DeepSWE 1.1

DeepSWE 1.1 is a benchmark for how well a model works as an autonomous coding agent. The official description says: "DeepSWE 1.1 - agentic coding benchmark evaluated with Claude Code harness at temp=1.0, top_p=0.95, 256K context window". Below we describe in simple words what benchmarks of this type generally examine.

DeepSWE 1.1 measures how well a model works as an autonomous coding agent. In detail, it checks:

• fixing real, open bugs from real software repositories
• planning multi-step changes, running commands and editing files by itself
• passing the project's test suite after its changes
• handling whole repositories, not just small isolated code snippets

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.

Official methodology note: DeepSWE 1.1 - agentic coding benchmark evaluated with Claude Code harness at temp=1.0, top_p=0.95, 256K context window.

What is the DeepSWE 1.1 benchmark?

DeepSWE 1.1 is a benchmark for how well a model works as an autonomous coding agent. The official description says: 'DeepSWE 1.1 - agentic coding benchmark evaluated with Claude Code harness at temp=1.0, top_p=0.95, 256K context window'. Below we describe in simple words what benchmarks of this type generally examine.

What does DeepSWE 1.1 test?

DeepSWE 1.1 mainly measures how well a model works as an autonomous coding agent. Concretely, it checks: fixing real, open bugs from real software repositories; planning multi-step changes, running commands and editing files by itself; passing the project's test suite after its changes.

How is the DeepSWE 1.1 score calculated?

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.

Where do I get DeepSWE 1.1 data and scores?

The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/deep-swe-1-1/scores) or pull it directly from the Open LLM Radar API with any HTTP client.