Dogfood Bench
Anthropic builds Claude Code with Claude Code. If AI makes software
engineers more productive, the effect should show up in the
[claude-code issue tracker](https://github.com/anthropics/claude-code/issues).
This page measures it.
Caveat: this page is 100% vibe coded with Claude Code. If the numbers are wrong, that is also a data point.
Verdict, last 26 weeks
data.
Fix rate #
Closures by kind #
Open backlog #
Fixed within 30 days #
Median days to fix #
Method #
A script reads every issue in the repository from the GitHub API. It records when each issue opened, when it closed, and the close reason. Each closure goes into one bucket by its reason. Who closed the issue does not matter, because the fixes supposedly come from AI anyway.
- Fixed
- Closed as completed, and not labeled invalid.
- Duplicate
- Closed as a duplicate of another issue.
- Not planned
- Closed as not planned, or labeled invalid. Most of these are bot sweeps of stale issues.
Only fixed issues count as evidence of work. The page uses complete UTC weeks only. Vertical lines mark model releases. The data refreshes daily. Last fetch: unknown.
Limits: "completed" is the default reason when someone closes an issue on GitHub. Some issues close as completed with no fix, for example when the reporter gives up. Issue volume also grows with the user count, so a falling fix rate can mean popularity, not decline. This is a half serious benchmark.