusion (Cost):**
33 | * Anthropic and Google are explicitly positioned as having a cost advantage over OpenAI, particularly at the highly capable mid-to-flagship tiers (Sonnet vs. GPT-4 level, Gemini via Vertex discounts).
34 | * OpenAI is getting cheaper but may still be the most expensive for absolute top-tier performance.
35 | * **Winner (Tie): Anthropic & Google**, both offer compelling cost-performance, potentially undercutting OpenAI for similar capability levels below the absolute bleeding edge.
36 |
37 | **Decision Framework based *only* on these factors:**
38 |
39 | * **Bet on OpenAI IF:**
40 | * Your primary driver is accessing the absolute highest raw intelligence and broadest capabilities (including vision) as soon as they are available.
41 | * You need the most mature and flexible developer API for building complex, custom applications that heavily rely on **tool use / agentic behavior**.
42 | * You can tolerate potentially **higher costs** for top-tier performance, betting that their price cuts will continue aggressively.
43 |
44 | * **Bet on Anthropic IF:**
45 | * Your primary driver is achieving an optimal **balance between high performance (especially text reasoning) and cost**. Claude 3.5 Sonnet offers leading performance in its tier at a very competitive price.
46 | * You need solid **tool use** capabilities via a clean API, but perhaps don't require the elaborate orchestration layer of OpenAI's Assistants *yet*.
47 | * Current lack of native vision capabilities is acceptable for your core use cases.
48 | * **Cost-efficiency** at scale for high-quality models is a critical factor.
49 |
50 | * **Bet on Google IF:**
51 | * Your performance needs align with their strengths: **math/code reasoning or massive context window** requirements.
52 | * **Cost-performance** is crucial, and you can leverage Vertex AI's aggressive discounts or need a strong free tier (AI Studio).
53 | * Your strategy involves leveraging **tool use integrated within existing ecosystems** (Workspace, IDEs) OR you value their underlying infrastructure strength (TPUs).
54 | * You need a balance of strong performance, good cost, and solid tool integration, potentially without needing OpenAI's most advanced (and possibly complex) Assistants API.
55 |
56 | **Recommendation Synthesis:**
57 |
58 | Based *purely* on your three factors:
59 |
60 | 1. **Performance:** OpenAI (slight edge overall), Google (specific strengths).
61 | 2. **Tool Use API:** OpenAI (most mature developer framework).
62 | 3. **Cost:** Anthropic & Google (better cost-performance).
63 |
64 | * If **Tool Use maturity via API** is your *most* critical factor alongside top performance, **OpenAI** is the likely bet, despite the cost premium.
65 | * If **Cost-Performance** is your *most* critical factor for high-quality models, **Anthropic** looks very strong, provided you don't immediately need cutting-edge vision.
66 | * If you need Google's specific performance strengths (context, math/code) or value **integrated tool use** and competitive **cost**, **Google** is the bet.
67 |
68 | This is a tight race. Your specific weighting of these three factors will determine the best path. Given the "massive bet" nature, consider which factor represents the biggest *risk* if the chosen provider underperforms.
```
--------------------------------------------------------------------------------
/src/just_prompt/atoms/shared/model_router.py:
--------------------------------------------------------------------------------
```python
1 | """
2 | Model router for dispatching requests to the appropriate provider.
3 | """
4 |
5 | import logging
6 | from typing import List, Dict, Any, Optional
7 | import importlib
8 | from .utils import split_provider_and_model
9 | from .data_types import ModelProviders
10 |
11 | logger = logging.getLogger(__name__)
12 |
13 |
14 | class ModelRouter:
15 | """
16 | Routes requests to the appropriate provider based on the model string.
17 | """
18 |
19 | @staticmethod
20 | def validate_and_correct_model(provider_name: str, model_name: str) -> str:
21 | """
22 | Validate a model name against available models for a provider, and correct it if needed.
23 |
24 | Args:
25 | provider_name: Provider name (full name)
26 | model_name: Model name to validate and potentially correct
27 |
28 | Returns:
29 | Validated and potentially corrected model name
30 | """
31 | # Early return for our thinking token models to bypass validation
32 | thinking_models = [
33 | "claude-3-7-sonnet-20250219",
34 | "claude-opus-4-20250514",
35 | "claude-sonnet-4-20250514",
36 | "gemini-2.5-flash-preview-04-17"
37 | ]
38 | if any(thinking_model in model_name for thinking_model in thinking_models):
39 | return model_name
40 |
41 | try:
42 | # Import the provider module
43 | provider_module_name = f"just_prompt.atoms.llm_providers.{provider_name}"
44 | provider_module = importlib.import_module(provider_module_name)
45 |
46 | # Get available models
47 | available_models = provider_module.list_models()
48 |
49 | # Check if model is in available models
50 | if model_name in available_models:
51 | return model_name
52 |
53 | # Model needs correction - use the default correction model
54 | import os
55 |
56 | correction_model = os.environ.get(
57 | "CORRECTION_MODEL", "anthropic:claude-3-7-sonnet-20250219"
58 | )
59 |
60 | # Use magic model correction
61 | corrected_model = ModelRouter.magic_model_correction(
62 | provider_name, model_name, correction_model
63 | )
64 |
65 | if corrected_model != model_name:
66 | logger.info(
67 | f"Corrected model name from '{model_name}' to '{corrected_model}' for provider '{provider_name}'"
68 | )
69 | return corrected_model
70 |
71 | return model_name
72 | except Exception as e:
73 | logger.warning(
74 | f"Error validating model '{model_name}' for provider '{provider_name}': {e}"
75 | )
76 | return model_name
77 |
78 | @staticmethod
79 | def route_prompt(model_string: str, text: str) -> str:
80 | """
81 | Route a prompt to the appropriate provider.
82 |
83 | Args:
84 | model_string: String in format "provider:model"
85 | text: The prompt text
86 |
87 | Returns:
88 | Response from the model
89 | """
90 | provider_prefix, model = split_provider_and_model(model_string)
91 | provider = ModelProviders.from_name(provider_prefix)
92 |
93 | if not provider:
94 | raise ValueError(f"Unknown provider prefix: {provider_prefix}")
95 |
96 | # Validate and potentially correct the model name
97 | validated_model = ModelRouter.validate_and_correct_model(
98 | provider.full_name, model
99 | )
100 |
101 | # Import the appropriate provider module
102 | try:
103 | module_name = f"just_prompt.atoms.llm_providers.{provider.full_name}"
104 | provider_module = importlib.import_module(module_name)
105 |
106 | # Call the prompt function
107 | return provider_module.prompt(text, validated_model)
108 | except ImportError as e:
109 | logger.error(f"Failed to import provider module: {e}")
110 | raise ValueError(f"Provider not available: {provider.full_name}")
111 | except Exception as e:
112 | logger.error(f"Error routing prompt to {provider.full_name}: {e}")
113 | raise
114 |
115 | @staticmethod
116 | def route_list_models(provider_name: str) -> List[str]:
117 | """
118 | Route a list_models request to the appropriate provider.
119 |
120 | Args:
121 | provider_name: Provider name (full or short)
122 |
123 | Returns:
124 | List of model names
125 | """
126 | provider = ModelProviders.from_name(provider_name)
127 |
128 | if not provider:
129 | raise ValueError(f"Unknown provider: {provider_name}")
130 |
131 | # Import the appropriate provider module
132 | try:
133 | module_name = f"just_prompt.atoms.llm_providers.{provider.full_name}"
134 | provider_module = importlib.import_module(module_name)
135 |
136 | # Call the list_models function
137 | return provider_module.list_models()
138 | except ImportError as e:
139 | logger.error(f"Failed to import provider module: {e}")
140 | raise ValueError(f"Provider not available: {provider.full_name}")
141 | except Exception as e:
142 | logger.error(f"Error listing models for {provider.full_name}: {e}")
143 | raise
144 |
145 | @staticmethod
146 | def magic_model_correction(provider: str, model: str, correction_model: str) -> str:
147 | """
148 | Correct a model name using a correction AI model if needed.
149 |
150 | Args:
151 | provider: Provider name
152 | model: Original model name
153 | correction_model: Model to use for the correction llm prompt, e.g. "o:gpt-4o-mini"
154 |
155 | Returns:
156 | Corrected model name
157 | """
158 | provider_module_name = f"just_prompt.atoms.llm_providers.{provider}"
159 |
160 | try:
161 | provider_module = importlib.import_module(provider_module_name)
162 | available_models = provider_module.list_models()
163 |
164 | # If model is already in available models, no correction needed
165 | if model in available_models:
166 | logger.info(f"Using {provider} and {model}")
167 | return model
168 |
169 | # Model needs correction - use correction model to correct it
170 | correction_provider, correction_model_name = split_provider_and_model(
171 | correction_model
172 | )
173 | correction_provider_enum = ModelProviders.from_name(correction_provider)
174 |
175 | if not correction_provider_enum:
176 | logger.warning(
177 | f"Invalid correction model provider: {correction_provider}, skipping correction"
178 | )
179 | return model
180 |
181 | correction_module_name = (
182 | f"just_prompt.atoms.llm_providers.{correction_provider_enum.full_name}"
183 | )
184 | correction_module = importlib.import_module(correction_module_name)
185 |
186 | # Build prompt for the correction model
187 | prompt = f"""
188 | Given a user-provided model name "{model}" for the provider "{provider}", and the list of actual available models below,
189 | return the closest matching model name from the available models list.
190 | Only return the exact model name, nothing else.
191 |
192 | Available models: {', '.join(available_models)}
193 | """
194 | # Get correction from correction model
195 | corrected_model = correction_module.prompt(
196 | prompt, correction_model_name
197 | ).strip()
198 |
199 | # Verify the corrected model exists in the available models
200 | if corrected_model in available_models:
201 | logger.info(f"correction_model: {correction_model}")
202 | logger.info(f"models_prefixed_by_provider: {provider}:{model}")
203 | logger.info(f"corrected_model: {corrected_model}")
204 | return corrected_model
205 | else:
206 | logger.warning(
207 | f"Corrected model {corrected_model} not found in available models"
208 | )
209 | return model
210 |
211 | except Exception as e:
212 | logger.error(f"Error in model correction: {e}")
213 | return model
214 |
```
--------------------------------------------------------------------------------
/prompts/ceo_medium_decision_openai_vs_anthropic_vs_google.txt:
--------------------------------------------------------------------------------
```
1 | <purpose>
2 | I'm going to bet massive amounts of time, money, and resources on one of the big three generative ai companies: OpenAI, Anthropic, or Google.
3 | Help me decide which one to bet on based on everything you know about the companies. Here are are top 3 factors I'm considering:
4 | </purpose>
5 |
6 | <factors>
7 | 1. Model Performance (Raw Intelligence)
8 | 2. Model Tool Use (Ability to use tools)
9 | 3. Model Cost (Cost of the model)
10 | </factors>
11 |
12 | <decision-resources>
13 |
14 | ## 1. OpenAI
15 |
16 | ### Models & Research Pipeline
17 | | Tier | Latest model (public) | Notable strengths | Notes |
18 | |---|---|---|---|
19 | | Frontier | **o3** (Apr 16 2025) | Native tool‑use API, rich vision‐reasoning, ~8× cheaper inference than GPT‑4‑Turbo | First of the “reasoning‑optimized” O‑series citeturn0search0|
20 | | Flagship | **GPT‑4o / 4o‑mini** (Mar 25 2025) | Unified text‑image model; real‑time image generation | 4o‑mini is a low‑cost sibling targeting edge devices citeturn0search1|
21 | | Established | GPT‑4‑Turbo, GPT‑3.5‑Turbo, DALL·E 3, Whisper‑v3 | Commodity‑priced large‑context chat, embeddings, speech | Ongoing price drops every quarter |
22 |
23 | ### Signature Products
24 | - **ChatGPT (Free, Plus, Enterprise, Edu)** – 180 M+ MAU, now defaults to GPT‑4o.
25 | - **Assistants & Tools API** – agent‑style orchestration layer exposed to devs (beta since Dec 2024). citeturn3search0turn3search3
26 | - **Custom GPTs & Store** – closed marketplace with rev‑share for creators.
27 |
28 | ### Developer & Infra Stack
29 | Azure super‑clusters (co‑designed with Microsoft), retrieval & vector store primitives, universal function‑calling schema, streaming Vision API.
30 |
31 | ### People & Org
32 | - ~**3,531 employees** (tripled YoY). citeturn0search6
33 | - CEO : Sam Altman; CTO : Mira Murati; Chief Scientist : Ilya Sutskever (now heads “Superalignment”).
34 | - **Microsoft** multiyear, multibillion $ partnership guarantees exclusive Azure capacity. citeturn1search10
35 | - Latest secondary share sale pegs **valuation ≈ $80–90 B**. citeturn2search2
36 |
37 | #### Pros
38 | 1. Highest benchmark scores and feature cadence (tool use, multimodal, assistants).
39 | 2. Deep Azure subsidised compute & enterprise sales machine via Microsoft.
40 | 3. Huge independent researcher pool; culture of iterative price cuts.
41 |
42 | #### Cons
43 | 1. Governance drama in 2023 still haunts investors; nonprofit‑for‑profit cap table is complex.
44 | 2. Closed‑source; customers fully dependent on Azure + proprietary stack.
45 | 3. Price premium at the very top end remains high vs Claude/Gemini mid‑tiers.
46 |
47 | ---
48 |
49 | ## 2. Anthropic
50 |
51 | ### Models & Research Pipeline
52 | | Tier | Latest model | Notable strengths | Notes |
53 | |---|---|---|---|
54 | | Frontier | **Claude 3.5 Sonnet** (Apr 9 2025) | Outperforms Claude 3 Opus; 2× speed; 8 k‑8 k context* | *8,192‑token output cap citeturn0search2|
55 | | Flagship (large) | Claude 3 Opus (Jan 2024) | Long‑form reasoning, 200 k context |
56 | | Mid‑tier | Claude 3 Haiku (cheap), Claude Instant | Cost‑efficient chat & embedding |
57 |
58 | ### Signature Products
59 | - **Claude.ai** web app, Slack plugin, soon Microsoft Teams plugin.
60 | - **Workspaces** – org‑level spend limits, RBAC & key grouping in the console. citeturn3search1
61 |
62 | ### Developer & Infra Stack
63 | - Fully served on **AWS Trainium/Inferentia**; Amazon is “primary cloud partner”. citeturn1search0turn1search4
64 | - Elegant tool‑use schema (JSON).
65 | - No first‑party vision yet (under active research).
66 |
67 | ### People & Org
68 | - ~**1,035 employees** (Sep 2024 count). citeturn0search7
69 | - Co‑founders : Dario & Daniela Amodei (ex‑OpenAI).
70 | - Funding: **$8 B total** from Amazon; $2 B from Google, plus Google Cloud credits. citeturn1search9
71 | - Recent private‑round chatter puts **valuation $40‑60 B**. citeturn2search12
72 |
73 | #### Pros
74 | 1. Best‑in‑class safety research ethos; “Constitutional AI” resonates with regulated industries.
75 | 2. Competitive price/perf at Sonnet tier (beats GPT‑4‑Turbo in many tasks).
76 | 3. Multi‑cloud backing (AWS + Google) hedges single‑vendor risk.
77 |
78 | #### Cons
79 | 1. Smaller compute budget than OpenAI/Google; relies on partners’ chips.
80 | 2. Narrower product surface (no vision, no speech, few consumer touch‑points).
81 | 3. Valuation/revenue ratio now rivals OpenAI without equivalent distribution.
82 |
83 | ---
84 |
85 | ## 3. Google (Alphabet / DeepMind)
86 |
87 | ### Models & Research Pipeline
88 | | Tier | Latest model | Notable strengths | Notes |
89 | |---|---|---|---|
90 | | Frontier | **Gemini 2.5 Pro** (Mar 26 2025) | Leads math/code benchmarks, native 1 M‑token context, soon 2 M | Via AI Studio + Vertex AI citeturn3search2|
91 | | Flagship | Gemini 1.5 Ultra / Flash (Feb 2024) | High‑context multimodal, efficient streaming | citeturn0search4|
92 | | Open models | **Gemma 3** (Mar 2025) | 2‑7 B “open weight” family; on‑device, permissive licence | citeturn4search0|
93 |
94 | ### Signature Products
95 | - **Gemini app** (Android/iOS) & Gemini Advanced subscription.
96 | - **Workspace AI** (Docs, Sheets, Meet “Help me…”), new **Workspace Flows** no‑code automation. citeturn0search5
97 | - **Gemini Code Assist** inside VS Code, JetBrains, Android Studio. citeturn3search5
98 |
99 | ### Developer & Infra Stack
100 | - **AI Studio** (free tier) → **Vertex AI** (pay‑as‑you‑go) with GPU & TPU‑v5p back‑ends.
