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.
Benchmark data & downloads
Where to get, inspect and reuse the data behind this benchmark.
Model scores 4 models tested
| # | 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 |