Coding
SWE-bench Multilingual Leaderboard
SWE-bench Multilingual extends SWE-bench beyond Python to real GitHub issues across many programming languages, measuring whether a model can fix bugs in codebases written in Java, Go, Rust, TypeScript and more.
Source: swebench4 open models ranked+10 proprietaryData through Feb 2026
Open models ranked on SWE-bench Multilingual
# shows rank among open models / rank overall (including proprietary).
| # | Model | Score |
|---|---|---|
| 1 / 4 | GLM 5 · 753.9B | 69.7% |
| 2 / 6 | MiniMax M2.5 · 228.7B | 68.3% |
| 3 / 7 | Kimi K2.5 · 1058.6B | 67.3% |
| 4 / 13 | DeepSeek V3.2 · 685.4B | 59.0% |
SWE-bench Multilingual: frequently asked questions
- What is the best open LLM on SWE-bench Multilingual?
- GLM 5 is the top open model on SWE-bench Multilingual, scoring 69.7%. Among all models tested — including proprietary ones — it ranks #4. The top model overall is Gemini 3 Flash (Google) at 72.7%.
- Can open models match proprietary models on SWE-bench Multilingual?
- Not quite on SWE-bench Multilingual: the strongest proprietary model (Gemini 3 Flash) scores 72.7%, ahead of the best open model (GLM 5) at 69.7% — but you can run the open one yourself.
Scores aggregated from swebench. llmrun does not run this benchmark — see the source for methodology, or the about benchmarks for what it measures.