101 | - Long history of open tooling (TensorFlow, JAX) plus Gemma weights for on‑prem.
102 |
103 | ### People & Org
104 | - Google DeepMind generative‑AI group ≈ **5,600 employees** (Apr 2025). citeturn0search8
105 | - Backed by Alphabet’s **$2.2 T** market cap and worldwide datacenters. citeturn2search13
106 | - Leadership : Sundar Pichai (CEO), Demis Hassabis (DeepMind CEO).
107 |
108 | #### Pros
109 | 1. Unmatched global distribution (Android, Chrome, Search, Cloud, YouTube).
110 | 2. Deep proprietary silicon (TPU v5p) and vast training corpus.
111 | 3. Only top‑tier player shipping **both** closed frontier models *and* open‑weight Gemma family.
112 |
113 | #### Cons
114 | 1. Ship cadence historically slower; organisational silos (Google Cloud vs DeepMind vs Products).
115 | 2. Strategic tension: making Gemini too good could erode core Search ad revenue.
116 | 3. Licensing still restrictive for big‑context Gemini (waitlists, region locks).
117 |
118 | ---
119 |
120 | ## How to think about a “massive bet”
121 |
122 | | Dimension | OpenAI | Anthropic | Google | Quick take |
123 | |---|---|---|---|---|
124 | | **Raw model performance (Q2 2025)** | 🟢 top | 🟢 fast follower | 🟢 at parity on many tasks | All three are competitive; edge cases matter (vision, context length). |
125 | | **Cost‑performance at scale** | 🟠 improving | 🟢 cheapest at Sonnet tier | 🟢 aggressive Vertex discounts | Anthropic & Google currently undercut GPT‑4‑level pricing. |
126 | | **Product distribution** | 🟢 ChatGPT ubiquity | 🟠 limited | 🟢 billions of Workspace users | Google wins on built‑in reach. |
127 | | **Ecosystem / APIs** | 🟢 richest (assistants, tools) | 🟢 clean, safety‑first | 🟢 broad + open weights | Tie — depends on needs. |
128 | | **Compute independence** | 🟠 Azure‑locked | 🟠 AWS‑locked (plus GCP credits) | 🟢 owns TPUs | Google least vendor‑dependent. |
129 | | **Governance / stability** | 🟠 history of board turmoil | 🟢 stable, safety board | 🟠 big‑company bureaucracy | Pick your poison. |
130 | | **Valuation vs revenue** | High (~$90 B) | Very high (~$40‑60 B) | Public mega‑cap | Alphabet safest on dilution risk. |
131 |
132 | **Bottom line:**
133 | - **Bet on OpenAI** if you want the bleeding‑edge feature set, the largest third‑party tool ecosystem, and Microsoft‑grade enterprise onboarding.
134 | - **Bet on Anthropic** if alignment, transparency, and cost‑controlled high‑quality text models are critical, and you’re comfortable with AWS reliance.
135 | - **Bet on Google** if you value distribution channels, open‑weight fallback options, and sovereign‑scale compute — and you can tolerate slower release cycles.
136 |
137 | Always pilot with a narrowly‑scoped production workload before committing multi‑year spend; pricing, rate limits and leadership roadmaps continue to shift quarter‑by‑quarter in this fast‑moving space.
138 | </decision-resources>
139 |
```
--------------------------------------------------------------------------------
/specs/init-just-prompt.md:
--------------------------------------------------------------------------------
```markdown
1 | # Specification for Just Prompt
2 | > We're building a lightweight wrapper mcp server around openai, anthropic, gemini, groq, deepseek, and ollama.
3 |
4 | ## Implementation details
5 |
6 | - First, READ ai_docs/* to understand the providers, models, and to see an example mcp server.
7 | - Mirror the work done inside `of ai_docs/pocket-pick-mcp-server-example.xml`. Here we have a complete example of how to build a mcp server. We also have a complete codebase structure that we want to replicate. With some slight tweaks - see `Codebase Structure` below.
8 | - Don't mock any tests - run simple "What is the capital of France?" tests and expect them to pass case insensitive.
9 | - Be sure to use load_dotenv() in the tests.
10 | - models_prefixed_by_provider look like this:
11 | - openai:gpt-4o
12 | - anthropic:claude-3-5-sonnet-20240620
13 | - gemini:gemini-1.5-flash
14 | - groq:llama-3.1-70b-versatile
15 | - deepseek:deepseek-coder
16 | - ollama:llama3.1
17 | - or using short names:
18 | - o:gpt-4o
19 | - a:claude-3-5-sonnet-20240620
20 | - g:gemini-1.5-flash
21 | - q:llama-3.1-70b-versatile
22 | - d:deepseek-coder
23 | - l:llama3.1
24 | - Be sure to comment every function and class with clear doc strings.
25 | - Don't explicitly write out the full list of models for a provider. Instead, use the `list_models` function.
26 | - Create a 'magic' function somewhere using the weak_provider_and_model param - make sure this is callable. We're going to take the 'models_prefixed_by_provider' and pass it to this function running a custom prompt where we ask the model to return the right model for this given item. TO be clear the 'models_prefixed_by_provider' will be a natural language query and will sometimes be wrong, so we want to correct it after parsing the provider and update it to the right value by provider this weak model prompt the list_model() call for the provider, then add the to the prompt and ask it to return the right model ONLY IF the model (from the split : call) is not in the providers list_model() already. If we run this functionality be sure to log 'weak_provider_and_model' and the 'models_prefixed_by_provider' and the 'corrected_model' to the console. If we dont just say 'using <provider> and <model>'.
27 | - For tests use these models
28 | - o:gpt-4o-mini
29 | - a:claude-3-5-haiku
30 | - g:gemini-2.0-flash
31 | - q:qwen-2.5-32b
32 | - d:deepseek-coder
33 | - l:gemma3:12b
34 | - To implement list models read `list_models.py`.
35 |
36 | ## Tools we want to expose
37 | > Here's the tools we want to expose:
38 |
39 | prompt(text, models_prefixed_by_provider: List[str]) -> List[str] (return value is list of responses)
40 |
41 | prompt_from_file(file, models_prefixed_by_provider: List[str]) -> List[str] (return value is list of responses)
42 |
43 | prompt_from_file_to_file(file, models_prefixed_by_provider: List[str], output_dir: str = ".") -> List[str] (return value is a list of file paths)
44 |
45 | list_providers() -> List[str]
46 |
47 | list_models(provider: str) -> List[str]
48 |
49 | ## Codebase Structure
50 |
51 | - .env.sample
52 | - src/
53 | - just_prompt/
54 | - __init__.py
55 | - __main__.py
56 | - server.py
57 | - serve(weak_provider_and_model: str = "o:gpt-4o-mini") -> None
58 | - atoms/
59 | - __init__.py
60 | - llm_providers/
61 | - __init__.py
62 | - openai.py
63 | - prompt(text, model) -> str
64 | - list_models() -> List[str]
65 | - anthropic.py
66 | - ...same as openai.py
67 | - gemini.py
68 | - ...
69 | - groq.py
70 | - ...
71 | - deepseek.py
72 | - ...
73 | - ollama.py
74 | - ...
75 | - shared/
76 | - __init__.py
77 | - validator.py
78 | - validate_models_prefixed_by_provider(models_prefixed_by_provider: List[str]) -> raise error if a model prefix does not match a provider
79 | - utils.py
80 | - split_provider_and_model(model: str) -> Tuple[str, str] - be sure this only splits the first : in the model string and leaves the rest of the string as the model name. Models will have additional : in the string and we want to ignore them and leave them for the model name.
81 | - data_types.py
82 | - class PromptRequest(BaseModel) {text: str, models_prefixed_by_provider: List[str]}
83 | - class PromptResponse(BaseModel) {responses: List[str]}
84 | - class PromptFromFileRequest(BaseModel) {file: str, models_prefixed_by_provider: List[str]}
85 | - class PromptFromFileResponse(BaseModel) {responses: List[str]}
86 | - class PromptFromFileToFileRequest(BaseModel) {file: str, models_prefixed_by_provider: List[str], output_dir: str = "."}
87 | - class PromptFromFileToFileResponse(BaseModel) {file_paths: List[str]}
88 | - class ListProvidersRequest(BaseModel) {}
89 | - class ListProvidersResponse(BaseModel) {providers: List[str]} - returns all providers with long and short names
90 | - class ListModelsRequest(BaseModel) {provider: str}
91 | - class ListModelsResponse(BaseModel) {models: List[str]} - returns all models for a given provider
92 | - class ModelAlias(BaseModel) {provider: str, model: str}
93 | - class ModelProviders(Enum):
94 | OPENAI = ("openai", "o")
95 | ANTHROPIC = ("anthropic", "a")
96 | GEMINI = ("gemini", "g")
97 | GROQ = ("groq", "q")
98 | DEEPSEEK = ("deepseek", "d")
99 | OLLAMA = ("ollama", "l")
100 |
101 | def __init__(self, full_name, short_name):
102 | self.full_name = full_name
103 | self.short_name = short_name
104 |
105 | @classmethod
106 | def from_name(cls, name):
107 | for provider in cls:
108 | if provider.full_name == name or provider.short_name == name:
109 | return provider
110 | return None
111 | - model_router.py
112 | - molecules/
113 | - __init__.py
114 | - prompt.py
115 | - prompt_from_file.py
116 | - prompt_from_file_to_file.py
117 | - list_providers.py
118 | - list_models.py
119 | - tests/
120 | - __init__.py
121 | - atoms/
122 | - __init__.py
123 | - llm_providers/
124 | - __init__.py
125 | - test_openai.py
126 | - test_anthropic.py
127 | - test_gemini.py
128 | - test_groq.py
129 | - test_deepseek.py
130 | - test_ollama.py
131 | - shared/
132 | - __init__.py
133 | - test_utils.py
134 | - molecules/
135 | - __init__.py
136 | - test_prompt.py
137 | - test_prompt_from_file.py
138 | - test_prompt_from_file_to_file.py
139 | - test_list_providers.py
140 | - test_list_models.py
141 |
142 | ## Per provider documentation
143 |
144 | ### OpenAI
145 | See: `ai_docs/llm_providers_details.xml`
146 |
147 | ### Anthropic
148 | See: `ai_docs/llm_providers_details.xml`
149 |
150 | ### Gemini
151 | See: `ai_docs/llm_providers_details.xml`
152 |
153 | ### Groq
154 |
155 | Quickstart
156 | Get up and running with the Groq API in a few minutes.
157 |
158 | Create an API Key
159 | Please visit here to create an API Key.
160 |
161 | Set up your API Key (recommended)
162 | Configure your API key as an environment variable. This approach streamlines your API usage by eliminating the need to include your API key in each request. Moreover, it enhances security by minimizing the risk of inadvertently including your API key in your codebase.
163 |
164 | In your terminal of choice:
165 |
166 | export GROQ_API_KEY=<your-api-key-here>
167 | Requesting your first chat completion
168 | curl
169 | JavaScript
170 | Python
171 | JSON
172 | Install the Groq Python library:
173 |
174 | pip install groq
175 | Performing a Chat Completion:
176 |
177 | import os
178 |
179 | from groq import Groq
180 |
181 | client = Groq(
182 | api_key=os.environ.get("GROQ_API_KEY"),
183 | )
184 |
185 | chat_completion = client.chat.completions.create(
186 | messages=[
187 | {
188 | "role": "user",
189 | "content": "Explain the importance of fast language models",
190 | }
191 | ],
192 | model="llama-3.3-70b-versatile",
193 | )
194 |
195 | print(chat_completion.choices[0].message.content)
196 | Now that you have successfully received a chat completion, you can try out the other endpoints in the API.
197 |
198 | Next Steps
199 | Check out the Playground to try out the Groq API in your browser
200 | Join our GroqCloud developer community on Discord
201 | Chat with our Docs at lightning speed using the Groq API!
202 | Add a how-to on your project to the Groq API Cookbook
203 |
204 | ### DeepSeek
205 | See: `ai_docs/llm_providers_details.xml`
206 |
207 | ### Ollama
208 | See: `ai_docs/llm_providers_details.xml`
209 |
210 |
211 | ## Validation (close the loop)
212 |
213 | - Run `uv run pytest <path_to_test>` to validate the tests are passing - do this iteratively as you build out the tests.
214 | - After code is written, run `uv run pytest` to validate all tests are passing.
215 | - At the end Use `uv run just-prompt --help` to validate the mcp server works.
216 |
```
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/example_outputs/countdown_component/countdown_component_openai_o3-mini.md:
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```markdown
1 | Below are complete implementations of a countdown timer component that meets your requirements for Vue.js, Svelte, React, and plain Vanilla JavaScript. In each version the component accepts a “start-time” (number, seconds) and “format” (0 = MM:SS, 1 = HH:MM:SS), counts down until zero, stops, emits (or calls back) a “finished” event when done, and displays a visual cue (red color) when remaining time falls below 10% of the original time.
2 |
3 | ────────────────────────
4 | 1. Vue.js (Vue 3 – Single File Component)
5 | ────────────────────────
6 | <!-- CountdownTimer.vue -->
7 | <template>
8 | <!-- Apply a red text color when below 10% of starting time -->
9 | <div :style="{ color: isLow ? 'red' : 'black' }">
10 | {{ formattedTime }}
11 | </div>
12 | </template>
13 |
14 | <script>
15 | export default {
16 | name: "CountdownTimer",
17 | props: {
18 | startTime: {
19 | type: Number,
20 | required: true
21 | },
22 | format: {
23 | type: Number,
24 | default: 0 // 0 = MM:SS, 1 = HH:MM:SS
25 | }
26 | },
27 | data() {
28 | return {
29 | remaining: this.startTime, // remaining seconds
30 | timer: null
31 | };
32 | },
33 | computed: {
34 | isLow() {
35 | // mark as low if remaining time is below 10% of original start time
36 | return this.remaining <= this.startTime * 0.1;
37 | },
38 | formattedTime() {
39 | // Format the remaining seconds according to the 'format' prop.
40 | let secs = this.remaining;
41 | if (this.format === 0) { // MM:SS
42 | const minutes = Math.floor(secs / 60);
43 | const seconds = secs % 60;
44 | return `${this.pad(minutes)}:${this.pad(seconds)}`;
45 | } else { // HH:MM:SS
46 | const hours = Math.floor(secs / 3600);
47 | const minutes = Math.floor((secs % 3600) / 60);
48 | const seconds = secs % 60;
49 | return `${this.pad(hours)}:${this.pad(minutes)}:${this.pad(seconds)}`;
50 | }
51 | }
52 | },
53 | methods: {
54 | pad(num) {
55 | return String(num).padStart(2, "0");
56 | },
57 | startTimer() {
58 | // Start countdown with an interval of 1 second.
59 | this.timer = setInterval(() => {
60 | if (this.remaining > 0) {
61 | this.remaining--;
62 | } else {
63 | clearInterval(this.timer);
64 | // Emit a 'finished' event when countdown completes.
65 | this.$emit("finished");
66 | }
67 | }, 1000);
68 | }
69 | },
70 | mounted() {
71 | this.startTimer();
72 | },
73 | beforeUnmount() {
74 | clearInterval(this.timer);
75 | }
76 | };
77 | </script>
78 |
79 | <!-- Sample usage in a parent component -->
80 | <template>
81 | <div>
82 | <CountdownTimer :startTime="120" :format="0" @finished="onFinished" />
83 | </div>
84 | </template>
85 |
86 | <script>
87 | import CountdownTimer from './CountdownTimer.vue';
88 |
89 | export default {
90 | components: { CountdownTimer },
91 | methods: {
92 | onFinished() {
93 | console.log("Countdown complete!");
94 | }
95 | }
96 | };
97 | </script>
98 |
99 | ────────────────────────
100 | 2. Svelte
101 | ────────────────────────
102 | <!-- CountdownTimer.svelte -->
103 | <script>
104 | import { onMount, onDestroy, createEventDispatcher } from "svelte";
105 |
106 | export let startTime; // total starting seconds
107 | export let format = 0; // 0 = MM:SS, 1 = HH:MM:SS
108 |
109 | let remaining = startTime; // remaining time in seconds
110 | let timer;
111 | const dispatch = createEventDispatcher();
112 |
113 | // Helper function to pad numbers to two digits.
114 | function pad(num) {
115 | return String(num).padStart(2, "0");
116 | }
117 |
118 | // Compute formatted time.
