reasoning benchmark
SWE-bench Verified (Agentic Coding)
SWE-bench Verified is a human-filtered subset of 500 software engineering problems drawn from real GitHub issues across 12 popular Python repositories. Given a codebase and an issue description, language models are tasked with generating patches that resolve the described problems. This benchmark evaluates AI's real-world agentic coding skills by requiring models to navigate complex codebases, understand software engineering problems, and coordinate changes across multiple functions, classes, and files to fix well-defined issues with clear descriptions.
Updated Aug 11, 2026
Models2
Model coverage2
MetricScore
EvidenceB
SWE-bench Verified (Agentic Coding) Ranking
Higher score ranks better on this benchmark.
| 01 | AN | 77.2% | 100.0% | 2 | C | |
| 02 | MA | 65.8% | 0.0% | 2 | C |
SWE-bench Verified (Agentic Coding) Score Distribution
A closer view of the leading scores on this benchmark.
SWE-bench Verified (Agentic Coding) Highlights
The leading models and scores on this benchmark.
What is SWE-bench Verified (Agentic Coding)?
What SWE-bench Verified (Agentic Coding) measures and how its scores work.
SWE-bench Verified is a human-filtered subset of 500 software engineering problems drawn from real GitHub issues across 12 popular Python repositories. Given a codebase and an issue description, language models are tasked with generating patches that resolve the described problems. This benchmark evaluates AI's real-world agentic coding skills by requiring models to navigate complex codebases, understand software engineering problems, and coordinate changes across multiple functions, classes, and files to fix well-defined issues with clear descriptions.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- SWE-bench Verified (Agentic Coding)
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- swe-bench-verified-(agentic-coding)|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about SWE-bench Verified (Agentic Coding).
Which model scores highest on SWE-bench Verified (Agentic Coding)?
Claude Sonnet 4.5 is currently ranked first with 77.2%.
What does SWE-bench Verified (Agentic Coding) measure?
SWE-bench Verified is a human-filtered subset of 500 software engineering problems drawn from real GitHub issues across 12 popular Python repositories. Given a codebase and an issue description, language models are tasked with generating patches that resolve the described problems. This benchmark evaluates AI's real-world agentic coding skills by requiring models to navigate complex codebases, understand software engineering problems, and coordinate changes across multiple functions, classes, and files to fix well-defined issues with clear descriptions.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
2 model results are currently shown.
Does this benchmark affect the overall score?
No. This benchmark is shown for reference but does not contribute to the overall score.