multimodal benchmark
Video-MME is a comprehensive evaluation benchmark for multi-modal large language models in video analysis. It features 900 videos across 6 primary visual domains with 30 subfields, ranging from 11 seconds to 1 hour in duration, with 2,700 question-answer pairs. The benchmark evaluates MLLMs' capabilities in processing sequential visual data and multi-modal content including video frames, subtitles, and audio.
Updated Aug 11, 2026
Higher score ranks better on this benchmark.
| 01 | AC | 83.9% | 100.0% | 10 | C | |
| 02 | AC | 82.8% | 88.9% | 10 | C | |
| 03 | AC | 82.5% | 77.8% | 10 | C | |
| 04 | AC | 82.5% | 66.7% | 10 | C | |
| 05 | AC | 79.2% | 55.6% | 10 | C | |
| 06 | AC | 79.0% | 44.4% | 10 | C | |
| 07 | AC | 77.3% | 33.3% | 10 | C | |
| 08 | AC | 73.3% | 22.2% | 10 | C | |
| 09 | AC | 70.5% | 11.1% | 10 | C | |
| 10 | AC | 65.1% | 0.0% | 10 | C |
A closer view of the leading scores on this benchmark.
The leading models and scores on this benchmark.
What VideoMME w/o sub. measures and how its scores work.
Video-MME is a comprehensive evaluation benchmark for multi-modal large language models in video analysis. It features 900 videos across 6 primary visual domains with 30 subfields, ranging from 11 seconds to 1 hour in duration, with 2,700 question-answer pairs. The benchmark evaluates MLLMs' capabilities in processing sequential visual data and multi-modal content including video frames, subtitles, and audio.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about VideoMME w/o sub..
Qwen3.5-122B-A10B is currently ranked first with 83.9%.
Video-MME is a comprehensive evaluation benchmark for multi-modal large language models in video analysis. It features 900 videos across 6 primary visual domains with 30 subfields, ranging from 11 seconds to 1 hour in duration, with 2,700 question-answer pairs. The benchmark evaluates MLLMs' capabilities in processing sequential visual data and multi-modal content including video frames, subtitles, and audio.
Yes. Higher values rank better for this benchmark.
10 model results are currently shown.
Yes. This benchmark can contribute to the current LLMBoard capability score.