FullStackBench en

FullStackBench en is a benchmark for how well a model works as an autonomous coding agent. The official description says: "FullStackBench en - English full-stack code generation benchmark". Below we describe in simple words what benchmarks of this type generally examine.

FullStackBench en 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: FullStackBench en - English full-stack code generation benchmark.

What is the FullStackBench en benchmark?

FullStackBench en is a benchmark for how well a model works as an autonomous coding agent. The official description says: 'FullStackBench en - English full-stack code generation benchmark'. Below we describe in simple words what benchmarks of this type generally examine.

What does FullStackBench en test?

FullStackBench en 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 FullStackBench en 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 FullStackBench en 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/fullstackbench-en/scores) or pull it directly from the Open LLM Radar API with any HTTP client.