Unverified60% confidenceBenchmarkExact time
SWE-Bench was proposed by the Princeton NLP team in 2023, crawling 2,294 real Issue-PR pairs from GitHub and covering 12 mainstream Python projects.
2
Sources
60%
Confidence
Long-term
Relevance
7/13/2026
First Seen
Sources
Ornith 1.0 九B模型本地实测：塔防游戏暴露小模型编程瓶颈
Related Claims
VerifiedSWE-bench was released in 2023 by a research team at Princeton University, collecting 2,294 real GitHub Issues and their corresponding Pull Request fixes from 12 well-known Python open-source projects.84% similarUnverifiedSWE-Bench由普林斯顿大学团队发布，从GitHub上12个真实Python开源项目抽取近2300个真实Issue，2024年初顶级模型通过率仅约4%-5%，至2025年头部模型已突破60%83% similarVerifiedSWE-Bench collected 2,294 real Issue–Pull Request pairs from 12 mainstream Python open-source projects on GitHub.81% similarUnverifiedSWE-bench由普林斯顿大学团队提出，包含2294个来自Django、Flask、Scikit-learn等12个Python开源仓库的真实GitHub Issue-Pull Request对80% similar
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