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multimodal benchmark

SWE-MM Leaderboard

SWE-MM evaluates software-engineering agents on repository tasks that require understanding both source code and visual evidence.

Updated Aug 17, 2026

Models1
Model coverage1
MetricScore
EvidenceB

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SWE-MM Ranking

Higher score ranks better on this benchmark.

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Rank
Model
Score
Percentile
Participants
Evidence
Evaluated
Rank01ModelACQwen3.8-27BAlibaba Cloud / Qwen TeamScore38.6%Percentile100.0%Participants1EvidenceCEvaluatedAug 17, 2026

SWE-MM Highlights

The leading models and scores on this benchmark.

Rank #1Qwen3.8-27B38.6%

The Top AI Models for SWE-MM

The first five results on this benchmark, with official price and output speed added where the model identity can be matched.

Ranking basisThis swe-mm AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.

  1. 01
    AC
    Qwen3.8-27BAlibaba Cloud / Qwen Team
    Score
    38.6%

    Strengths

    • Ranks #1 of 1 compared models
    • 100th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures SWE-MM, not total model capability

Selection summary

Best AI Models for SWE-MM

Qwen3.8-27B currently leads SWE-MM with 38.6%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price and runtime.

Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.

Benchmark rank #1Qwen3.8-27B38.6%

What is SWE-MM?

What SWE-MM measures and how its scores work.

SWE-MM evaluates software-engineering agents on repository tasks that require understanding both source code and visual evidence.

Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.

Family
SWE-MM
Modality
multimodal
Primary category
multimodal
Score direction
higher
LLMBoard eligible
No
Evaluation key
swe-mm|llm-stats-current

Benchmark scores retain their original unit. Overall score eligibility is shown separately.

FAQ

Common questions about SWE-MM.

Which model scores highest on SWE-MM?

Qwen3.8-27B is currently ranked first with 38.6%.

What does SWE-MM measure?

SWE-MM evaluates software-engineering agents on repository tasks that require understanding both source code and visual evidence.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

1 model results are currently shown.

Does this benchmark affect the overall score?

No. This benchmark is shown for reference but does not contribute to the overall score.