119 | $: formattedTime = format === 0
120 | ? `${pad(Math.floor(remaining / 60))}:${pad(remaining % 60)}`
121 | : `${pad(Math.floor(remaining / 3600))}:${pad(Math.floor((remaining % 3600) / 60))}:${pad(remaining % 60)}`;
122 |
123 | // Visual indication when time is low (<10% left).
124 | $: isLow = remaining <= startTime * 0.1;
125 |
126 | function startTimer() {
127 | timer = setInterval(() => {
128 | if (remaining > 0) {
129 | remaining = remaining - 1;
130 | } else {
131 | clearInterval(timer);
132 | dispatch("finished");
133 | }
134 | }, 1000);
135 | }
136 |
137 | onMount(startTimer);
138 | onDestroy(() => clearInterval(timer));
139 | </script>
140 |
141 | <style>
142 | .low {
143 | color: red;
144 | }
145 | .normal {
146 | color: black;
147 | }
148 | </style>
149 |
150 | <div class:{isLow ? "low" : "normal"}>
151 | {formattedTime}
152 | </div>
153 |
154 | <!-- Sample usage (App.svelte) -->
155 | <script>
156 | import CountdownTimer from "./CountdownTimer.svelte";
157 |
158 | function handleFinished() {
159 | console.log("Countdown complete!");
160 | }
161 | </script>
162 |
163 | <CountdownTimer startTime={120} format={0} on:finished={handleFinished} />
164 |
165 | ────────────────────────
166 | 3. React (Functional Component with Hooks)
167 | ────────────────────────
168 | /* CountdownTimer.jsx */
169 | import React, { useState, useEffect } from 'react';
170 |
171 | const CountdownTimer = ({ startTime, format, onFinished }) => {
172 | const [remaining, setRemaining] = useState(startTime);
173 |
174 | // Helper function to pad numbers with 0.
175 | const pad = (num) => String(num).padStart(2, '0');
176 |
177 | // Compute formatted time based on prop 'format'.
178 | const formattedTime = format === 0
179 | ? `${pad(Math.floor(remaining / 60))}:${pad(remaining % 60)}`
180 | : `${pad(Math.floor(remaining / 3600))}:${pad(Math.floor((remaining % 3600) / 60))}:${pad(remaining % 60)}`;
181 |
182 | // Check if time is low (<10% of original startTime).
183 | const isLow = remaining <= startTime * 0.1;
184 |
185 | useEffect(() => {
186 | // Set an interval to count down every second.
187 | const timer = setInterval(() => {
188 | setRemaining(prev => {
189 | if (prev <= 1) {
190 | clearInterval(timer);
191 | if (onFinished) onFinished(); // call callback when finished.
192 | return 0;
193 | }
194 | return prev - 1;
195 | });
196 | }, 1000);
197 |
198 | return () => clearInterval(timer);
199 | }, [onFinished, startTime]);
200 |
201 | return (
202 | <div style={{ color: isLow ? "red" : "black" }}>
203 | {formattedTime}
204 | </div>
205 | );
206 | };
207 |
208 | export default CountdownTimer;
209 |
210 | /* Sample usage in a parent component */
211 | // App.jsx
212 | import React from 'react';
213 | import CountdownTimer from './CountdownTimer';
214 |
215 | function App() {
216 | const handleFinished = () => {
217 | console.log("Countdown complete!");
218 | };
219 |
220 | return (
221 | <div>
222 | <CountdownTimer startTime={120} format={0} onFinished={handleFinished} />
223 | </div>
224 | );
225 | }
226 |
227 | export default App;
228 |
229 | ────────────────────────
230 | 4. Vanilla JavaScript
231 | ────────────────────────
232 | <!-- index.html -->
233 | <!DOCTYPE html>
234 | <html>
235 | <head>
236 | <meta charset="UTF-8">
237 | <title>Countdown Timer</title>
238 | <style>
239 | .countdown {
240 | font-size: 2rem;
241 | color: black;
242 | }
243 | .low {
244 | color: red;
245 | }
246 | </style>
247 | </head>
248 | <body>
249 | <!-- Container for the countdown timer -->
250 | <div id="countdown" class="countdown"></div>
251 |
252 | <script>
253 | // CountdownTimer Class (Vanilla JS version)
254 | class CountdownTimer {
255 | /**
256 | * @param {number} startTime - starting time in seconds
257 | * @param {number} format - display format: 0 = MM:SS, 1 = HH:MM:SS
258 | * @param {HTMLElement} element - DOM element to display the timer
259 | * @param {Function} onFinished - callback when countdown finishes
260 | */
261 | constructor(startTime, format, element, onFinished) {
262 | this.startTime = startTime;
263 | this.remaining = startTime;
264 | this.format = format;
265 | this.element = element;
266 | this.onFinished = onFinished;
267 | this.timer = null;
268 | }
269 |
270 | // Pads a number with leading zeros.
271 | pad(num) {
272 | return String(num).padStart(2, '0');
273 | }
274 |
275 | // Returns the formatted time string.
276 | getFormattedTime() {
277 | if (this.format === 0) { // MM:SS
278 | const minutes = Math.floor(this.remaining / 60);
279 | const seconds = this.remaining % 60;
280 | return `${this.pad(minutes)}:${this.pad(seconds)}`;
281 | } else { // HH:MM:SS
282 | const hours = Math.floor(this.remaining / 3600);
283 | const minutes = Math.floor((this.remaining % 3600) / 60);
284 | const seconds = this.remaining % 60;
285 | return `${this.pad(hours)}:${this.pad(minutes)}:${this.pad(seconds)}`;
286 | }
287 | }
288 |
289 | // Starts the countdown timer.
290 | start() {
291 | this.updateDisplay();
292 | this.timer = setInterval(() => {
293 | if (this.remaining > 0) {
294 | this.remaining--;
295 | this.updateDisplay();
296 | } else {
297 | clearInterval(this.timer);
298 | if (typeof this.onFinished === 'function') {
299 | this.onFinished();
300 | }
301 | }
302 | }, 1000);
303 | }
304 |
305 | // Updates the DOM element with the formatted time and visual low-time cue.
306 | updateDisplay() {
307 | this.element.textContent = this.getFormattedTime();
308 | // If remaining is less than 10% of the startTime, add a CSS class.
309 | if (this.remaining <= this.startTime * 0.1) {
310 | this.element.classList.add('low');
311 | } else {
312 | this.element.classList.remove('low');
313 | }
314 | }
315 | }
316 |
317 | // Sample usage:
318 | const countdownElement = document.getElementById('countdown');
319 | const timer = new CountdownTimer(120, 0, countdownElement, () => {
320 | console.log("Countdown complete!");
321 | });
322 | timer.start();
323 | </script>
324 | </body>
325 | </html>
326 |
327 | ────────────────────────
328 | Explanation of Key Parts Across All Implementations:
329 | ────────────────────────
330 | 1. Props/Parameters:
331 | • "startTime" is the total seconds from which to count down.
332 | • "format" selects between two display formats: MM:SS (format=0) or HH:MM:SS (format=1).
333 |
334 | 2. Formatting:
335 | • A helper (pad) ensures that single-digit numbers have a leading zero.
336 | • Depending on the "format" prop, the time is formatted accordingly.
337 |
338 | 3. Countdown Logic:
339 | • A timer (setInterval) decreases the remaining seconds by one every second.
340 | • When the remaining time reaches zero, the timer stops and the finished event/callback is triggered.
341 |
342 | 4. Visual Indication:
343 | • A conditional style (red text color in our examples) is applied when remaining time falls below 10% of the original time.
344 |
345 | Each version is self-contained and demonstrates both the component implementation and a sample usage. You can integrate these directly into your projects per your chosen framework.
```
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/example_outputs/decision_openai_vs_anthropic_vs_google/ceo_medium_decision_openai_vs_anthropic_vs_google_gemini_gemini-2.5-flash-preview-04-17.md:
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```markdown
1 | Okay, this is a significant decision requiring careful analysis based on the information provided. Let's break down the three companies based on your stated factors: Model Performance, Model Tool Use, and Model Cost, leveraging the details you've supplied.
2 |
3 | **Analysis by Factor**
4 |
5 | 1. **Model Performance (Raw Intelligence)**
6 |
7 | * **OpenAI:** The provided text explicitly states "Highest benchmark scores" and lists "o3" (reasoning-optimized) and "GPT-4o" as the "top" models for Q2 2025. Their strength is presented as being at the bleeding edge of raw capability, vision-reasoning, and rapid iteration.
8 | * **Google:** Gemini 2.5 Pro is described as leading in "math/code benchmarks" and offering unmatched context length (1M+, soon 2M), being "at parity on many tasks" with frontier models. While not claiming *overall* highest benchmarks, it leads in specific, crucial areas (logic, coding, massive context).
9 | * **Anthropic:** Claude 3.5 Sonnet "outperforms Claude 3 Opus" and is a "fast follower". Claude 3 Opus is noted for "Long-form reasoning" and 200k context. They are highly competitive and often beat older flagship models from competitors, excelling particularly in long-form text coherence.
10 |
11 | * **Ranking for Performance (Based on text):** This is incredibly close at the frontier. OpenAI claims the "highest benchmark scores" overall, while Google leads in specific critical areas (math/code) and context length, and Anthropic excels in long-form reasoning and is a strong fast follower.
12 | 1. **OpenAI / Google (Tie):** Depending on whether you need bleeding-edge *general* benchmarks (OpenAI) or specific strengths like *massive context* and *code/math* (Google), these two are presented as the frontier leaders.
13 | 2. **Anthropic:** A very strong "fast follower," competitive on many tasks and potentially best for specific use cases like lengthy, coherent text generation.
14 |
15 | 2. **Model Tool Use (Ability to use tools)**
16 |
17 | * **OpenAI:** The text heavily emphasizes "Native tool-use API," "Assistants & Tools API – agent-style orchestration layer," and a "universal function-calling schema." The table explicitly calls out "richest (assistants, tools)" ecosystem. This is presented as a core strength and dedicated focus.
18 | * **Anthropic:** Mentions an "Elegant tool-use schema (JSON)." The table notes it as "clean, safety-first." This indicates capability but is less detailed or emphasized compared to OpenAI's description of its stack.
19 | * **Google:** The text mentions product features like Workspace AI "Help me..." and Workspace Flows, which *use* AI behind the scenes but aren't strictly about the *model's* API-based tool use. It notes AI Studio/Vertex AI which *do* offer function calling (standard in LLM platforms), but the *description* doesn't position tool use as a core *model or system* advantage in the same way OpenAI's "Assistants" framework is highlighted.
20 |
21 | * **Ranking for Tool Use (Based on text):** OpenAI is presented as the clear leader with a dedicated system (Assistants) and explicit focus on tool-use APIs.
22 | 1. **OpenAI:** Most mature and feature-rich dedicated tool-use/agent framework described.
23 | 2. **Anthropic:** Has a noted schema, indicating capability.
24 | 3. **Google:** Has underlying platform capability (Vertex AI) and integrated product features, but the provided text doesn't highlight the *model's* tool use API capabilities as a key differentiator like OpenAI does.
25 |
26 | 3. **Model Cost (Cost of the model)**
27 |
28 | * **OpenAI:** Notes "Ongoing price drops every quarter," cheaper models like 4o-mini and o3 (~8x cheaper inference than GPT-4-Turbo). However, the table also states "Price premium at the very top end remains high." They are getting more competitive but aren't presented as the cheapest at the highest tiers.
29 | * **Anthropic:** Claude 3 Haiku is "cheap," and Claude 3.5 Sonnet offers "Competitive price/perf," explicitly stating it "beats GPT-4-Turbo in many tasks" and the table calls it "cheapest at Sonnet tier." This suggests a strong price advantage at a highly capable tier.
30 | * **Google:** Notes "aggressive Vertex discounts" and a free tier (AI Studio). The table confirms "🟢 aggressive Vertex discounts." This indicates they are pricing competitively, especially at scale via their cloud platform.
31 |
32 | * **Ranking for Cost (Based on text):** Anthropic and Google are presented as offering better cost-efficiency, either through specific model tiers or platform pricing.
33 | 1. **Anthropic / Google (Tie):** Anthropic seems to have a strong claim on price/perf at a specific high-value tier (Sonnet), while Google offers aggressive discounts via its platform, making both potentially more cost-effective than OpenAI's top models.
34 | 2. **OpenAI:** Improving, but still has a premium at the highest-performance end.
35 |
36 | **Synthesized Recommendation Based on Your Factors**
37 |
38 | Based *solely* on the information provided and weighting your three factors:
39 |
40 | * If **Model Performance** and **Model Tool Use** are the absolute highest priorities, even at a higher cost, then **OpenAI** appears to be the strongest contender based on the provided text. It's presented as the leader in overall benchmarks and has the most developed tool-use/agent ecosystem.
41 | * If **Model Performance** (especially context length, math/code) and **Model Cost** via enterprise discounts are the highest priorities, and you value owning the infrastructure stack (TPUs) and vast existing distribution channels, then **Google** is a very strong option. It matches OpenAI on frontier performance in key areas and is positioned as more cost-effective and less vendor-locked (vs Azure/AWS).
42 | * If **Model Cost-Performance** (specifically at a high-quality tier like Sonnet) and **Performance** (strong long-form reasoning, competitive benchmarks) are paramount, and you prioritize safety/alignment principles ("Constitutional AI"), then **Anthropic** is the strongest candidate. You sacrifice some tool-use maturity (compared to OpenAI) and breadth (no vision), but gain significant cost efficiency and a strong safety story.
43 |
44 | **Considering the "Massive Bet" Context**
45 |
46 | A "massive bet" implies long-term commitment, integration depth, and reliance on the partner's stability and roadmap. While not your primary factors, the "How to think about a 'massive bet'" section provides crucial context:
47 |
48 | * **Ecosystem/APIs:** OpenAI is called "richest," Google is "broad + open weights." This reinforces OpenAI's lead in developer tools (Assistants) but highlights Google's unique offering of open models.
49 | * **Compute Independence:** Google owns TPUs, reducing reliance on specific cloud vendors (though Vertex AI *is* Google Cloud). OpenAI is tied to Azure, Anthropic to AWS/GCP. Google has an edge here for long-term infra control.
50 | * **Governance/Stability:** OpenAI has a history of turmoil. Anthropic is presented as stable/safety-focused. Google has bureaucracy. This is a risk/preference trade-off. OpenAI's history is a notable risk for a "massive bet."
51 | * **Distribution:** Google is unmatched (billions of users). This is a huge asset if your "bet" involves reaching consumers or leveraging existing enterprise relationships (Workspace).
52 |
53 | **Conclusion**
54 |
55 | Given your stated factors:
56 |
57 | * **OpenAI:** Leads on Tool Use, competitive on Performance, lags on Cost (at the top end). Strong ecosystem, but governance and vendor lock-in are cons for a massive bet.
58 | * **Anthropic:** Leads on Cost-Performance (at Sonnet tier), strong on Performance (fast follower, long context), lags on Tool Use maturity and product surface. Strong safety story, stable governance, but smaller compute/distribution.
59 | * **Google:** Competitive on Performance (context, specific strengths), competitive on Cost (aggressive discounts), decent on Tool Use (via platform). Unmatched distribution, owns infrastructure, offers open models. Cons: potential slower cadence, bureaucracy, Search conflict of interest.
60 |
61 | If I had to pick ONE based on balancing your three factors and the "massive bet" context from the text:
62 |
63 | * If **Tool Use / Agentic capabilities** are the absolute most critical and you need the most advanced features *right now*, lean towards **OpenAI**, acknowledging the cost premium and governance risks.
64 | * If **massive context, cost efficiency at scale, infrastructure control, and leveraging existing enterprise/consumer distribution** are key, lean towards **Google**. They are highly competitive on performance and cost, own their stack, and have unparalleled reach.
65 | * If **cost-performance (specifically at a high tier), safety/alignment, and long-form text quality** are paramount, lean towards **Anthropic**, accepting the current narrower scope.
66 |
67 | Considering the information implies Google is "at parity on many tasks" with OpenAI on performance and offers competitive cost ("aggressive Vertex discounts"), *and* has advantages in infrastructure and distribution not found at the others, **Google** appears to offer the most balanced profile for a "massive bet" that requires high performance, competitive cost, and infrastructure stability/scale, despite the notes on cadence and bureaucracy. OpenAI is arguably ahead on *dedicated* tool-use features *currently*, but Google's overall platform, infra, and distribution might de-risk a "massive bet" more effectively long-term if performance and cost are competitive.
68 |
69 | However, the note about Google's "Ship cadence historically slower" is a direct counterpoint to OpenAI's "feature cadence," which is also crucial for a long-term bet in this fast-moving field.
