Agentic Coding Benchmark

SWE-MM

SWE-MM - multimodal software engineering benchmark. Evaluated on Claude Code harness using public dev split of SWE-bench Multimodal with modifications.

Category

Agentic Coding

Score type

percent

Scale

100%

higher is better

Models tested

2

What is SWE-MM?

SWE-MM is a benchmark for how well a model works as an autonomous coding agent. The official description says: "SWE-MM - multimodal software engineering benchmark". Below we describe in simple words what benchmarks of this type generally examine.

What does it test?

SWE-MM 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: SWE-MM - multimodal software engineering benchmark. Evaluated on Claude Code harness using public dev split of SWE-bench Multimodal with modifications.

Frequently asked questions

What is the SWE-MM benchmark?

SWE-MM is a benchmark for how well a model works as an autonomous coding agent. The official description says: 'SWE-MM - multimodal software engineering benchmark'. Below we describe in simple words what benchmarks of this type generally examine.

What does SWE-MM test?

SWE-MM 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 SWE-MM 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 SWE-MM 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/swe-mm/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 2 models tested

Reset
# Model Score Date Source Verified
1 Qwen3.8-27B
Qwen Team (Alibaba Cloud)
38.6%
18.08.2026 Report Verified
2 Qwen3.6-27B
Qwen Team (Alibaba Cloud)
25.7%
18.08.2026 Report Unverified

What now?

You've seen how models score on SWE-MM — here's what to do next.