Agentic Coding Benchmark

MLS-Bench-Lite

MLS-Bench-Lite - agentic coding benchmark. Evaluated with Claude Code using a 5-hour timeout and max_tokens=131,072. Scores from the official leaderboard.

Category

Agentic Coding

Score type

percent

Scale

relative

higher is better

Models tested

4

What is MLS-Bench-Lite?

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

What does it test?

MLS-Bench-Lite 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.

Official methodology note: MLS-Bench-Lite - agentic coding benchmark. Evaluated with Claude Code using a 5-hour timeout and max_tokens=131,072. Scores from the official leaderboard.

Frequently asked questions

What is the MLS-Bench-Lite benchmark?

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

What does MLS-Bench-Lite test?

MLS-Bench-Lite 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 MLS-Bench-Lite 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 MLS-Bench-Lite 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/mls-bench-lite/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 4 models tested

Reset
# Model Score Date Source Verified
1 Kimi K3
Moonshot AI
48.3%
18.08.2026 Report Verified
2 Qwen3.8-2.4T-A95B
Qwen
41%
18.08.2026 Report Verified
3 Kimi K2.7 Code
Moonshot AI
35.1%
23.08.2026 Report Verified
4 Kimi K2.6
Moonshot AI
26.7%
23.08.2026 Report Verified

What now?

You've seen how models score on MLS-Bench-Lite — here's what to do next.