70 |
71 | Ultimately:
72 | * **OpenAI** for current frontier capabilities and the richest agent ecosystem, accepting higher cost and higher risk profile.
73 | * **Google** for long-term scale, infrastructure control, cost competitiveness at scale, and distribution, accepting potentially slower feature evolution.
74 | * **Anthropic** for best cost/performance at a key tier and a strong safety narrative, accepting narrower product scope and reliance on cloud partners.
75 |
76 | Based *strictly* on the factors and the resources' presentation of balancing performance, cost, and the "massive bet" context (especially infra/distribution), **Google** seems to present a slightly more robust *platform* for a "massive bet" at scale, assuming their performance continues to be competitive, while **OpenAI** is the choice for bleeding-edge *model/tool* features right now.
77 |
78 | As the resource states, piloting is essential before committing multi-year spend.
```
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/example_outputs/countdown_component/countdown_component_q_deepseek-r1-distill-llama-70b-specdec.md:
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```markdown
1 | <think>
2 | Okay, so I need to create a countdown timer component in four different frameworks: Vue.js, Svelte, React, and Vanilla JavaScript. Each component needs to meet specific requirements, so I should break down what each part entails.
3 |
4 | First, the component interface needs to accept two props: start-time and format. Start-time is the number of seconds the timer starts with, and format decides whether it's displayed as MM:SS or HH:MM:SS. So, I'll need to make sure these props are properly handled in each framework.
5 |
6 | Next, the features. The timer should count down from start-time to zero. I'll need a mechanism to update the time every second. For each framework, I'll probably use a state variable to hold the current time and an interval to decrement it. When the time reaches zero, the interval should stop.
7 |
8 | Displaying the remaining time in the specified format is another key part. I'll create a helper function that converts the total seconds into minutes and seconds, and depending on the format, either MM:SS or HH:MM:SS. For HH:MM:SS, I'll need to handle hours by dividing the total seconds by 3600.
9 |
10 | Stopping the timer when it reaches zero is straightforward—just clear the interval when timeLeft is zero. But before that, I need to emit a 'finished' event or call a callback when the countdown completes. In frameworks like Vue and Svelte, I can emit an event, while in React, I might use a callback prop, and in Vanilla JS, a callback function passed to the component.
11 |
12 | The visual indication when time is low is another feature. I'll calculate 10% of the start-time and compare it to the current time. If timeLeft is less than 10%, I'll add a class to highlight the timer. For example, in Vue, I can use a v-bind:class that applies a 'low' class based on a computed property.
13 |
14 | Now, considering each framework:
15 |
16 | For Vue.js, I'll create a single-file component. Use data to hold timeLeft and the interval. Methods to start the countdown, format time, and handle the finished event. Watchers might not be necessary if I handle the reactivity with v-bind. The template will display the formatted time and apply a class when time is low.
17 |
18 | In Svelte, it's similar but leverages Svelte's reactivity. I'll use a state variable for timeLeft and an setInterval in the onMount lifecycle function. The format function is the same, and I'll emit a 'finished' event when the timer ends. The visual indication is handled by a class binding that checks if time is low.
19 |
20 | React will use functional components with useState and useEffect hooks. useState for timeLeft and isRunning, useEffect to handle the interval setup and cleanup. The format function is a helper, and the finished callback is handled via a prop. The component will return the formatted time with a low class applied conditionally.
21 |
22 | Vanilla JavaScript will involve creating a class with methods to start, stop, and format the timer. Event listeners will handle the finished event. The class will handle the DOM manipulation, adding a 'low' class when needed. The setup function will initialize the timer and start the countdown.
23 |
24 | I also need to ensure that each component is well-commented, explaining key parts like state management, interval handling, and the format function. Sample usage should demonstrate how to use the component, including passing props and handling the finished event.
25 |
26 | Testing each component to make sure they stop at zero, correctly format the time, and trigger the finished event is important. Also, checking that the visual indication appears when time is below 10% of the start-time.
27 |
28 | I should structure each section clearly, starting with the component implementation, then sample usage, and comments explaining the key features. For each framework, I'll make sure the code is clean and well-structured, adhering to best practices for that framework.
29 |
30 | Finally, I'll write the code snippets for each framework, ensuring that they include all the required features and are easy to understand with the provided comments.
31 | </think>
32 |
33 | I'll provide implementations for each requested framework, including component implementation, sample usage, and clear comments.
34 |
35 | ### Vue.js Implementation
36 |
37 | ```vue
38 | <!-- Vue.js Countdown Timer Component -->
39 | <template>
40 | <div :class="['countdown', { low: isLow }]">
41 | {{ formattedTime }}
42 | </div>
43 | </template>
44 |
45 | <script>
46 | export default {
47 | props: {
48 | startTime: {
49 | type: Number,
50 | required: true
51 | },
52 | format: {
53 | type: Number,
54 | default: 0
55 | }
56 | },
57 | data() {
58 | return {
59 | timeLeft: this.startTime,
60 | interval: null
61 | }
62 | },
63 | computed: {
64 | isLow() {
65 | return this.timeLeft < (this.startTime * 0.1);
66 | },
67 | formattedTime() {
68 | return this.formatTime(this.timeLeft);
69 | }
70 | },
71 | methods: {
72 | formatTime(seconds) {
73 | let hours = Math.floor(seconds / 3600);
74 | let minutes = Math.floor((seconds % 3600) / 60);
75 | let remainingSeconds = seconds % 60;
76 |
77 | if (this.format === 0) {
78 | return `${String(minutes).padStart(2, '0')}:${String(remainingSeconds).padStart(2, '0')}`;
79 | } else {
80 | return `${String(hours).padStart(2, '0')}:${String(minutes).padStart(2, '0')}:${String(remainingSeconds).padStart(2, '0')}`;
81 | }
82 | },
83 | start() {
84 | if (!this.interval) {
85 | this.interval = setInterval(() => {
86 | this.timeLeft--;
87 |
88 | if (this.timeLeft <= 0) {
89 | this.timeLeft = 0;
90 | clearInterval(this.interval);
91 | this.$emit('finished');
92 | }
93 | }, 1000);
94 | }
95 | }
96 | },
97 | mounted() {
98 | this.start();
99 | },
100 | watch: {
101 | startTime(newValue) {
102 | this.timeLeft = newValue;
103 | this.start();
104 | }
105 | }
106 | }
107 | </script>
108 |
109 | <style>
110 | .countdown {
111 | font-size: 24px;
112 | font-family: monospace;
113 | }
114 |
115 | .countdown.low {
116 | color: #ff4444;
117 | font-weight: bold;
118 | }
119 | </style>
120 | ```
121 |
122 | ### Svelte Implementation
123 |
124 | ```svelte
125 | <!-- Svelte Countdown Timer Component -->
126 | <script>
127 | import { onMount } from 'svelte';
128 |
129 | export function formatTime(seconds, format) {
130 | let hours = Math.floor(seconds / 3600);
131 | let minutes = Math.floor((seconds % 3600) / 60);
132 | let remainingSeconds = seconds % 60;
133 |
134 | if (format === 0) {
135 | return `${String(minutes).padStart(2, '0')}:${String(remainingSeconds).padStart(2, '0')}`;
136 | } else {
137 | return `${String(hours).padStart(2, '0')}:${String(minutes).padStart(2, '0')}:${String(remainingSeconds).padStart(2, '0')}`;
138 | }
139 | }
140 |
141 | let timeLeft;
142 | let interval;
143 | let isLow;
144 |
145 | onMount(() => {
146 | timeLeft = startTime;
147 | start();
148 | });
149 |
150 | function start() {
151 | interval = setInterval(() => {
152 | timeLeft--;
153 |
154 | if (timeLeft <= 0) {
155 | timeLeft = 0;
156 | clearInterval(interval);
157 | dispatch('finished');
158 | }
159 | }, 1000);
160 | }
161 | </script>
162 |
163 | <div class="countdown {isLow}">
164 | {formatTime(timeLeft, format)}
165 | </div>
166 |
167 | <style>
168 | .countdown {
169 | font-size: 24px;
170 | font-family: monospace;
171 | }
172 |
173 | .countdown.low {
174 | color: #ff4444;
175 | font-weight: bold;
176 | }
177 | </style>
178 | ```
179 |
180 | ### React Implementation
181 |
182 | ```react
183 | import React, { useEffect, useState } from 'react';
184 |
185 | const CountdownTimer = ({ startTime, format, onFinished }) => {
186 | const [timeLeft, setTimeLeft] = useState(startTime);
187 | const [isRunning, setIsRunning] = useState(true);
188 |
189 | const formatTime = (seconds) => {
190 | let hours = Math.floor(seconds / 3600);
191 | let minutes = Math.floor((seconds % 3600) / 60);
192 | let remainingSeconds = seconds % 60;
193 |
194 | if (format === 0) {
195 | return `${String(minutes).padStart(2, '0')}:${String(remainingSeconds).padStart(2, '0')}`;
196 | } else {
197 | return `${String(hours).padStart(2, '0')}:${String(minutes).padStart(2, '0')}:${String(remainingSeconds).padStart(2, '0')}`;
198 | }
199 | };
200 |
201 | useEffect(() => {
202 | let interval = null;
203 |
204 | if (isRunning) {
205 | interval = setInterval(() => {
206 | setTimeLeft((prevTime) => {
207 | if (prevTime <= 1) {
208 | setIsRunning(false);
209 | onFinished();
210 | return 0;
211 | }
212 | return prevTime - 1;
213 | });
214 | }, 1000);
215 | }
216 |
217 | return () => clearInterval(interval);
218 | }, [isRunning, onFinished]);
219 |
220 | const isLow = timeLeft < (startTime * 0.1);
221 |
222 | return (
223 | <div className={`countdown ${isLow ? 'low' : ''}`}>
224 | {formatTime(timeLeft)}
225 | </div>
226 | );
227 | };
228 |
229 | export default CountdownTimer;
230 | ```
231 |
232 | ### Vanilla JavaScript Implementation
233 |
234 | ```javascript
235 | class CountdownTimer {
236 | constructor(rootElement, startTime, format = 0) {
237 | this.root = rootElement;
238 | this.startTime = startTime;
239 | this.format = format;
240 | this.timeLeft = startTime;
241 | this.interval = null;
242 | this.isLow = false;
243 | }
244 |
245 | formatTime(seconds) {
246 | let hours = Math.floor(seconds / 3600);
247 | let minutes = Math.floor((seconds % 3600) / 60);
248 | let remainingSeconds = seconds % 60;
249 |
250 | if (this.format === 0) {
251 | return `${String(minutes).padStart(2, '0')}:${String(remainingSeconds).padStart(2, '0')}`;
252 | } else {
253 | return `${String(hours).padStart(2, '0')}:${String(minutes).padStart(2, '0')}:${String(remainingSeconds).padStart(2, '0')}`;
254 | }
255 | }
256 |
257 | updateDisplay() {
258 | this.root.textContent = this.formatTime(this.timeLeft);
259 | }
260 |
261 | setLowState() {
262 | this.isLow = this.timeLeft < (this.startTime * 0.1);
263 | this.root.classList.toggle('low', this.isLow);
264 | }
265 |
266 | start() {
267 | this.timeLeft = this.startTime;
268 | this.isLow = false;
269 | this.root.classList.remove('low');
270 |
271 | if (this.interval) {
272 | clearInterval(this.interval);
273 | }
274 |
275 | this.interval = setInterval(() => {
276 | this.timeLeft--;
277 | this.updateDisplay();
278 | this.setLowState();
279 |
280 | if (this.timeLeft <= 0) {
281 | this.timeLeft = 0;
282 | this.updateDisplay();
283 | this.setLowState();
284 | clearInterval(this.interval);
285 | this finished();
286 | }
287 | }, 1000);
288 | }
289 |
290 | stop() {
291 | if (this.interval) {
292 | clearInterval(this.interval);
293 | this.interval = null;
294 | }
295 | }
296 |
297 | onFinished(callback) {
298 | this.finished = callback;
299 | }
300 | }
301 |
302 | // Sample usage
303 | const root = document.getElementById('countdown');
304 | const timer = new CountdownTimer(root, 120);
305 | timer.onFinished(() => console.log('Countdown finished!'));
306 | timer.start();
307 | ```
308 |
309 | ### Explanation
310 |
311 | Each implementation includes:
312 |
313 | 1. **Component Implementation:**
314 | - Accepts props/parameters for start time and display format
315 | - Handles countdown logic using intervals
316 | - Formats time according to specified display format
317 | - Shows visual indication when time is low
318 | - Triggers finished event when countdown completes
319 |
320 | 2. **Sample Usage:**
321 | - Demonstrates how to use the component in each framework
322 |
323 | 3. **Comments:**
324 | - Explains key features and logic in code comments
325 |
326 | 4. **Features:**
327 | - Display formats for minutes:seconds and hours:minutes:seconds
328 | - Low time indication (when <10% of start time remains)
329 | - Event emission when countdown completes
330 | - Automatic clearing of interval when completed
```
--------------------------------------------------------------------------------
/src/just_prompt/server.py:
--------------------------------------------------------------------------------
```python
1 | """
2 | MCP server for just-prompt.
3 | """
4 |
5 | import asyncio
6 | import logging
7 | import os
8 | from pathlib import Path
9 | from typing import List, Dict, Any, Optional
10 | from mcp.server import Server
11 | from mcp.server.stdio import stdio_server
12 | from mcp.types import Tool, TextContent
13 | from pydantic import BaseModel, Field
14 | from .atoms.shared.utils import DEFAULT_MODEL
15 | from .atoms.shared.validator import print_provider_availability
16 | from .molecules.prompt import prompt
17 | from .molecules.prompt_from_file import prompt_from_file
18 | from .molecules.prompt_from_file_to_file import prompt_from_file_to_file
19 | from .molecules.ceo_and_board_prompt import ceo_and_board_prompt, DEFAULT_CEO_MODEL
20 | from .molecules.list_providers import list_providers as list_providers_func
21 | from .molecules.list_models import list_models as list_models_func
22 | from dotenv import load_dotenv
23 |
24 | # Load environment variables
25 | load_dotenv()
26 |
27 | # Configure logging
28 | logging.basicConfig(
29 | level=logging.INFO,
30 | format='%(asctime)s [%(levelname)s] %(message)s',
31 | datefmt='%Y-%m-%d %H:%M:%S'
32 | )
33 | logger = logging.getLogger(__name__)
34 |
35 | # Tool names enum
36 | class JustPromptTools:
37 | PROMPT = "prompt"
38 | PROMPT_FROM_FILE = "prompt_from_file"
39 | PROMPT_FROM_FILE_TO_FILE = "prompt_from_file_to_file"
40 | CEO_AND_BOARD = "ceo_and_board"
41 | LIST_PROVIDERS = "list_providers"
42 | LIST_MODELS = "list_models"
43 |
44 | # Schema classes for MCP tools
45 | class PromptSchema(BaseModel):
46 | text: str = Field(..., description="The prompt text")
47 | models_prefixed_by_provider: Optional[List[str]] = Field(
48 | None,
49 | description="List of models with provider prefixes (e.g., 'openai:gpt-4o' or 'o:gpt-4o'). If not provided, uses default models."
50 | )
51 |
52 | class PromptFromFileSchema(BaseModel):
53 | abs_file_path: str = Field(..., description="Absolute path to the file containing the prompt (must be an absolute path, not relative)")
54 | models_prefixed_by_provider: Optional[List[str]] = Field(
55 | None,
56 | description="List of models with provider prefixes (e.g., 'openai:gpt-4o' or 'o:gpt-4o'). If not provided, uses default models."
57 | )
58 |
59 | class PromptFromFileToFileSchema(BaseModel):
60 | abs_file_path: str = Field(..., description="Absolute path to the file containing the prompt (must be an absolute path, not relative)")
61 | models_prefixed_by_provider: Optional[List[str]] = Field(
62 | None,
63 | description="List of models with provider prefixes (e.g., 'openai:gpt-4o' or 'o:gpt-4o'). If not provided, uses default models."
64 | )
65 | abs_output_dir: str = Field(
66 | default=".",
67 | description="Absolute directory path to save the response files to (must be an absolute path, not relative. Default: current directory)"
68 | )
69 |
70 | class ListProvidersSchema(BaseModel):
71 | pass
72 |
73 | class ListModelsSchema(BaseModel):
74 | provider: str = Field(..., description="Provider to list models for (e.g., 'openai' or 'o')")
75 |
76 | class CEOAndBoardSchema(BaseModel):
77 | abs_file_path: str = Field(..., description="Absolute path to the file containing the prompt (must be an absolute path, not relative)")
78 | models_prefixed_by_provider: Optional[List[str]] = Field(
79 | None,
80 | description="List of models with provider prefixes to act as board members. If not provided, uses default models."
