Test requiring agents to read the README.md file and extract the first and last 10 lines, evaluating file reading capabilities and instruction following.
| Model | Response |
|---|---|
| gpt-5-nano | FIRST 10 LINES: # Nano Agent A production-ready MCP (Model Context Protocol) server implementation for autonomous engineering agents with multi-provider LLM support. ## Installation ### Quick Install (Recommended) ```bash # Clone the repository LAST 10 LINES: - ai_docs/ contains comprehensive guides for:- Building MCP servers with Python and UV - OpenAI Agents SDK implementation patterns - GPT-5 and GPT-OSS model specifications - Anthropic OpenAI compatibility documentation - Ollama OpenAI compatibility documentation ## License MIT |
| gpt-5-mini | FIRST 10 LINES: # Nano Agent A production-ready MCP (Model Context Protocol) server implementation for autonomous engineering agents with multi-provider LLM support. ## Installation ### Quick Install (Recommended) ```bash # Clone the repository LAST 10 LINES: - ai_docs/ contains comprehensive guides for:- Building MCP servers with Python and UV - OpenAI Agents SDK implementation patterns - GPT-5 and GPT-OSS model specifications - Anthropic OpenAI compatibility documentation - Ollama OpenAI compatibility documentation ## License MIT |
| gpt-5 | FIRST 10 LINES: # Nano Agent A production-ready MCP (Model Context Protocol) server implementation for autonomous engineering agents with multi-provider LLM support. ## Installation ### Quick Install (Recommended) ```bash # Clone the repository LAST 10 LINES: - ai_docs/ contains comprehensive guides for:- Building MCP servers with Python and UV - OpenAI Agents SDK implementation patterns - GPT-5 and GPT-OSS model specifications - Anthropic OpenAI compatibility documentation - Ollama OpenAI compatibility documentation ## License MIT |
| claude-opus-4-1 | Now I'll provide the first and last 10 lines as requested: FIRST 10 LINES: # Nano Agent A production-ready MCP (Model Context Protocol) server implementation for autonomous engineering agents with multi-provider LLM support. ## Installation ### Quick Install (Recommended) ```bash # Clone the repository LAST 10 LINES: ### Documentation - ai_docs/ contains comprehensive guides for:- Building MCP servers with Python and UV - OpenAI Agents SDK implementation patterns - GPT-5 and GPT-OSS model specifications - Anthropic OpenAI compatibility documentation - Ollama OpenAI compatibility documentation ## License MIT |
| claude-opus-4 | Now I'll extract the first and last 10 lines from the file content: FIRST 10 LINES: # Nano Agent A production-ready MCP (Model Context Protocol) server implementation for autonomous engineering agents with multi-provider LLM support. ## Installation ### Quick Install (Recommended) ```bash # Clone the repository LAST 10 LINES: ### Documentation - ai_docs/ contains comprehensive guides for:- Building MCP servers with Python and UV - OpenAI Agents SDK implementation patterns - GPT-5 and GPT-OSS model specifications - Anthropic OpenAI compatibility documentation - Ollama OpenAI compatibility documentation ## License MIT |
| claude-sonnet-4 | Now I'll extract the first and last 10 lines from the content: FIRST 10 LINES: <br># Nano Agent<br><br>A production-ready MCP (Model Context Protocol) server implementation for autonomous engineering agents with multi-provider LLM support.<br><br>## Installation<br><br>### Quick Install (Recommended)<br><br>bash# Clone the repository <br><br>LAST 10 LINES:<br>- ai_docs/ contains comprehensive guides for:- Building MCP servers with Python and UV - OpenAI Agents SDK implementation patterns - GPT-5 and GPT-OSS model specifications - Anthropic OpenAI compatibility documentation - Ollama OpenAI compatibility documentation ## License MIT ``` |
