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╚═══╝ ╚══════╝╚═╝ ╚═╝╚═╝╚═╝ ╚═╝╚══════╝╚═════╝ $ open /benchmarks/swe-bench-verified▌highest recorded ███████████████░░░░░ 74.4 resolved (%) · Claude 4.5 Opus (20251101) (med…ready
SWE-bench Verified
Code / Agents
SWE-bench Verified is the 500-issue subset of SWE-bench that human annotators confirmed to be solvable and correctly specified. Each task is a real GitHub issue in a Python repository.
swe-bench-verified
History
SWE-bench Verified was released in August 2024 through work by OpenAI and the SWE-bench team to improve the reliability of repository-level software engineering evaluation. It selected tasks from the original SWE-bench collection after human review of whether their issue descriptions and tests supported a fair, solvable task. The dataset is hosted under princeton-nlp on Hugging Face.
How it was built
The benchmark contains 500 tasks drawn from real GitHub issues in Python repositories. Each task is tied to a base commit and a test-based evaluation of a candidate patch. Human annotators screened the tasks for solvability and specification quality, reducing cases where an unclear request or unsuitable tests could confound the result. Successful patches must fix the targeted failing tests while preserving the required previously passing tests.
At a glance
- Size
- Not imported here; see the official distribution for the current size.
- Released
- Dataset published on Hugging Face 2024-08
- Maintainer
- princeton-nlp (source organisation)
- Answer access
- The dataset declares no licence, and a run needs the repository checkout and test harness rather than the issue text alone. The catalogue links to the official distribution.
- Highest recorded
- 74.4 resolved (%) — Claude 4.5 Opus (20251101) (medium) (leaderboard, 2025-11-24)
What a problem looks like
A real GitHub issue with the repository, base commit and the tests that must go from failing to passing.
How it is scored
The dataset declares no licence, and a run needs the repository checkout and test harness rather than the issue text alone. The catalogue links to the official distribution.
Reported scores
Evaluation conditions differ between rows, so this is not a ranking. Rows marked vendor are reports by the model’s own organization.
| Model | Score | Metric | Setup | Reported by | Source |
|---|---|---|---|---|---|
| Claude 4.5 Opus (20251101) (medium)Anthropic | 74.4 | resolved (%) | Verified; mini-SWE-agent; Model: claude-opus-4-5-20251101; Org: Anthropic; System: Attempts - 1; Mini: 1.16.0 | leaderboard | Verified leaderboard; 20251124_mini-v1.16.0_claude-opus-4-5-20251101Reported 2025-11-24 |
| Gemini 3 Pro Preview (2025-11-18)Google DeepMind | 74.2 | resolved (%) | Verified; mini-SWE-agent; Model: gemini-3-pro-preview; Org: Google DeepMind; System: Attempts - 1; Mini: 1.15.0 | leaderboard | Verified leaderboard; 20251118_mini-v1.15.0_gemini-3-pro-preview-20251118Reported 2025-11-18 |
| OpenHands + GPT-5OpenAI | 71.8 | resolved (%) | Verified; OpenHands; Model: openai/gpt-5-2025-08-07; Org: OpenHands; System: Attempts - 1 | leaderboard | Verified leaderboard; 20250807_openhands_gpt5Reported 2025-08-07 |
Why it is hard
A patch must make specific tests pass without breaking others, inside a real codebase the model has not seen.
Limitations and criticism
A resolved task means that the patch satisfies the evaluation tests, not that it meets every unstated user expectation or is ready for production. The collection covers a particular set of public Python repositories, so it does not represent all languages or software engineering work. Public issues, historical patches and test data create potential prior-exposure risks. Agent tools, retry budgets and scaffolding also affect results, making the reported score a property of the complete evaluation system.
How much room is left
The leaderboard records included here report 74.4 percent resolved for Claude 4.5 Opus at medium effort and 74.2 percent for Gemini 3 Pro Preview, using mini-SWE-agent in November 2025. These results leave roughly a quarter of the tasks unresolved in the reported configurations. The records use different mini-SWE-agent versions, so even their close scores should not be read as a controlled comparison of models alone. They demonstrate remaining headroom in those reports rather than establishing the current leaderboard ceiling.
Reference links
- Issues
- Dataset
Related benchmarks
Discussion
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Public problem summary
Catalogue metadata only; public problem statistics are not available.
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