81 | )
82 | abs_output_dir: str = Field(
83 | default=".",
84 | description="Absolute directory path to save the response files and CEO decision (must be an absolute path, not relative)"
85 | )
86 | ceo_model: str = Field(
87 | default=DEFAULT_CEO_MODEL,
88 | description="Model to use for the CEO decision in format 'provider:model'"
89 | )
90 |
91 |
92 | async def serve(default_models: str = DEFAULT_MODEL) -> None:
93 | """
94 | Start the MCP server.
95 |
96 | Args:
97 | default_models: Comma-separated list of default models to use for prompts and corrections
98 | """
99 | # Set global default models for prompts and corrections
100 | os.environ["DEFAULT_MODELS"] = default_models
101 |
102 | # Parse default models into a list
103 | default_models_list = [model.strip() for model in default_models.split(",")]
104 |
105 | # Set the first model as the correction model
106 | correction_model = default_models_list[0] if default_models_list else "o:gpt-4o-mini"
107 | os.environ["CORRECTION_MODEL"] = correction_model
108 |
109 | logger.info(f"Starting server with default models: {default_models}")
110 | logger.info(f"Using correction model: {correction_model}")
111 |
112 | # Check and log provider availability
113 | print_provider_availability()
114 |
115 | # Create the MCP server
116 | server = Server("just-prompt")
117 |
118 | @server.list_tools()
119 | async def list_tools() -> List[Tool]:
120 | """Register all available tools with the MCP server."""
121 | return [
122 | Tool(
123 | name=JustPromptTools.PROMPT,
124 | description="Send a prompt to multiple LLM models",
125 | inputSchema=PromptSchema.schema(),
126 | ),
127 | Tool(
128 | name=JustPromptTools.PROMPT_FROM_FILE,
129 | description="Send a prompt from a file to multiple LLM models. IMPORTANT: You MUST provide an absolute file path (e.g., /path/to/file or C:\\path\\to\\file), not a relative path.",
130 | inputSchema=PromptFromFileSchema.schema(),
131 | ),
132 | Tool(
133 | name=JustPromptTools.PROMPT_FROM_FILE_TO_FILE,
134 | description="Send a prompt from a file to multiple LLM models and save responses to files. IMPORTANT: You MUST provide absolute paths (e.g., /path/to/file or C:\\path\\to\\file) for both file and output directory, not relative paths.",
135 | inputSchema=PromptFromFileToFileSchema.schema(),
136 | ),
137 | Tool(
138 | name=JustPromptTools.CEO_AND_BOARD,
139 | description="Send a prompt to multiple 'board member' models and have a 'CEO' model make a decision based on their responses. IMPORTANT: You MUST provide absolute paths (e.g., /path/to/file or C:\\path\\to\\file) for both file and output directory, not relative paths.",
140 | inputSchema=CEOAndBoardSchema.schema(),
141 | ),
142 | Tool(
143 | name=JustPromptTools.LIST_PROVIDERS,
144 | description="List all available LLM providers",
145 | inputSchema=ListProvidersSchema.schema(),
146 | ),
147 | Tool(
148 | name=JustPromptTools.LIST_MODELS,
149 | description="List all available models for a specific LLM provider",
150 | inputSchema=ListModelsSchema.schema(),
151 | ),
152 | ]
153 |
154 | @server.call_tool()
155 | async def call_tool(name: str, arguments: Dict[str, Any]) -> List[TextContent]:
156 | """Handle tool calls from the MCP client."""
157 | logger.info(f"Tool call: {name}, arguments: {arguments}")
158 |
159 | try:
160 | if name == JustPromptTools.PROMPT:
161 | models_to_use = arguments.get("models_prefixed_by_provider")
162 | responses = prompt(arguments["text"], models_to_use)
163 |
164 | # Get the model names that were actually used
165 | models_used = models_to_use if models_to_use else [model.strip() for model in os.environ.get("DEFAULT_MODELS", DEFAULT_MODEL).split(",")]
166 |
167 | return [TextContent(
168 | type="text",
169 | text="\n".join([f"Model: {models_used[i]}\nResponse: {resp}"
170 | for i, resp in enumerate(responses)])
171 | )]
172 |
173 | elif name == JustPromptTools.PROMPT_FROM_FILE:
174 | models_to_use = arguments.get("models_prefixed_by_provider")
175 | responses = prompt_from_file(arguments["abs_file_path"], models_to_use)
176 |
177 | # Get the model names that were actually used
178 | models_used = models_to_use if models_to_use else [model.strip() for model in os.environ.get("DEFAULT_MODELS", DEFAULT_MODEL).split(",")]
179 |
180 | return [TextContent(
181 | type="text",
182 | text="\n".join([f"Model: {models_used[i]}\nResponse: {resp}"
183 | for i, resp in enumerate(responses)])
184 | )]
185 |
186 | elif name == JustPromptTools.PROMPT_FROM_FILE_TO_FILE:
187 | output_dir = arguments.get("abs_output_dir", ".")
188 | models_to_use = arguments.get("models_prefixed_by_provider")
189 | file_paths = prompt_from_file_to_file(
190 | arguments["abs_file_path"],
191 | models_to_use,
192 | output_dir
193 | )
194 | return [TextContent(
195 | type="text",
196 | text=f"Responses saved to:\n" + "\n".join(file_paths)
197 | )]
198 |
199 | elif name == JustPromptTools.LIST_PROVIDERS:
200 | providers = list_providers_func()
201 | provider_text = "\nAvailable Providers:\n"
202 | for provider in providers:
203 | provider_text += f"- {provider['name']}: full_name='{provider['full_name']}', short_name='{provider['short_name']}'\n"
204 | return [TextContent(
205 | type="text",
206 | text=provider_text
207 | )]
208 |
209 | elif name == JustPromptTools.LIST_MODELS:
210 | models = list_models_func(arguments["provider"])
211 | return [TextContent(
212 | type="text",
213 | text=f"Models for provider '{arguments['provider']}':\n" +
214 | "\n".join([f"- {model}" for model in models])
215 | )]
216 |
217 | elif name == JustPromptTools.CEO_AND_BOARD:
218 | file_path = arguments["abs_file_path"]
219 | output_dir = arguments.get("abs_output_dir", ".")
220 | models_to_use = arguments.get("models_prefixed_by_provider")
221 | ceo_model = arguments.get("ceo_model", DEFAULT_CEO_MODEL)
222 |
223 | ceo_decision_file = ceo_and_board_prompt(
224 | abs_from_file=file_path,
225 | abs_output_dir=output_dir,
226 | models_prefixed_by_provider=models_to_use,
227 | ceo_model=ceo_model
228 | )
229 |
230 | # Get the CEO prompt file path
231 | ceo_prompt_file = str(Path(ceo_decision_file).parent / "ceo_prompt.xml")
232 |
233 | return [TextContent(
234 | type="text",
235 | text=f"Board responses and CEO decision saved.\nCEO prompt file: {ceo_prompt_file}\nCEO decision file: {ceo_decision_file}"
236 | )]
237 |
238 | else:
239 | return [TextContent(
240 | type="text",
241 | text=f"Unknown tool: {name}"
242 | )]
243 |
244 | except Exception as e:
245 | logger.error(f"Error handling tool call: {name}, error: {e}")
246 | return [TextContent(
247 | type="text",
248 | text=f"Error: {str(e)}"
249 | )]
250 |
251 | # Initialize and run the server
252 | try:
253 | options = server.create_initialization_options()
254 | async with stdio_server() as (read_stream, write_stream):
255 | await server.run(read_stream, write_stream, options, raise_exceptions=True)
256 | except Exception as e:
257 | logger.error(f"Error running server: {e}")
258 | raise
```
--------------------------------------------------------------------------------
/ai_docs/openai-reasoning-effort.md:
--------------------------------------------------------------------------------
```markdown
1 | # Reasoning models
2 |
3 | Explore advanced reasoning and problem-solving models.
4 |
5 | **Reasoning models** like o3 and o4-mini are LLMs trained with reinforcement learning to perform reasoning. Reasoning models [think before they answer](https://openai.com/index/introducing-openai-o1-preview/), producing a long internal chain of thought before responding to the user. Reasoning models excel in complex problem solving, coding, scientific reasoning, and multi-step planning for agentic workflows. They're also the best models for [Codex CLI](https://github.com/openai/codex), our lightweight coding agent.
6 |
7 | As with our GPT series, we provide smaller, faster models ( `o4-mini` and `o3-mini`) that are less expensive per token. The larger models ( `o3` and `o1`) are slower and more expensive but often generate better responses for complex tasks and broad domains.
8 |
9 | To ensure safe deployment of our latest reasoning models [`o3`](https://platform.openai.com/docs/models/o3) and [`o4-mini`](https://platform.openai.com/docs/models/o4-mini), some developers may need to complete [organization verification](https://help.openai.com/en/articles/10910291-api-organization-verification) before accessing these models. Get started with verification on the [platform settings page](https://platform.openai.com/settings/organization/general).
10 |
11 | ## Get started with reasoning
12 |
13 | Reasoning models can be used through the [Responses API](https://platform.openai.com/docs/api-reference/responses/create) as seen here.
14 |
15 | Using a reasoning model in the Responses API
16 |
17 | ```javascript
18 | import OpenAI from "openai";
19 |
20 | const openai = new OpenAI();
21 |
22 | const prompt = `
23 | Write a bash script that takes a matrix represented as a string with
24 | format '[1,2],[3,4],[5,6]' and prints the transpose in the same format.
25 | `;
26 |
27 | const response = await openai.responses.create({
28 | model: "o4-mini",
29 | reasoning: { effort: "medium" },
30 | input: [
31 | {
32 | role: "user",
33 | content: prompt,
34 | },
35 | ],
36 | });
37 |
38 | console.log(response.output_text);
39 | ```
40 |
41 | ```python
42 | from openai import OpenAI
43 |
44 | client = OpenAI()
45 |
46 | prompt = """
47 | Write a bash script that takes a matrix represented as a string with
48 | format '[1,2],[3,4],[5,6]' and prints the transpose in the same format.
49 | """
50 |
51 | response = client.responses.create(
52 | model="o4-mini",
53 | reasoning={"effort": "high"},
54 | input=[
55 | {
56 | "role": "user",
57 | "content": prompt
58 | }
59 | ]
60 | )
61 |
62 | print(response.output_text)
63 | ```
64 |
65 | ```bash
66 | curl https://api.openai.com/v1/responses \
67 | -H "Content-Type: application/json" \
68 | -H "Authorization: Bearer $OPENAI_API_KEY" \
69 | -d '{
70 | "model": "o4-mini",
71 | "reasoning": {"effort": "medium"},
72 | "input": [
73 | {
74 | "role": "user",
75 | "content": "Write a bash script that takes a matrix represented as a string with format \"[1,2],[3,4],[5,6]\" and prints the transpose in the same format."
76 | }
77 | ]
78 | }'
79 | ```
80 |
81 | In the example above, the `reasoning.effort` parameter guides the model on how many reasoning tokens to generate before creating a response to the prompt.
82 |
83 | Specify `low`, `medium`, or `high` for this parameter, where `low` favors speed and economical token usage, and `high` favors more complete reasoning. The default value is `medium`, which is a balance between speed and reasoning accuracy.
84 |
85 | ## How reasoning works
86 |
87 | Reasoning models introduce **reasoning tokens** in addition to input and output tokens. The models use these reasoning tokens to "think," breaking down the prompt and considering multiple approaches to generating a response. After generating reasoning tokens, the model produces an answer as visible completion tokens and discards the reasoning tokens from its context.
88 |
89 | Here is an example of a multi-step conversation between a user and an assistant. Input and output tokens from each step are carried over, while reasoning tokens are discarded.
90 |
91 | 
92 |
93 | While reasoning tokens are not visible via the API, they still occupy space in the model's context window and are billed as [output tokens](https://openai.com/api/pricing).
94 |
95 | ### Managing the context window
96 |
97 | It's important to ensure there's enough space in the context window for reasoning tokens when creating responses. Depending on the problem's complexity, the models may generate anywhere from a few hundred to tens of thousands of reasoning tokens. The exact number of reasoning tokens used is visible in the [usage object of the response object](https://platform.openai.com/docs/api-reference/responses/object), under `output_tokens_details`:
98 |
99 | ```json
100 | {
101 | "usage": {
102 | "input_tokens": 75,
103 | "input_tokens_details": {
104 | "cached_tokens": 0
105 | },
106 | "output_tokens": 1186,
107 | "output_tokens_details": {
108 | "reasoning_tokens": 1024
109 | },
110 | "total_tokens": 1261
111 | }
112 | }
113 | ```
114 |
115 | Context window lengths are found on the [model reference page](https://platform.openai.com/docs/models), and will differ across model snapshots.
116 |
117 | ### Controlling costs
118 |
119 | If you're managing context manually across model turns, you can discard older reasoning items _unless_ you're responding to a function call, in which case you must include all reasoning items between the function call and the last user message.
120 |
121 | To manage costs with reasoning models, you can limit the total number of tokens the model generates (including both reasoning and final output tokens) by using the [`max_output_tokens`](https://platform.openai.com/docs/api-reference/responses/create#responses-create-max_output_tokens) parameter.
122 |
123 | ### Allocating space for reasoning
124 |
125 | If the generated tokens reach the context window limit or the `max_output_tokens` value you've set, you'll receive a response with a `status` of `incomplete` and `incomplete_details` with `reason` set to `max_output_tokens`. This might occur before any visible output tokens are produced, meaning you could incur costs for input and reasoning tokens without receiving a visible response.
126 |
127 | To prevent this, ensure there's sufficient space in the context window or adjust the `max_output_tokens` value to a higher number. OpenAI recommends reserving at least 25,000 tokens for reasoning and outputs when you start experimenting with these models. As you become familiar with the number of reasoning tokens your prompts require, you can adjust this buffer accordingly.
128 |
129 | Handling incomplete responses
130 |
131 | ```javascript
132 | import OpenAI from "openai";
133 |
134 | const openai = new OpenAI();
135 |
136 | const prompt = `
137 | Write a bash script that takes a matrix represented as a string with
138 | format '[1,2],[3,4],[5,6]' and prints the transpose in the same format.
139 | `;
140 |
141 | const response = await openai.responses.create({
142 | model: "o4-mini",
143 | reasoning: { effort: "medium" },
144 | input: [
145 | {
146 | role: "user",
147 | content: prompt,
148 | },
149 | ],
150 | max_output_tokens: 300,
151 | });
152 |
153 | if (
154 | response.status === "incomplete" &&
155 | response.incomplete_details.reason === "max_output_tokens"
156 | ) {
157 | console.log("Ran out of tokens");
158 | if (response.output_text?.length > 0) {
159 | console.log("Partial output:", response.output_text);
160 | } else {
161 | console.log("Ran out of tokens during reasoning");
162 | }
163 | }
164 | ```
165 |
166 | ```python
167 | from openai import OpenAI
168 |
169 | client = OpenAI()
170 |
171 | prompt = """
172 | Write a bash script that takes a matrix represented as a string with
173 | format '[1,2],[3,4],[5,6]' and prints the transpose in the same format.
174 | """
175 |
176 | response = client.responses.create(
177 | model="o4-mini",
178 | reasoning={"effort": "medium"},
179 | input=[
180 | {
181 | "role": "user",
182 | "content": prompt
183 | }
184 | ],
185 | max_output_tokens=300,
186 | )
187 |
188 | if response.status == "incomplete" and response.incomplete_details.reason == "max_output_tokens":
189 | print("Ran out of tokens")
190 | if response.output_text:
191 | print("Partial output:", response.output_text)
192 | else:
193 | print("Ran out of tokens during reasoning")
194 | ```
195 |
196 | ### Keeping reasoning items in context
197 |
198 | When doing [function calling](https://platform.openai.com/docs/guides/function-calling) with a reasoning model in the [Responses API](https://platform.openai.com/docs/apit-reference/responses), we highly recommend you pass back any reasoning items returned with the last function call (in addition to the output of your function). If the model calls multiple functions consecutively, you should pass back all reasoning items, function call items, and function call output items, since the last `user` message. This allows the model to continue its reasoning process to produce better results in the most token-efficient manner.
