agents benchmark
DeepSWE
DeepSWE is a software engineering agent benchmark evaluated with the mini-swe-agent harness, where each task is solved in an isolated container with no internet access. It measures an agent's ability to autonomously resolve real-world coding issues end to end.
Updated Aug 11, 2026
Models10
Model coverage10
MetricScore
EvidenceB
DeepSWE Ranking
Higher score ranks better on this benchmark.
| 01 | OP | 72.7% | 100.0% | 10 | C | |
| 02 | OP | 69.6% | 88.9% | 10 | C | |
| 03 | MA | 67.5% | 77.8% | 10 | C | |
| 04 | OP | 67.2% | 66.7% | 10 | C | |
| 05 | DE | 54.4% | 55.6% | 10 | C | |
| 06 | XA | 53.0% | 44.4% | 10 | C | |
| 07 | ZA | 46.2% | 33.3% | 10 | C | |
| 08 | BY | 32.7% | 22.2% | 10 | C | |
| 09 | TE | 28.0% | 11.1% | 10 | C | |
| 10 | BY | 23.0% | 0.0% | 10 | C |
DeepSWE Score Distribution
A closer view of the leading scores on this benchmark.
DeepSWE Highlights
The leading models and scores on this benchmark.
What is DeepSWE?
What DeepSWE measures and how its scores work.
DeepSWE is a software engineering agent benchmark evaluated with the mini-swe-agent harness, where each task is solved in an isolated container with no internet access. It measures an agent's ability to autonomously resolve real-world coding issues end to end.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- DeepSWE
- Modality
- text
- Primary category
- agents
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- deepswe|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about DeepSWE.
Which model scores highest on DeepSWE?
GPT-5.6 Sol is currently ranked first with 72.7%.
What does DeepSWE measure?
DeepSWE is a software engineering agent benchmark evaluated with the mini-swe-agent harness, where each task is solved in an isolated container with no internet access. It measures an agent's ability to autonomously resolve real-world coding issues end to end.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
10 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.