SWE-bench Pro
About
731-task multilingual real-world GitHub issue benchmark extending SWE-bench Verified with harder, more diverse tasks across Python, JavaScript, TypeScript, Java, Go, C++, and Rust.
How to read this benchmark
This benchmark uses a scoring system where higher is better. Scores are useful for directional filtering and model shortlisting, not for universal quality ranking.
Interpretation checklist: prefer benchmarks that are closest to your workload style, then validate against the linked model pages for pricing, context window, and provider availability.
Trust when:
- There is a fresh timestamped snapshot (or multiple snapshots) for this benchmark.
- Model list covers the same version family you can actually deploy today.
- Top candidates overlap with your required routing and feature requirements.
Don't trust when:
- There is only one benchmark snapshot or the dataset appears stale.
- Benchmark metric direction is opposite of your decision objective.
- The score difference between options is narrow and likely within implementation variance.
Current modeled score band for tracked entries is roughly 69.2 – 58.6.
Window
May 20 to May 29
last 3 snapshots
Mean delta
-4.30
score points
Coverage
1
models in latest snapshot
Leaderboard preview (top 5)
- 1. Claude Opus 4.869.2
- 2. Claude Opus 4.764.3
- 3. Qwen3.7-Max60.6
- 4. GPT-5.558.6
- 5. Kimi K2.658.6