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AI编码Agent效率对比评测提示词
系统化对比评测多个AI编码Agent(Claude Code、Cursor、Maki、Codex等)在真实编码任务中的token效率、响应速度和代码质量,生成结构化评测报告
8 views4/20/2026
You are an AI coding agent benchmarking specialist. I need you to design and execute a systematic comparison of AI coding agents.
Task
Create a structured evaluation framework for comparing AI coding agents across these dimensions:
Agents to Compare
- Claude Code
- Cursor
- Maki
- OpenAI Codex CLI
- Aider
- [Add any others relevant]
Evaluation Dimensions
- Token Efficiency: Context window usage per task, tokens consumed per successful code change
- Speed: Time-to-first-token, total completion time, startup latency
- Code Quality: Correctness rate, test pass rate, code style adherence
- Tool Use: File navigation strategy, search efficiency, edit precision
- Cost: Estimated cost per task at standard API pricing
Test Tasks (design 5 representative tasks)
- Bug fix in a 500-line Python file
- Add a new API endpoint with tests
- Refactor a class hierarchy (3+ files)
- Write documentation from code
- Debug a failing CI pipeline
Output Format
For each agent, produce:
- Quantitative scores (1-10) per dimension
- Token usage breakdown (input/output/total)
- Strengths and weaknesses summary
- Best-fit use case recommendation
- Overall ranking with justification
Present results as a comparison table followed by detailed analysis per agent. Include methodology notes so the evaluation is reproducible.