reasoning benchmark
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.
Updated Aug 11, 2026
Models6
Model coverage6
MetricScore
EvidenceB
Multi-SWE-Bench Ranking
Higher score ranks better on this benchmark.
| 1 | MI | 52.7% | 100.0% | 6 | C | |
| 2 | MI | 51.3% | 80.0% | 6 | C | |
| 3 | MI | 49.4% | 60.0% | 6 | C | |
| 4 | MA | 41.9% | 40.0% | 6 | C | |
| 5 | MI | 36.2% | 20.0% | 6 | C | |
| 6 | AC | 25.8% | 0.0% | 6 | C |
Multi-SWE-Bench Score Distribution
A closer view of the leading scores on this benchmark.
Multi-SWE-Bench Highlights
The leading models and scores on this benchmark.
What is Multi-SWE-Bench?
What Multi-SWE-Bench measures and how its scores work.
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.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- Multi-SWE-Bench
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- multi-swe-bench|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about Multi-SWE-Bench.
Which model scores highest on Multi-SWE-Bench?
MiniMax M2.7 is currently ranked first with 52.7%.
What does Multi-SWE-Bench measure?
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.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
6 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.