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multimodal benchmark

MusicCaps

MusicCaps is a dataset composed of 5,521 music examples, each labeled with an English aspect list and a free text caption written by musicians. The dataset contains 10-second music clips from AudioSet paired with rich textual descriptions that capture sonic qualities and musical elements like genre, mood, tempo, instrumentation, and rhythm. Created to support research in music-text understanding and generation tasks.

Updated Aug 11, 2026

Models1
Model coverage1
MetricScore
EvidenceB

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MusicCaps Ranking

Higher score ranks better on this benchmark.

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01ACQwen2.5-Omni-7BAlibaba Cloud / Qwen Team32.8%100.0%1CAug 11, 2026

MusicCaps Highlights

The leading models and scores on this benchmark.

Rank #1Qwen2.5-Omni-7B32.8%

What is MusicCaps?

What MusicCaps measures and how its scores work.

MusicCaps is a dataset composed of 5,521 music examples, each labeled with an English aspect list and a free text caption written by musicians. The dataset contains 10-second music clips from AudioSet paired with rich textual descriptions that capture sonic qualities and musical elements like genre, mood, tempo, instrumentation, and rhythm. Created to support research in music-text understanding and generation tasks.

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

Family
MusicCaps
Modality
multimodal
Primary category
multimodal
Score direction
higher
LLMBoard eligible
No
Evaluation key
musiccaps|llm-stats-current

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

FAQ

Common questions about MusicCaps.

Which model scores highest on MusicCaps?

Qwen2.5-Omni-7B is currently ranked first with 32.8%.

What does MusicCaps measure?

MusicCaps is a dataset composed of 5,521 music examples, each labeled with an English aspect list and a free text caption written by musicians. The dataset contains 10-second music clips from AudioSet paired with rich textual descriptions that capture sonic qualities and musical elements like genre, mood, tempo, instrumentation, and rhythm. Created to support research in music-text understanding and generation tasks.

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.