Anthropic ships Claude Fable 5.1 and cuts cache reads by 75%
The two models are the same system under different safeguard regimes. The pricing change, not the benchmark table, is what will show up in developers' bills.
Benchmark
SWE-bench Verified
The share of real GitHub issues from open-source Python repositories that a model resolves with a patch passing the project's own tests, on a human-validated subset of 500 tasks.
- Maintainer
- OpenAI and the SWE-bench team
- Score
- %
- Last checked
- September 2, 2026
Leaderboard
No verified results recorded yet.
Methodology
A model receives an issue description and the repository, and must produce a patch. The patch is applied and the repository's existing test suite is run; a task counts as resolved only when the relevant tests pass. The Verified subset was human-reviewed to remove tasks that were unsolvable or ambiguously specified.
Important limitations
Coverage is limited to Python repositories present in the dataset, so results generalise imperfectly to other languages and codebases. Scaffolding, tool access and retry budget vary between submissions and materially change scores, so two numbers are only comparable when the harness is comparable.