Extension of SWE-bench to multiple programming languages beyond Python, testing real-world bug fixing across TypeScript, Java, Go, Rust and more.
为什么重要： Most real codebases are polyglot. This benchmark tests whether coding models can handle the diversity of languages seen in production software engineering.
顶级模型
73.7%
Composer 2
平均评分
73.7%
共1个模型
已测试模型
1
指标: resolved rate
人类基准
—
评分范围: 0%–100%
All models with a reported SWE-bench ML score, ranked by highest resolved rate.
SWE-bench ML is a standardized evaluation that measures AI model performance on specific tasks. It provides comparable scores across different models, helping developers choose the right model for their needs.
Composer 2 currently holds the top score on the SWE-bench ML benchmark. See our full rankings table above for the complete leaderboard with 1 models.
We update benchmark data from multiple sources including HuggingFace Open LLM Leaderboard and LMArena. Scores are refreshed regularly as new evaluations are published and new models are released.
No. While SWE-bench ML is an important indicator, real-world performance depends on many factors including pricing, latency, context window, and specific task requirements. We recommend using our composite score which weighs multiple benchmarks and practical factors.