CL-bench

CL-bench is a benchmark for how well a model works as an autonomous coding agent. It was built to compare language models on a clearly defined set of tasks under the same grading rules, so scores are directly comparable between models.

CL-bench 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.

What is the CL-bench benchmark?

CL-bench is a benchmark for how well a model works as an autonomous coding agent. It was built to compare language models on a clearly defined set of tasks under the same grading rules, so scores are directly comparable between models.

What does CL-bench test?

CL-bench 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 CL-bench 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 CL-bench 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/cl-bench/scores) or pull it directly from the Open LLM Radar API with any HTTP client.