FrontierSWE

FrontierSWE is a benchmark for how well a model works as an autonomous coding agent. The official description says: "FrontierSWE - agentic coding benchmark". Below we describe in simple words what benchmarks of this type generally examine.

FrontierSWE 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: FrontierSWE - agentic coding benchmark. MEAN@5 scores from the official FrontierSWE leaderboard. Dominance scores recomputed from raw scores using the official evaluation script.

What is the FrontierSWE benchmark?

FrontierSWE is a benchmark for how well a model works as an autonomous coding agent. The official description says: 'FrontierSWE - agentic coding benchmark'. Below we describe in simple words what benchmarks of this type generally examine.

What does FrontierSWE test?

FrontierSWE 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 FrontierSWE score calculated?

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

Where do I get FrontierSWE data and scores?

The official source is https://www.frontierswe.com. 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/frontierswe/scores) or pull it directly from the Open LLM Radar API with any HTTP client.