The Test Nobody Wanted Me to Run
I gave Claude Code, Cursor, and GitHub Copilot the same 10 real-world coding tasks and measured how many lines they got right on the first try. Claude Code won 7/10, but the how matters more than the score.
This isn't about which tool autocompletes faster or has better UX. I'm measuring accuracy on context-heavy tasks — the kind where you need to understand existing code, follow project conventions, and avoid introducing bugs. The kind that actually saves time instead of creating cleanup work.
I logged every suggestion, counted how many lines I had to rewrite, and tracked how often each tool hallucinated imports or broke existing logic. Here's what 40 hours of side-by-side testing taught me.
The 10-Task Gauntlet
I designed tasks that require codebase understanding, not just autocomplete:
- Add error handling to an async API client (existing codebase, 200 lines)
- Refactor a 15-line function to use pattern matching (Python 3.10+)
- Write unit tests for a class with 3 edge cases I described
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