AI benchmarks and leaderboards are different things
Learn the difference between a benchmark, an evaluation run and a leaderboard, and use each to answer the right question.
LLM Stats
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.
Evaluation context and evidence
- Source
- LLM Stats
- Publisher
- Not recorded
- Benchmark release
- Not recorded
- Collected
- 2026-08-17
- Modality
- text
These are source-reported observations, not independent reruns by this site. Check the task version, evaluation protocol, unit and reporting date before comparing models. Results from different setups are not a universal ranking.
Original sources
Reported results 6
Shown in source observation order, not ranked. Dates retain the precision provided by the source.
| Model | Value | Unit | Protocol | Reported date and basis | Source |
|---|---|---|---|---|---|
| MiniMax M2.7 | 0.527 | Not recorded | Not recorded | 2026-03-18 · model_announcement | LLM Stats |
| MiniMax M2.5 | 0.513 | Not recorded | Not recorded | 2026-02-12 · model_announcement | LLM Stats |
| MiniMax M2.1 | 0.494 | Not recorded | Not recorded | 2025-12-23 · model_announcement | LLM Stats |
| Kimi K2-Thinking-0905 | 0.419 | Not recorded | Not recorded | 2025-09-05 · model_announcement | LLM Stats |
| MiniMax M2 | 0.362 | Not recorded | Not recorded | 2025-10-27 · model_announcement | LLM Stats |
| Qwen3-Coder 480B A35B Instruct | 0.258 | Not recorded | Not recorded | 2025-01-31 · model_announcement | LLM Stats |