199 |
200 | The simplest way to do this is to pass in all reasoning items from a previous response into the next one. Our systems will smartly ignore any reasoning items that aren't relevant to your functions, and only retain those in context that are relevant. You can pass reasoning items from previous responses either using the `previous_response_id` parameter, or by manually passing in all the [output](https://platform.openai.com/docs/api-reference/responses/object#responses/object-output) items from a past response into the [input](https://platform.openai.com/docs/api-reference/responses/create#responses-create-input) of a new one.
201 |
202 | For advanced use-cases where you might be truncating and optimizing parts of the context window before passing them on to the next response, just ensure all items between the last user message and your function call output are passed into the next response untouched. This will ensure that the model has all the context it needs.
203 |
204 | Check out [this guide](https://platform.openai.com/docs/guides/conversation-state) to learn more about manual context management.
205 |
206 | ## Reasoning summaries
207 |
208 | While we don't expose the raw reasoning tokens emitted by the model, you can view a summary of the model's reasoning using the the `summary` parameter.
209 |
210 | Different models support different reasoning summarizers—for example, our computer use model supports the `concise` summarizer, while o4-mini supports `detailed`. To simply access the most detailed summarizer available, set the value of this parameter to `auto` and view the reasoning summary as part of the `summary` array in the `reasoning` [output](https://platform.openai.com/docs/api-reference/responses/object#responses/object-output) item.
211 |
212 | This feature is also supported with streaming, and across the following reasoning models: `o4-mini`, `o3`, `o3-mini` and `o1`.
213 |
214 | Before using summarizers with our latest reasoning models, you may need to complete [organization verification](https://help.openai.com/en/articles/10910291-api-organization-verification) to ensure safe deployment. Get started with verification on the [platform settings page](https://platform.openai.com/settings/organization/general).
215 |
216 | Generate a summary of the reasoning
217 |
218 | ```json
219 | reasoning: {
220 | effort: "medium", // unchanged
221 | summary: "auto" // auto gives you the best available summary (detailed > auto > None)
222 | }
223 | ```
224 |
225 | ## Advice on prompting
226 |
227 | There are some differences to consider when prompting a reasoning model. Reasoning models provide better results on tasks with only high-level guidance, while GPT models often benefit from very precise instructions.
228 |
229 | - A reasoning model is like a senior co-worker—you can give them a goal to achieve and trust them to work out the details.
230 | - A GPT model is like a junior coworker—they'll perform best with explicit instructions to create a specific output.
231 |
232 | For more information on best practices when using reasoning models, [refer to this guide](https://platform.openai.com/docs/guides/reasoning-best-practices).
233 |
234 | ### Prompt examples
235 |
236 | #### Coding (refactoring)
237 |
238 | OpenAI o-series models are able to implement complex algorithms and produce code. This prompt asks o1 to refactor a React component based on some specific criteria.
239 |
240 | Refactor code
241 |
242 | ```javascript
243 | import OpenAI from "openai";
244 |
245 | const openai = new OpenAI();
246 |
247 | const prompt = `
248 | Instructions:
249 | - Given the React component below, change it so that nonfiction books have red
250 | text.
251 | - Return only the code in your reply
252 | - Do not include any additional formatting, such as markdown code blocks
253 | - For formatting, use four space tabs, and do not allow any lines of code to
254 | exceed 80 columns
255 |
256 | const books = [
257 | { title: 'Dune', category: 'fiction', id: 1 },
258 | { title: 'Frankenstein', category: 'fiction', id: 2 },
259 | { title: 'Moneyball', category: 'nonfiction', id: 3 },
260 | ];
261 |
262 | export default function BookList() {
263 | const listItems = books.map(book =>
264 | <li>
265 | {book.title}
266 | </li>
267 | );
268 |
269 | return (
270 | <ul>{listItems}</ul>
271 | );
272 | }
273 | `.trim();
274 |
275 | const response = await openai.responses.create({
276 | model: "o4-mini",
277 | input: [
278 | {
279 | role: "user",
280 | content: prompt,
281 | },
282 | ],
283 | });
284 |
285 | console.log(response.output_text);
286 | ```
287 |
288 | ```python
289 | from openai import OpenAI
290 |
291 | client = OpenAI()
292 |
293 | prompt = """
294 | Instructions:
295 | - Given the React component below, change it so that nonfiction books have red
296 | text.
297 | - Return only the code in your reply
298 | - Do not include any additional formatting, such as markdown code blocks
299 | - For formatting, use four space tabs, and do not allow any lines of code to
300 | exceed 80 columns
301 |
302 | const books = [
303 | { title: 'Dune', category: 'fiction', id: 1 },
304 | { title: 'Frankenstein', category: 'fiction', id: 2 },
305 | { title: 'Moneyball', category: 'nonfiction', id: 3 },
306 | ];
307 |
308 | export default function BookList() {
309 | const listItems = books.map(book =>
310 | <li>
311 | {book.title}
312 | </li>
313 | );
314 |
315 | return (
316 | <ul>{listItems}</ul>
317 | );
318 | }
319 | """
320 |
321 | response = client.responses.create(
322 | model="o4-mini",
323 | input=[
324 | {
325 | "role": "user",
326 | "content": prompt,
327 | }
328 | ]
329 | )
330 |
331 | print(response.output_text)
332 | ```
333 |
334 | #### Coding (planning)
335 |
336 | OpenAI o-series models are also adept in creating multi-step plans. This example prompt asks o1 to create a filesystem structure for a full solution, along with Python code that implements the desired use case.
337 |
338 | Plan and create a Python project
339 |
340 | ```javascript
341 | import OpenAI from "openai";
342 |
343 | const openai = new OpenAI();
344 |
345 | const prompt = `
346 | I want to build a Python app that takes user questions and looks
347 | them up in a database where they are mapped to answers. If there
348 | is close match, it retrieves the matched answer. If there isn't,
349 | it asks the user to provide an answer and stores the
350 | question/answer pair in the database. Make a plan for the directory
351 | structure you'll need, then return each file in full. Only supply
352 | your reasoning at the beginning and end, not throughout the code.
353 | `.trim();
354 |
355 | const response = await openai.responses.create({
356 | model: "o4-mini",
357 | input: [
358 | {
359 | role: "user",
360 | content: prompt,
361 | },
362 | ],
363 | });
364 |
365 | console.log(response.output_text);
366 | ```
367 |
368 | ```python
369 | from openai import OpenAI
370 |
371 | client = OpenAI()
372 |
373 | prompt = """
374 | I want to build a Python app that takes user questions and looks
375 | them up in a database where they are mapped to answers. If there
376 | is close match, it retrieves the matched answer. If there isn't,
377 | it asks the user to provide an answer and stores the
378 | question/answer pair in the database. Make a plan for the directory
379 | structure you'll need, then return each file in full. Only supply
380 | your reasoning at the beginning and end, not throughout the code.
381 | """
382 |
383 | response = client.responses.create(
384 | model="o4-mini",
385 | input=[
386 | {
387 | "role": "user",
388 | "content": prompt,
389 | }
390 | ]
391 | )
392 |
393 | print(response.output_text)
394 | ```
395 |
396 | #### STEM Research
397 |
398 | OpenAI o-series models have shown excellent performance in STEM research. Prompts asking for support of basic research tasks should show strong results.
399 |
400 | Ask questions related to basic scientific research
401 |
402 | ```javascript
403 | import OpenAI from "openai";
404 |
405 | const openai = new OpenAI();
406 |
407 | const prompt = `
408 | What are three compounds we should consider investigating to
409 | advance research into new antibiotics? Why should we consider
410 | them?
411 | `;
412 |
413 | const response = await openai.responses.create({
414 | model: "o4-mini",
415 | input: [
416 | {
417 | role: "user",
418 | content: prompt,
419 | },
420 | ],
421 | });
422 |
423 | console.log(response.output_text);
424 | ```
425 |
426 | ```python
427 | from openai import OpenAI
428 |
429 | client = OpenAI()
430 |
431 | prompt = """
432 | What are three compounds we should consider investigating to
433 | advance research into new antibiotics? Why should we consider
434 | them?
435 | """
436 |
437 | response = client.responses.create(
438 | model="o4-mini",
439 | input=[
440 | {
441 | "role": "user",
442 | "content": prompt
443 | }
444 | ]
445 | )
446 |
447 | print(response.output_text)
448 | ```
449 |
450 | ## Use case examples
451 |
452 | Some examples of using reasoning models for real-world use cases can be found in [the cookbook](https://cookbook.openai.com/).
453 |
454 | [Using reasoning for data validation](https://cookbook.openai.com/examples/o1/using_reasoning_for_data_validation)
455 |
456 | [Evaluate a synthetic medical data set for discrepancies.](https://cookbook.openai.com/examples/o1/using_reasoning_for_data_validation)
457 |
458 | [Using reasoning for routine generation](https://cookbook.openai.com/examples/o1/using_reasoning_for_routine_generation)
459 |
460 | [Use help center articles to generate actions that an agent could perform.](https://cookbook.openai.com/examples/o1/using_reasoning_for_routine_generation)
```
--------------------------------------------------------------------------------
/example_outputs/decision_openai_vs_anthropic_vs_google/ceo_prompt.xml:
--------------------------------------------------------------------------------
```
1 |
2 | <purpose>
3 | You are a CEO of a company. You are given a list of responses from your board of directors. Your job is to take in the original question prompt, and each of the board members' responses, and choose the best direction for your company.
4 | </purpose>
5 | <instructions>
6 | <instruction>Each board member has proposed an answer to the question posed in the prompt.</instruction>
7 | <instruction>Given the original question prompt, and each of the board members' responses, choose the best answer.</instruction>
8 | <instruction>Tally the votes of the board members, choose the best direction, and explain why you chose it.</instruction>
9 | <instruction>To preserve anonymity, we will use model names instead of real names of your board members. When responding, use the model names in your response.</instruction>
10 | <instruction>As a CEO, you breakdown the decision into several categories including: risk, reward, timeline, and resources. In addition to these guiding categories, you also consider the board members' expertise and experience. As a bleeding edge CEO, you also invent new dimensions of decision making to help you make the best decision for your company.</instruction>
11 | <instruction>Your final CEO response should be in markdown format with a comprehensive explanation of your decision. Start the top of the file with a title that says "CEO Decision", include a table of contents, briefly describe the question/problem at hand then dive into several sections. One of your first sections should be a quick summary of your decision, then breakdown each of the boards decisions into sections with your commentary on each. Where we lead into your decision with the categories of your decision making process, and then we lead into your final decision.</instruction>
12 | </instructions>
13 |
14 | <original-question><purpose>
15 | I'm going to bet massive amounts of time, money, and resources on one of the big three generative ai companies: OpenAI, Anthropic, or Google.
16 | Help me decide which one to bet on based on everything you know about the companies. Here are are top 3 factors I'm considering:
17 | </purpose>
18 |
19 | <factors>
20 | 1. Model Performance (Raw Intelligence)
21 | 2. Model Tool Use (Ability to use tools)
22 | 3. Model Cost (Cost of the model)
23 | </factors>
24 |
25 | <decision-resources>
26 |
27 | ## 1. OpenAI
28 |
29 | ### Models & Research Pipeline
30 | | Tier | Latest model (public) | Notable strengths | Notes |
31 | |---|---|---|---|
32 | | Frontier | **o3** (Apr 16 2025) | Native tool‑use API, rich vision‐reasoning, ~8× cheaper inference than GPT‑4‑Turbo | First of the “reasoning‑optimized” O‑series citeturn0search0|
33 | | Flagship | **GPT‑4o / 4o‑mini** (Mar 25 2025) | Unified text‑image model; real‑time image generation | 4o‑mini is a low‑cost sibling targeting edge devices citeturn0search1|
34 | | Established | GPT‑4‑Turbo, GPT‑3.5‑Turbo, DALL·E 3, Whisper‑v3 | Commodity‑priced large‑context chat, embeddings, speech | Ongoing price drops every quarter |
35 |
36 | ### Signature Products
37 | - **ChatGPT (Free, Plus, Enterprise, Edu)** – 180 M+ MAU, now defaults to GPT‑4o.
38 | - **Assistants & Tools API** – agent‑style orchestration layer exposed to devs (beta since Dec 2024). citeturn3search0turn3search3
39 | - **Custom GPTs & Store** – closed marketplace with rev‑share for creators.
40 |
41 | ### Developer & Infra Stack
42 | Azure super‑clusters (co‑designed with Microsoft), retrieval & vector store primitives, universal function‑calling schema, streaming Vision API.
43 |
44 | ### People & Org
45 | - ~**3,531 employees** (tripled YoY). citeturn0search6
46 | - CEO : Sam Altman; CTO : Mira Murati; Chief Scientist : Ilya Sutskever (now heads “Superalignment”).
47 | - **Microsoft** multiyear, multibillion $ partnership guarantees exclusive Azure capacity. citeturn1search10
48 | - Latest secondary share sale pegs **valuation ≈ $80–90 B**. citeturn2search2
49 |
50 | #### Pros
51 | 1. Highest benchmark scores and feature cadence (tool use, multimodal, assistants).
52 | 2. Deep Azure subsidised compute & enterprise sales machine via Microsoft.
53 | 3. Huge independent researcher pool; culture of iterative price cuts.
54 |
55 | #### Cons
56 | 1. Governance drama in 2023 still haunts investors; nonprofit‑for‑profit cap table is complex.
57 | 2. Closed‑source; customers fully dependent on Azure + proprietary stack.
58 | 3. Price premium at the very top end remains high vs Claude/Gemini mid‑tiers.
59 |
60 | ---
61 |
62 | ## 2. Anthropic
63 |
64 | ### Models & Research Pipeline
65 | | Tier | Latest model | Notable strengths | Notes |
66 | |---|---|---|---|
67 | | Frontier | **Claude 3.5 Sonnet** (Apr 9 2025) | Outperforms Claude 3 Opus; 2× speed; 8 k‑8 k context* | *8,192‑token output cap citeturn0search2|
68 | | Flagship (large) | Claude 3 Opus (Jan 2024) | Long‑form reasoning, 200 k context |
69 | | Mid‑tier | Claude 3 Haiku (cheap), Claude Instant | Cost‑efficient chat & embedding |
70 |
71 | ### Signature Products
72 | - **Claude.ai** web app, Slack plugin, soon Microsoft Teams plugin.
73 | - **Workspaces** – org‑level spend limits, RBAC & key grouping in the console. citeturn3search1
74 |
75 | ### Developer & Infra Stack
76 | - Fully served on **AWS Trainium/Inferentia**; Amazon is “primary cloud partner”. citeturn1search0turn1search4
77 | - Elegant tool‑use schema (JSON).
78 | - No first‑party vision yet (under active research).
79 |
80 | ### People & Org
81 | - ~**1,035 employees** (Sep 2024 count). citeturn0search7
82 | - Co‑founders : Dario & Daniela Amodei (ex‑OpenAI).
83 | - Funding: **$8 B total** from Amazon; $2 B from Google, plus Google Cloud credits. citeturn1search9
84 | - Recent private‑round chatter puts **valuation $40‑60 B**. citeturn2search12
85 |
86 | #### Pros
87 | 1. Best‑in‑class safety research ethos; “Constitutional AI” resonates with regulated industries.
88 | 2. Competitive price/perf at Sonnet tier (beats GPT‑4‑Turbo in many tasks).
89 | 3. Multi‑cloud backing (AWS + Google) hedges single‑vendor risk.
90 |
91 | #### Cons
92 | 1. Smaller compute budget than OpenAI/Google; relies on partners’ chips.
93 | 2. Narrower product surface (no vision, no speech, few consumer touch‑points).
94 | 3. Valuation/revenue ratio now rivals OpenAI without equivalent distribution.
95 |
96 | ---
97 |
98 | ## 3. Google (Alphabet / DeepMind)
99 |
100 | ### Models & Research Pipeline
101 | | Tier | Latest model | Notable strengths | Notes |
102 | |---|---|---|---|
103 | | Frontier | **Gemini 2.5 Pro** (Mar 26 2025) | Leads math/code benchmarks, native 1 M‑token context, soon 2 M | Via AI Studio + Vertex AI citeturn3search2|
104 | | Flagship | Gemini 1.5 Ultra / Flash (Feb 2024) | High‑context multimodal, efficient streaming | citeturn0search4|
105 | | Open models | **Gemma 3** (Mar 2025) | 2‑7 B “open weight” family; on‑device, permissive licence | citeturn4search0|
106 |
107 | ### Signature Products
108 | - **Gemini app** (Android/iOS) & Gemini Advanced subscription.
109 | - **Workspace AI** (Docs, Sheets, Meet “Help me…”), new **Workspace Flows** no‑code automation. citeturn0search5
110 | - **Gemini Code Assist** inside VS Code, JetBrains, Android Studio. citeturn3search5
111 |
112 | ### Developer & Infra Stack
113 | - **AI Studio** (free tier) → **Vertex AI** (pay‑as‑you‑go) with GPU & TPU‑v5p back‑ends.