| claude-3-haiku | FIRST 10 LINES: # Nano Agent A production-ready MCP (Model Context Protocol) server implementation for autonomous engineering agents with multi-provider LLM support. ## Installation ### Quick Install (Recommended) bash<br># Clone the repository<br>git clone https://github.com/yourusername/nano-agent.git<br>cd nano-agent/apps/nano_agent_mcp_server<br><br># Run the installation script<br>./scripts/install.sh<br><br># Or install manually<br>uv tool install -e .<br>LAST 10 LINES: Important: Always use --extra test when you need to run tests, as uv sync alone will remove test dependencies.### Running the Server bash<br>cd apps/nano_agent_mcp_server<br>uv run python -m nano_agent<br>The server communicates via stdin/stdout using the MCP protocol. ## Nano Agent Architecture ### Nested Agent Hierarchy Key Concept: This is a nested agent system with two distinct agent layers. |
| gpt-oss-20b | FIRST 10 LINES:<br># Nano Agent<br><br>A production-ready MCP (Model Context Protocol) server implementation for autonomous engineering agents with multi-provider LLM support.<br><br>## Installation<br><br>### Quick Install (Recommended)<br>LAST 10 LINES: <br>### Development<br><br>### Adding a New App<br><br>1. Create a new directory under `apps/`<br>2. Initialize with UV: `uv init --lib`<br>3. Add your dependencies and code<br>4. Include tests in `apps/your_app/tests/`<br><br>### Documentation<br><br>- `ai_docs/` contains comprehensive guides for:<br> - Building MCP servers with Python and UV<br> - OpenAI Agents SDK implementation patterns<br> - GPT-5 and GPT-OSS model specifications<br> - Anthropic OpenAI compatibility documentation<br> - Ollama OpenAI compatibility documentation<br><br>## License<br><br>MIT<br> |
| gpt-oss-120b | FIRST 10 LINES: # Nano Agent A production-ready MCP (Model Context Protocol) server implementation for autonomous engineering agents with multi-provider LLM support. ## Installation ### Quick Install (Recommended) bash<br># Clone the repository<br>git clone https://github.com/yourusername/nano-agent.git<br>cd nano-agent/apps/nano_agent_mcp_server<br><br># Run the installation script<br>./scripts/install.sh<br><br># Or install manually<br>uv tool install -e .<br>LAST 10 LINES: ## License MIT |
| Model | Performance Grade | Speed Grade | Cost Grade | Overall Grade |
|---|---|---|---|---|
| gpt-5-nano | B | C | S | B |
| gpt-5-mini | A | D | B | B |
| gpt-5 | A | F | F | D |
| claude-opus-4-1 | C | B | E | D |
| claude-opus-4 | C | C | E | D |
| claude-sonnet-4 | D | B | D | C |
| claude-3-haiku | F | S | A | C |
| gpt-oss-20b | F | E | S | D |
| gpt-oss-120b | F | E | S | D |
| Model | Execution Time | Total Tokens | Total Cost | Input Tokens | Output Tokens |
|---|---|---|---|---|---|
| gpt-5-nano | 15.15s | 11850 | $0.002891 | 8647 | 3203 |
| gpt-5-mini | 17.29s | 9552 | $0.007300 | 8104 | 1448 |
| gpt-5 | 136.11s | 16869 | $0.173182 | 8637 | 8232 |
| claude-opus-4-1 | 9.21s | 9730 | $0.160770 | 9483 | 247 |
| claude-opus-4 | 13.83s | 9732 | $0.160920 | 9483 | 249 |
| claude-sonnet-4 | 5.73s | 9744 | $0.032268 | 9491 | 253 |
| claude-3-haiku | 3.25s | 9670 | $0.002717 | 9371 | 299 |
| gpt-oss-20b | 27.80s | 7588 | $0.000000 | 7357 | 231 |
| gpt-oss-120b | 75.56s | 7457 | $0.000000 | 7303 | 154 |
- Performance: Combines accuracy and conciseness of the response
- Speed: Relative ranking (fastest = S, slowest = worst grade)
- Cost: Relative ranking (cheapest = S, most expensive = worst grade)
- Overall: Weighted average considering all factors
- 1st Place: gpt-5-nano (Overall Grade: B)
- 1st Place: gpt-5-mini (Overall Grade: B)
- 3rd Place: claude-sonnet-4 (Overall Grade: C)
- 3rd Place: claude-3-haiku (Overall Grade: C)
- 5th Place: gpt-5 (Overall Grade: D)
- 5th Place: claude-opus-4-1 (Overall Grade: D)
- 5th Place: claude-opus-4 (Overall Grade: D)
- 5th Place: gpt-oss-20b (Overall Grade: D)
- 5th Place: gpt-oss-120b (Overall Grade: D)
- Accuracy Issues: Several models (claude-3-haiku, gpt-oss-20b, gpt-oss-120b) extracted incorrect lines, showing wrong content for either first or last 10 lines
- Speed Winner: claude-3-haiku at 3.25s despite accuracy issues
- Most Expensive: gpt-5 at $0.173182, followed by claude-opus models
- Format Variations: Some models added extra formatting (code blocks, explanatory text) instead of following the exact format requested