multimodal benchmark
The first-ever comprehensive evaluation benchmark of Multi-modal LLMs in Video analysis. Features 900 videos (254 hours) with 2,700 question-answer pairs covering 6 primary visual domains and 30 subfields. Evaluates temporal understanding across short (11 seconds) to long (1 hour) videos with multi-modal inputs including video frames, subtitles, and audio.
Updated Aug 11, 2026
Higher score ranks better on this benchmark.
| 01 | AC | 90.4% | 100.0% | 10 | C | |
| 02 | AC | 87.7% | 88.9% | 10 | C | |
| 03 | AC | 87.3% | 77.8% | 10 | C | |
| 04 | AC | 87.0% | 66.7% | 10 | C | |
| 05 | OP | 86.7% | 55.6% | 10 | C | |
| 06 | AC | 86.6% | 44.4% | 10 | C | |
| 07 | AC | 86.6% | 33.3% | 10 | C | |
| 08 | AC | 77.9% | 22.2% | 10 | C | |
| 09 | AC | 72.4% | 11.1% | 10 | C | |
| 10 | AC | 71.6% | 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 sub. measures and how its scores work.
The first-ever comprehensive evaluation benchmark of Multi-modal LLMs in Video analysis. Features 900 videos (254 hours) with 2,700 question-answer pairs covering 6 primary visual domains and 30 subfields. Evaluates temporal understanding across short (11 seconds) to long (1 hour) videos with multi-modal inputs 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 sub..
Qwen3.8 Max is currently ranked first with 90.4%.
The first-ever comprehensive evaluation benchmark of Multi-modal LLMs in Video analysis. Features 900 videos (254 hours) with 2,700 question-answer pairs covering 6 primary visual domains and 30 subfields. Evaluates temporal understanding across short (11 seconds) to long (1 hour) videos with multi-modal inputs 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.