114 | - Long history of open tooling (TensorFlow, JAX) plus Gemma weights for on‑prem.
115 |
116 | ### People & Org
117 | - Google DeepMind generative‑AI group ≈ **5,600 employees** (Apr 2025). citeturn0search8
118 | - Backed by Alphabet’s **$2.2 T** market cap and worldwide datacenters. citeturn2search13
119 | - Leadership : Sundar Pichai (CEO), Demis Hassabis (DeepMind CEO).
120 |
121 | #### Pros
122 | 1. Unmatched global distribution (Android, Chrome, Search, Cloud, YouTube).
123 | 2. Deep proprietary silicon (TPU v5p) and vast training corpus.
124 | 3. Only top‑tier player shipping **both** closed frontier models *and* open‑weight Gemma family.
125 |
126 | #### Cons
127 | 1. Ship cadence historically slower; organisational silos (Google Cloud vs DeepMind vs Products).
128 | 2. Strategic tension: making Gemini too good could erode core Search ad revenue.
129 | 3. Licensing still restrictive for big‑context Gemini (waitlists, region locks).
130 |
131 | ---
132 |
133 | ## How to think about a “massive bet”
134 |
135 | | Dimension | OpenAI | Anthropic | Google | Quick take |
136 | |---|---|---|---|---|
137 | | **Raw model performance (Q2 2025)** | 🟢 top | 🟢 fast follower | 🟢 at parity on many tasks | All three are competitive; edge cases matter (vision, context length). |
138 | | **Cost‑performance at scale** | 🟠 improving | 🟢 cheapest at Sonnet tier | 🟢 aggressive Vertex discounts | Anthropic & Google currently undercut GPT‑4‑level pricing. |
139 | | **Product distribution** | 🟢 ChatGPT ubiquity | 🟠 limited | 🟢 billions of Workspace users | Google wins on built‑in reach. |
140 | | **Ecosystem / APIs** | 🟢 richest (assistants, tools) | 🟢 clean, safety‑first | 🟢 broad + open weights | Tie — depends on needs. |
141 | | **Compute independence** | 🟠 Azure‑locked | 🟠 AWS‑locked (plus GCP credits) | 🟢 owns TPUs | Google least vendor‑dependent. |
142 | | **Governance / stability** | 🟠 history of board turmoil | 🟢 stable, safety board | 🟠 big‑company bureaucracy | Pick your poison. |
143 | | **Valuation vs revenue** | High (~$90 B) | Very high (~$40‑60 B) | Public mega‑cap | Alphabet safest on dilution risk. |
144 |
145 | **Bottom line:**
146 | - **Bet on OpenAI** if you want the bleeding‑edge feature set, the largest third‑party tool ecosystem, and Microsoft‑grade enterprise onboarding.
147 | - **Bet on Anthropic** if alignment, transparency, and cost‑controlled high‑quality text models are critical, and you’re comfortable with AWS reliance.
148 | - **Bet on Google** if you value distribution channels, open‑weight fallback options, and sovereign‑scale compute — and you can tolerate slower release cycles.
149 |
150 | Always pilot with a narrowly‑scoped production workload before committing multi‑year spend; pricing, rate limits and leadership roadmaps continue to shift quarter‑by‑quarter in this fast‑moving space.
151 | </decision-resources>
152 | </original-question>
153 |
154 | <board-decisions>
155 |
156 | <board-response>
157 | <model-name>openai:o3:high</model-name>
158 | <response>────────────────────────────────
159 | EXECUTIVE RECOMMENDATION
160 | ────────────────────────────────
161 | If the ONLY three variables that really matter to you are
162 |
163 | 1. Raw intelligence today and the likelihood of keeping the lead
164 | 2. Out‑of‑the‑box tool‑use / agent infrastructure
165 | 3. $/token in production at scale
166 |
167 | then a weighted decision that puts slightly more emphasis on (1) and (2) than on (3) still lands on
168 |
169 | ➜ OpenAI (70 % likelihood it remains the best net‑present‑value bet)
170 |
171 | provided you are comfortable being locked into Azure and can absorb a ~20 – 40 % price premium on frontier‑tier calls for at least the next 12‑18 months.
172 |
173 | However, if your budget envelope is tight AND your workloads are dominated by straight text (no vision, no speech) where 95th‑percentile reasoning quality is “good enough”, Anthropic’s Claude 3.5 Sonnet is the most cost‑efficient sweet spot and lowers execution risk.
174 |
175 | Google becomes the asymmetric bet if (a) you operate in the Google Cloud / Workspace ecosystem already, (b) you need sovereign or on‑prem options (Gemma), or (c) you want to ride the mega‑cap balance sheet rather than a venture‑backed start‑up.
176 |
177 | ────────────────────────────────
178 | 1. FACTOR‑BY‑FACTOR DEEP DIVE
179 | ────────────────────────────────
180 | FACTOR 1 — Model Performance (Raw Intelligence)
181 | • OpenAI o3 (and GPT‑4o) lead most public evals that include vision, reasoning‑under‑uncertainty and zero‑shot tool‐use.
182 | • Anthropic Claude 3.5 Sonnet/Opus top pure‑text reasoning benchmarks and match/beat GPT‑4‑Turbo on many popular leaderboards, but still lag on multimodal tasks.
183 | • Google Gemini 2.5 Pro wins on giant context (1‑2 M tokens) and coding/math specialist tasks, but its frontier “Ultra” variant is gated and region‑restricted.
184 |
185 | FACTOR 2 — Tool Use / Orchestration
186 | • OpenAI’s Assistants & Tools API is the most mature: built‑in function calling, auto‑RAG, file‑level plans, beta agentic retries, hundreds of SDK wrappers.
187 | • Anthropic exposes clean JSON tool‑use with schema‑by‑example, but lacks higher‑order agent features (no planner/executor modules, no retrieval primitives).
188 | • Google’s Vertex AI Agents & Extensions are promising (can invoke Google Search, Gmail, Drive, etc.) but APIs still in preview and less documented.
189 |
190 | FACTOR 3 — Cost
191 | (List is for “rough GPT‑4‑equivalent quality, May 2025 price sheets, 1K‑token prompt+completion, on‑demand)
192 | • Claude 3.5 Sonnet —— $3.00 (input $2.00, output $1.00)
193 | • GPT‑4o‑mini —— $3.20
194 | • GPT‑4o (full) —— $5.00
195 | • Gemini 2.5 Pro —— $4.20 (Vertex pay‑as‑you‑go, before sustained‑use discounts)
196 |
197 | Fixed commitments, reserved‑capacity and committed‑use discounts can bring all three within 10 – 15 % of each other, but Anthropic retains the consistent low‑cost edge.
198 |
199 | ────────────────────────────────
200 | 2. SIMPLE SCORING MATRIX
201 | ────────────────────────────────
202 | Weights chosen: Performance 45 %, Tool‑use 35 %, Cost 20 %
203 |
204 | Perf (45) Tools (35) Cost (20) Weighted
205 | OpenAI 9 10 6 8.7
206 | Anthropic 8 7 9 7.7
207 | Google 8 8 7 7.9
208 |
209 | (Score 1‑10, higher is better. Sensitivity check: If you up‑weight Cost to 40 %, Anthropic wins; if you up‑weight Context‑length or on‑prem‑friendly, Google can edge ahead.)
210 |
211 | ────────────────────────────────
212 | 3. RISK & STRATEGIC CONSIDERATIONS
213 | ────────────────────────────────
214 | Vendor Lock‑in
215 | • OpenAI = Azure only (unless you self‑host smaller open‑weights—which defeats the purpose).
216 | • Anthropic = AWS primary, GCP secondary; less rigid, but still contractual minimums.
217 | • Google = GP/TPU first‑party; Gemma open weights give a credible exit hatch.
218 |
219 | Governance / Corporate Stability
220 | • Alphabet is public, transparent, Sarbanes‑Oxley‑level reporting.
221 | • Anthropic has a single‑share “long‑term benefit trust” and a safety board, but Amodei siblings firmly in control.
222 | • OpenAI’s capped‑profit / non‑profit hybrid is unique; last November’s board drama shows governance risk, but Microsoft’s observer seat adds adult supervision.
223 |
224 | Capex & Compute Security
225 | • Google owns the fabs and TPUs → least likely to hit supply constraints.
226 | • Microsoft fronts multi‑billion‑dollar Azure clusters for OpenAI; so far, delivery has kept pace.
227 | • Anthropic rents from AWS & GCP; anything longer than 3‑year horizons depends on partners’ roadmap.
228 |
229 | ────────────────────────────────
230 | 4. HOW TO DERISK A “ONE‑HORSE” BET
231 | ────────────────────────────────
232 | 1. Contract for a three‑year spend floor but keep 20 % budget for a secondary provider.
233 | 2. Architect with an abstraction layer (LangChain, Semantic‑Kernel, or your own) so that swapping LLM endpoints is <2 weeks work.
234 | 3. Maintain an internal eval harness; run weekly quality/compliance tests across at least two providers.
235 | 4. Negotiate an “annual price step‑down clause” tied to hardware cost curves.
236 |
237 | ────────────────────────────────
238 | BOTTOM‑LINE GUIDANCE BY PROFILE
239 | ────────────────────────────────
240 | • You’re building consumer‑facing, vision‑heavy, agentic features, want fastest feature velocity → Bet OpenAI.
241 | • You’re doing enterprise knowledge work with 10‑100× token volume, heavily regulated, cost‑sensitive → Bet Anthropic.
242 | • You need extreme context windows, tight Workspace integration, or sovereign/on‑prem control → Bet Google.
243 |
244 | If forced to choose exactly ONE for the next three years, I lean 60/40 in favor of OpenAI for most green‑field, innovation‑led projects—while keeping a migration strategy alive, because in generative AI the “leader” position flips roughly every 12‑18 months.</response>
245 | </board-response>
246 |
247 | <board-response>
248 | <model-name>openai:o4-mini:high</model-name>
249 | <response>Here’s a focused comparison of OpenAI, Anthropic and Google along your three axes—Model Performance, Model Tool Use and Model Cost—plus a bottom‑line “which to pick” based on different priorities.
250 |
251 | 1. Model Performance (Raw Intelligence)
252 |
• OpenAI (o3 / GPT‑4o): leads most public benchmarks, best vision‑reasoning, continuous frontier releases.
253 | • Google (Gemini 2.5 Pro): at parity on reasoning & code benchmarks, unrivaled context windows (1M→2M tokens soon).
254 | • Anthropic (Claude 3.5 Sonnet): very strong in free‑form reasoning, matches or beats GPT‑4‑Turbo in text tasks but lags on vision/speech.
255 |
256 | 2. Model Tool Use (Ability to orchestrate APIs, plug‑ins, agents)
257 |
• OpenAI: richest ecosystem—Assistants API with built‑in tool discovery, function‑calls, vision+generation APIs out of the box.
258 | • Anthropic: clean, safety‑centric JSON tool schema; coming tooling ecosystem but fewer first‑party connectors (no vision yet).
259 | • Google: Vertex AI + AI Studio pipelines, good SDKs and open‑weight Gemma for on‑prem, but less mature “agent” layer than OpenAI.
260 |
261 | 3. Model Cost (Price / Performance at scale)
262 |
• Anthropic (Sonnet tier): cheapest per token for GPT‑4‑level quality today.
263 | • Google (Vertex discounts & Gemma open models): aggressive pricing and on‑device options with Gemma 3.
264 | • OpenAI: steadily falling prices, but top‑end O‑series still carries a premium vs Sonnet/Gemini mid‑tiers.
265 |
266 | Summary “Bet” Recommendations
267 |
• If you care most about **bleeding‑edge capabilities + seamless, production‑ready tool/agent support**, lean into **OpenAI**. You get top scores, the largest third‑party connector ecosystem and Microsoft’s enterprise muscle—at a premium price.
268 |
• If **unit economics** (cost‑performance) is your #1 driver and you value a safety‑first alignment ethos, **Anthropic** is the sweet spot. You give up some multimodal/speech features but gain the lowest cost for GPT‑4‑class chat and clean tool integration.
269 |
• If you prize **distribution scale, open‑weight fallbacks and full control over compute**, **Google** stands out. You’ll trade a slightly slower release cadence and less “agent magic” for unrivaled throughput (TPUs + 1M+ token contexts), built‑in Workspace/Android reach and on‑prem options.
270 |
271 | All three are competitive on raw intelligence. Your choice really comes down to your biggest lever:
272 | – Performance & tooling ⇒ OpenAI
273 | – Cost‑performance & alignment ⇒ Anthropic
274 | – Distribution & compute sovereignty ⇒ Google
275 |
276 | Whichever you pick, pilot a real workload (with rate limits, enterprise features, support SLAs) before you commit multi‑year spend. This space is evolving so rapidly that today’s “win” can shift next quarter.</response>
277 | </board-response>
278 |
279 | <board-response>
280 | <model-name>anthropic:claude-3-7-sonnet-20250219:4k</model-name>
281 | <response># Comparing OpenAI, Anthropic, and Google for Your Major Investment
282 |
283 | Based on your three key factors, I'll help you evaluate which AI company might best deserve your significant investment of time, money, and resources.
284 |
285 | ## Factor 1: Model Performance (Raw Intelligence)
286 |
287 | **OpenAI**: Currently leads with o3 and GPT-4o models, demonstrating superior reasoning capabilities and consistent top benchmark scores. Their frontier research pipeline maintains a performance edge.
288 |
289 | **Anthropic**: Claude 3.5 Sonnet is competitive, with Claude 3 Opus showing excellent long-form reasoning and 200k context window support. While impressive, they typically follow OpenAI's innovations.
290 |
291 | **Google**: Gemini 2.5 Pro leads specifically in math and code benchmarks with native 1M token context (soon 2M). Their models excel in certain specialized domains but lack OpenAI's consistent lead across all dimensions.
292 |
293 | **Edge**: OpenAI, though the gap has narrowed considerably in 2025.
294 |
295 | ## Factor 2: Model Tool Use
296 |
297 | **OpenAI**: Most mature with native tool-use API, Assistants & Tools API for agent orchestration, and a universal function-calling schema. Their ecosystem encourages extensive third-party integration.
298 |
299 | **Anthropic**: Has an elegant JSON-based tool-use schema but lacks first-party vision capabilities. Their approach is solid but more limited in scope.
300 |
301 | **Google**: Good tool integration within their ecosystem, particularly with Workspace AI and Workspace Flows. They offer a blend of closed models and open weights (Gemma), providing flexibility.
302 |
303 | **Edge**: OpenAI has the richest tool ecosystem and integration capabilities.
304 |
305 | ## Factor 3: Model Cost
306 |
307 | **OpenAI**: Historically premium-priced, though they've implemented consistent quarterly price drops. GPT-4o is roughly 8× cheaper than previous generation models.
308 |
309 | **Anthropic**: Offers the most cost-effective options at the Sonnet tier, undercutting GPT-4-Turbo in many cases. Their mid-tier models (Claude 3 Haiku) are explicitly designed for cost efficiency.
310 |
311 | **Google**: Provides aggressive Vertex AI discounts and offers Gemma as open-weight models for on-premises deployment, potentially reducing costs for certain use cases.
312 |
313 | **Edge**: Anthropic for pure API cost efficiency, with Google offering compelling total cost of ownership advantages.
314 |
315 | ## Overall Assessment
316 |
317 | If you're prioritizing raw intelligence and cutting-edge capabilities, **OpenAI** would be your bet, though you'll pay a premium.
318 |
319 | If cost-efficiency with very competitive performance is your priority, **Anthropic** offers the best value, especially with Claude 3.5 Sonnet.
320 |
321 | If you need deep integration with existing productivity tools, desire open-weight options, or want the backing of the most financially stable company, **Google** is your best choice.
322 |
323 | ## Recommendation
324 |
325 | Based on your three factors weighted equally, I would recommend **Anthropic** as your primary bet, as they offer the best balance of performance and cost, while maintaining competitive tool use capabilities. Their Constitutional AI approach also suggests a more stable and responsible trajectory.
326 |
327 | However, if raw performance and extensive tool integration outweigh cost considerations for your specific needs, OpenAI would be the better choice despite the higher costs.</response>
328 | </board-response>
329 |
330 | <board-response>
331 | <model-name>gemini:gemini-2.5-pro-preview-03-25</model-name>
332 | <response>Okay, let's break down this massive decision based *strictly* on the information provided and your three key factors: Model Performance (Raw Intelligence), Model Tool Use, and Model Cost.
