Agentic Coding Benchmark

KernelBench Hard

Category

Agentic Coding

Score type

percent

Scale

100%

higher is better

Models tested

1

What is KernelBench Hard?

KernelBench Hard 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 it test?

KernelBench Hard 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

How to read the score

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

Frequently asked questions

What is the KernelBench Hard benchmark?

KernelBench Hard 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 KernelBench Hard test?

KernelBench Hard 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 KernelBench Hard 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 KernelBench Hard 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/kernelbench-hard/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 1 model tested

Reset
# Model Score Date Source Verified
1 MiniMax-M3
MiniMaxAI
28.8%
11.06.2026 MiniMax-M3 model card benchmark table Unverified

What now?

You've seen how models score on KernelBench Hard — here's what to do next.