reasoning benchmark
SWE-bench Verified (Agentless)
A human-validated subset of SWE-bench that evaluates language models' ability to resolve real-world GitHub issues using an agentless approach. The benchmark tests models on software engineering problems requiring understanding and coordinating changes across multiple functions, classes, and files simultaneously.
Updated Aug 11, 2026
Models2
Model coverage2
MetricScore
EvidenceB
SWE-bench Verified (Agentless) Ranking
Higher score ranks better on this benchmark.
| 1 | MA | 51.8% | 100.0% | 2 | C | |
| 2 | XI | 35.7% | 0.0% | 2 | C |
SWE-bench Verified (Agentless) Score Distribution
A closer view of the leading scores on this benchmark.
SWE-bench Verified (Agentless) Highlights
The leading models and scores on this benchmark.
What is SWE-bench Verified (Agentless)?
What SWE-bench Verified (Agentless) measures and how its scores work.
A human-validated subset of SWE-bench that evaluates language models' ability to resolve real-world GitHub issues using an agentless approach. The benchmark tests models on software engineering problems requiring understanding and coordinating changes across multiple functions, classes, and files simultaneously.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- SWE-bench Verified (Agentless)
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- swe-bench-verified-(agentless)|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about SWE-bench Verified (Agentless).
Which model scores highest on SWE-bench Verified (Agentless)?
Kimi K2 Instruct is currently ranked first with 51.8%.
What does SWE-bench Verified (Agentless) measure?
A human-validated subset of SWE-bench that evaluates language models' ability to resolve real-world GitHub issues using an agentless approach. The benchmark tests models on software engineering problems requiring understanding and coordinating changes across multiple functions, classes, and files simultaneously.
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.