Multi-SWE-Bench
A multilingual benchmark for issue resolving that evaluates Large Language Models' ability to resolve software issues across diverse programming ecosystems. Covers 7 programming languages (Java, TypeScript, JavaScript, Go, Rust, C, and C++) with 1,632 high-quality instances carefully annotated by 68 expert annotators. Addresses limitations of existing benchmarks that focus almost exclusively on Python.
MiniMax M2.7 from MiniMax currently leads the Multi-SWE-Bench leaderboard with a score of 0.527 across 6 evaluated AI models.
MiniMax M2.7 leads with 52.7%, followed by MiniMax M2.5 at 51.3% and MiniMax M2.1 at 49.4%.
Progress Over Time
Interactive timeline showing model performance evolution on Multi-SWE-Bench
Multi-SWE-Bench Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  MiniMax | — | 205K | $0.30 / $1.20 | ||
| 2 | MiniMax | 230B | 1.0M | $0.30 / $1.20 | ||
| 3 | MiniMax | 230B | 1.0M | $0.30 / $1.20 | ||
| 4 |  Moonshot AI | 1.0T | — | — | ||
| 5 | MiniMax | 230B | 1.0M | $0.30 / $1.20 | ||
| 6 |  Alibaba Cloud / Qwen Team | 480B | — | — |
FAQ
Common questions about Multi-SWE-Bench.
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