333 |
334 | **Analysis based on your factors:**
335 |
336 | 1. **Model Performance (Raw Intelligence):**
337 | * **OpenAI:** Holds the edge with the "highest benchmark scores" noted in its pros. The new `o3` model is specifically "reasoning-optimized," and `GPT-4o` offers strong unified text-image capabilities. They consistently push the frontier.
338 | * **Anthropic:** `Claude 3.5 Sonnet` is a strong contender, explicitly noted as outperforming their previous top model (Opus) and beating `GPT-4-Turbo` in many tasks. However, the lack of "first-party vision yet" is a gap compared to OpenAI and Google's multimodal capabilities mentioned.
339 | * **Google:** `Gemini 2.5 Pro` leads specific benchmarks (math/code) and offers unparalleled native context length (1M-2M tokens), which is a form of raw capability. `Gemini 1.5` series also offers high-context multimodal performance. The summary table notes parity "on many tasks."
340 |
341 | * **Conclusion (Performance):** All three are extremely competitive at the frontier.
342 | * OpenAI likely has a slight edge in *general* benchmark performance and multimodal reasoning (vision).
343 | * Google excels in specific areas like *math/code* and *extreme context length*.
344 | * Anthropic offers very strong *text-based* reasoning, competitive with OpenAI's flagship tiers, but currently lags in native multimodality (vision).
345 | * **Winner (slight edge): OpenAI**, due to perceived overall benchmark leadership and strong multimodal features. Google is very close, especially if context length or specific code/math tasks are paramount.
346 |
347 | 2. **Model Tool Use (Ability to use tools):**
348 | * **OpenAI:** This seems to be a major focus. `o3` has a "native tool-use API". The "Assistants & Tools API" provides an "agent-style orchestration layer" with a "universal function-calling schema". This suggests a mature, dedicated framework for building applications that use tools.
349 | * **Anthropic:** Possesses an "elegant tool-use schema (JSON)". This implies capability, but the description lacks the emphasis on a dedicated orchestration layer or specific agentic framework seen with OpenAI.
350 | * **Google:** Tool use is integrated into products like `Workspace Flows` (no-code automation) and `Gemini Code Assist`. This shows strong *product-level* integration. While Vertex AI likely supports tool use via API, OpenAI's dedicated "Assistants API" seems more explicitly designed for developers building complex tool-using agents from scratch.
351 |
352 | * **Conclusion (Tool Use):**
353 | * OpenAI appears to offer the most *developer-centric, flexible, and mature API framework* specifically for building complex applications involving tool use (Assistants API).
354 | * Google excels at *integrating* tool use into its existing products (Workspace, IDEs).
355 | * Anthropic provides the capability but seems less emphasized as a distinct product/framework layer compared to OpenAI.
356 | * **Winner: OpenAI**, for building sophisticated, custom agentic systems via API. Google wins if the goal is leveraging tool use *within* Google's ecosystem products.
357 |
358 | 3. **Model Cost (Cost of the model):**
359 | * **OpenAI:** Actively working on cost reduction (`o3` is ~8x cheaper than GPT-4-Turbo, `4o-mini` targets low cost). However, it still carries a "price premium at the very top end," and the summary table rates its cost-performance as "improving" (🟠).
360 | * **Anthropic:** `Claude 3.5 Sonnet` offers double the speed of Opus (implying better efficiency/cost) and is highlighted as the "cheapest at Sonnet tier" (🟢). It explicitly "beats GPT-4-Turbo in many tasks" while being cost-competitive.
361 | * **Google:** `Gemini 1.5 Flash` is noted for efficiency. Vertex AI offers "aggressive discounts" (🟢). AI Studio provides a free tier.
362 |
363 | * **Conclusion (Cost):**
364 | * Anthropic and Google are explicitly positioned as having a cost advantage over OpenAI, particularly at the highly capable mid-to-flagship tiers (Sonnet vs. GPT-4 level, Gemini via Vertex discounts).
365 | * OpenAI is getting cheaper but may still be the most expensive for absolute top-tier performance.
366 | * **Winner (Tie): Anthropic & Google**, both offer compelling cost-performance, potentially undercutting OpenAI for similar capability levels below the absolute bleeding edge.
367 |
368 | **Decision Framework based *only* on these factors:**
369 |
370 | * **Bet on OpenAI IF:**
371 | * Your primary driver is accessing the absolute highest raw intelligence and broadest capabilities (including vision) as soon as they are available.
372 | * You need the most mature and flexible developer API for building complex, custom applications that heavily rely on **tool use / agentic behavior**.
373 | * You can tolerate potentially **higher costs** for top-tier performance, betting that their price cuts will continue aggressively.
374 |
375 | * **Bet on Anthropic IF:**
376 | * Your primary driver is achieving an optimal **balance between high performance (especially text reasoning) and cost**. Claude 3.5 Sonnet offers leading performance in its tier at a very competitive price.
377 | * You need solid **tool use** capabilities via a clean API, but perhaps don't require the elaborate orchestration layer of OpenAI's Assistants *yet*.
378 | * Current lack of native vision capabilities is acceptable for your core use cases.
379 | * **Cost-efficiency** at scale for high-quality models is a critical factor.
380 |
381 | * **Bet on Google IF:**
382 | * Your performance needs align with their strengths: **math/code reasoning or massive context window** requirements.
383 | * **Cost-performance** is crucial, and you can leverage Vertex AI's aggressive discounts or need a strong free tier (AI Studio).
384 | * Your strategy involves leveraging **tool use integrated within existing ecosystems** (Workspace, IDEs) OR you value their underlying infrastructure strength (TPUs).
385 | * You need a balance of strong performance, good cost, and solid tool integration, potentially without needing OpenAI's most advanced (and possibly complex) Assistants API.
386 |
387 | **Recommendation Synthesis:**
388 |
389 | Based *purely* on your three factors:
390 |
391 | 1. **Performance:** OpenAI (slight edge overall), Google (specific strengths).
392 | 2. **Tool Use API:** OpenAI (most mature developer framework).
393 | 3. **Cost:** Anthropic & Google (better cost-performance).
394 |
395 | * If **Tool Use maturity via API** is your *most* critical factor alongside top performance, **OpenAI** is the likely bet, despite the cost premium.
396 | * If **Cost-Performance** is your *most* critical factor for high-quality models, **Anthropic** looks very strong, provided you don't immediately need cutting-edge vision.
397 | * If you need Google's specific performance strengths (context, math/code) or value **integrated tool use** and competitive **cost**, **Google** is the bet.
398 |
399 | This is a tight race. Your specific weighting of these three factors will determine the best path. Given the "massive bet" nature, consider which factor represents the biggest *risk* if the chosen provider underperforms.</response>
400 | </board-response>
401 |
402 | <board-response>
403 | <model-name>gemini:gemini-2.5-flash-preview-04-17</model-name>
404 | <response>Okay, this is a significant decision requiring careful analysis based on the information provided. Let's break down the three companies based on your stated factors: Model Performance, Model Tool Use, and Model Cost, leveraging the details you've supplied.
405 |
406 | **Analysis by Factor**
407 |
408 | 1. **Model Performance (Raw Intelligence)**
409 |
410 | * **OpenAI:** The provided text explicitly states "Highest benchmark scores" and lists "o3" (reasoning-optimized) and "GPT-4o" as the "top" models for Q2 2025. Their strength is presented as being at the bleeding edge of raw capability, vision-reasoning, and rapid iteration.
411 | * **Google:** Gemini 2.5 Pro is described as leading in "math/code benchmarks" and offering unmatched context length (1M+, soon 2M), being "at parity on many tasks" with frontier models. While not claiming *overall* highest benchmarks, it leads in specific, crucial areas (logic, coding, massive context).
412 | * **Anthropic:** Claude 3.5 Sonnet "outperforms Claude 3 Opus" and is a "fast follower". Claude 3 Opus is noted for "Long-form reasoning" and 200k context. They are highly competitive and often beat older flagship models from competitors, excelling particularly in long-form text coherence.
413 |
414 | * **Ranking for Performance (Based on text):** This is incredibly close at the frontier. OpenAI claims the "highest benchmark scores" overall, while Google leads in specific critical areas (math/code) and context length, and Anthropic excels in long-form reasoning and is a strong fast follower.
415 | 1. **OpenAI / Google (Tie):** Depending on whether you need bleeding-edge *general* benchmarks (OpenAI) or specific strengths like *massive context* and *code/math* (Google), these two are presented as the frontier leaders.
416 | 2. **Anthropic:** A very strong "fast follower," competitive on many tasks and potentially best for specific use cases like lengthy, coherent text generation.
417 |
418 | 2. **Model Tool Use (Ability to use tools)**
419 |
420 | * **OpenAI:** The text heavily emphasizes "Native tool-use API," "Assistants & Tools API – agent-style orchestration layer," and a "universal function-calling schema." The table explicitly calls out "richest (assistants, tools)" ecosystem. This is presented as a core strength and dedicated focus.
421 | * **Anthropic:** Mentions an "Elegant tool-use schema (JSON)." The table notes it as "clean, safety-first." This indicates capability but is less detailed or emphasized compared to OpenAI's description of its stack.
422 | * **Google:** The text mentions product features like Workspace AI "Help me..." and Workspace Flows, which *use* AI behind the scenes but aren't strictly about the *model's* API-based tool use. It notes AI Studio/Vertex AI which *do* offer function calling (standard in LLM platforms), but the *description* doesn't position tool use as a core *model or system* advantage in the same way OpenAI's "Assistants" framework is highlighted.
423 |
424 | * **Ranking for Tool Use (Based on text):** OpenAI is presented as the clear leader with a dedicated system (Assistants) and explicit focus on tool-use APIs.
425 | 1. **OpenAI:** Most mature and feature-rich dedicated tool-use/agent framework described.
426 | 2. **Anthropic:** Has a noted schema, indicating capability.
427 | 3. **Google:** Has underlying platform capability (Vertex AI) and integrated product features, but the provided text doesn't highlight the *model's* tool use API capabilities as a key differentiator like OpenAI does.
428 |
429 | 3. **Model Cost (Cost of the model)**
430 |
431 | * **OpenAI:** Notes "Ongoing price drops every quarter," cheaper models like 4o-mini and o3 (~8x cheaper inference than GPT-4-Turbo). However, the table also states "Price premium at the very top end remains high." They are getting more competitive but aren't presented as the cheapest at the highest tiers.
432 | * **Anthropic:** Claude 3 Haiku is "cheap," and Claude 3.5 Sonnet offers "Competitive price/perf," explicitly stating it "beats GPT-4-Turbo in many tasks" and the table calls it "cheapest at Sonnet tier." This suggests a strong price advantage at a highly capable tier.
433 | * **Google:** Notes "aggressive Vertex discounts" and a free tier (AI Studio). The table confirms "🟢 aggressive Vertex discounts." This indicates they are pricing competitively, especially at scale via their cloud platform.
434 |
435 | * **Ranking for Cost (Based on text):** Anthropic and Google are presented as offering better cost-efficiency, either through specific model tiers or platform pricing.
436 | 1. **Anthropic / Google (Tie):** Anthropic seems to have a strong claim on price/perf at a specific high-value tier (Sonnet), while Google offers aggressive discounts via its platform, making both potentially more cost-effective than OpenAI's top models.
437 | 2. **OpenAI:** Improving, but still has a premium at the highest-performance end.
438 |
439 | **Synthesized Recommendation Based on Your Factors**
440 |
441 | Based *solely* on the information provided and weighting your three factors:
442 |
443 | * If **Model Performance** and **Model Tool Use** are the absolute highest priorities, even at a higher cost, then **OpenAI** appears to be the strongest contender based on the provided text. It's presented as the leader in overall benchmarks and has the most developed tool-use/agent ecosystem.
444 | * If **Model Performance** (especially context length, math/code) and **Model Cost** via enterprise discounts are the highest priorities, and you value owning the infrastructure stack (TPUs) and vast existing distribution channels, then **Google** is a very strong option. It matches OpenAI on frontier performance in key areas and is positioned as more cost-effective and less vendor-locked (vs Azure/AWS).
445 | * If **Model Cost-Performance** (specifically at a high-quality tier like Sonnet) and **Performance** (strong long-form reasoning, competitive benchmarks) are paramount, and you prioritize safety/alignment principles ("Constitutional AI"), then **Anthropic** is the strongest candidate. You sacrifice some tool-use maturity (compared to OpenAI) and breadth (no vision), but gain significant cost efficiency and a strong safety story.
446 |
447 | **Considering the "Massive Bet" Context**
448 |
449 | A "massive bet" implies long-term commitment, integration depth, and reliance on the partner's stability and roadmap. While not your primary factors, the "How to think about a 'massive bet'" section provides crucial context:
450 |
451 | * **Ecosystem/APIs:** OpenAI is called "richest," Google is "broad + open weights." This reinforces OpenAI's lead in developer tools (Assistants) but highlights Google's unique offering of open models.
452 | * **Compute Independence:** Google owns TPUs, reducing reliance on specific cloud vendors (though Vertex AI *is* Google Cloud). OpenAI is tied to Azure, Anthropic to AWS/GCP. Google has an edge here for long-term infra control.
453 | * **Governance/Stability:** OpenAI has a history of turmoil. Anthropic is presented as stable/safety-focused. Google has bureaucracy. This is a risk/preference trade-off. OpenAI's history is a notable risk for a "massive bet."
454 | * **Distribution:** Google is unmatched (billions of users). This is a huge asset if your "bet" involves reaching consumers or leveraging existing enterprise relationships (Workspace).
455 |
456 | **Conclusion**
457 |
458 | Given your stated factors:
459 |
460 | * **OpenAI:** Leads on Tool Use, competitive on Performance, lags on Cost (at the top end). Strong ecosystem, but governance and vendor lock-in are cons for a massive bet.
461 | * **Anthropic:** Leads on Cost-Performance (at Sonnet tier), strong on Performance (fast follower, long context), lags on Tool Use maturity and product surface. Strong safety story, stable governance, but smaller compute/distribution.
462 | * **Google:** Competitive on Performance (context, specific strengths), competitive on Cost (aggressive discounts), decent on Tool Use (via platform). Unmatched distribution, owns infrastructure, offers open models. Cons: potential slower cadence, bureaucracy, Search conflict of interest.
463 |
464 | If I had to pick ONE based on balancing your three factors and the "massive bet" context from the text:
465 |
466 | * If **Tool Use / Agentic capabilities** are the absolute most critical and you need the most advanced features *right now*, lean towards **OpenAI**, acknowledging the cost premium and governance risks.
467 | * If **massive context, cost efficiency at scale, infrastructure control, and leveraging existing enterprise/consumer distribution** are key, lean towards **Google**. They are highly competitive on performance and cost, own their stack, and have unparalleled reach.
468 | * If **cost-performance (specifically at a high tier), safety/alignment, and long-form text quality** are paramount, lean towards **Anthropic**, accepting the current narrower scope.
469 |
470 | Considering the information implies Google is "at parity on many tasks" with OpenAI on performance and offers competitive cost ("aggressive Vertex discounts"), *and* has advantages in infrastructure and distribution not found at the others, **Google** appears to offer the most balanced profile for a "massive bet" that requires high performance, competitive cost, and infrastructure stability/scale, despite the notes on cadence and bureaucracy. OpenAI is arguably ahead on *dedicated* tool-use features *currently*, but Google's overall platform, infra, and distribution might de-risk a "massive bet" more effectively long-term if performance and cost are competitive.
471 |
472 | However, the note about Google's "Ship cadence historically slower" is a direct counterpoint to OpenAI's "feature cadence," which is also crucial for a long-term bet in this fast-moving field.
473 |
474 | Ultimately:
475 | * **OpenAI** for current frontier capabilities and the richest agent ecosystem, accepting higher cost and higher risk profile.
476 | * **Google** for long-term scale, infrastructure control, cost competitiveness at scale, and distribution, accepting potentially slower feature evolution.
477 | * **Anthropic** for best cost/performance at a key tier and a strong safety narrative, accepting narrower product scope and reliance on cloud partners.
478 |
479 | Based *strictly* on the factors and the resources' presentation of balancing performance, cost, and the "massive bet" context (especially infra/distribution), **Google** seems to present a slightly more robust *platform* for a "massive bet" at scale, assuming their performance continues to be competitive, while **OpenAI** is the choice for bleeding-edge *model/tool* features right now.
480 |
481 | As the resource states, piloting is essential before committing multi-year spend.</response>
482 | </board-response>
483 |
484 | </board-decisions>
485 |
```