MMVU
MMVU - multi-modal video understanding benchmark evaluating comprehension across diverse video types.
Category
Video Understanding
Score type
percent
Scale
relative
higher is better
Models tested
7
What is MMVU?
MMVU is a benchmark for understanding videos instead of static images. The official description says: "MMVU - multi-modal video understanding benchmark evaluating comprehension across diverse video types". Below we describe in simple words what benchmarks of this type generally examine.
What does it test?
MMVU measures understanding videos instead of static images. In detail, it checks:
- following events, actions and their order across time
- answering questions about long videos (with and without subtitles)
- reasoning about what happened and why, not just single frames
- integrating audio/subtitle context with the visual stream
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: MMVU - multi-modal video understanding benchmark evaluating comprehension across diverse video types.
Frequently asked questions
What is the MMVU benchmark?
MMVU is a benchmark for understanding videos instead of static images. The official description says: 'MMVU - multi-modal video understanding benchmark evaluating comprehension across diverse video types'. Below we describe in simple words what benchmarks of this type generally examine.
What does MMVU test?
MMVU mainly measures understanding videos instead of static images. Concretely, it checks: following events, actions and their order across time; answering questions about long videos (with and without subtitles); reasoning about what happened and why, not just single frames.
How is the MMVU score calculated?
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.
Where do I get MMVU 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/mmvu/scores) or pull it directly from the Open LLM Radar API with any HTTP client.
Benchmark data & downloads
Where to get, inspect and reuse the data behind this benchmark.
Benchmark dataset
The question/task data itself — download and evaluate locally
Benchmark metadata (JSON)
This page's benchmark record from the Open LLM Radar API
All model scores (JSON download)
Full score table as JSON; paginate with limit/offset params
Open LLM Radar API docs
Swagger UI to explore and reuse all radar API endpoints
Model scores 7 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
Kimi K3
Moonshot AI
|
82.1%
|
18.08.2026 | Report | Verified |
| 2 |
Qwen3.5-122B-A10B
Qwen Team (Alibaba Cloud)
|
74.7%
|
01.02.2026 | Report | Verified |
| 3 |
Qwen3.5-27B
Qwen Team (Alibaba Cloud)
|
73.3%
|
01.02.2026 | Report | Unverified |
| 4 |
Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
|
72.3%
|
01.02.2026 | Report | Unverified |
| 5 |
Qwen3-235B-A22B
Qwen Team (Alibaba Cloud)
|
71.1%
|
01.02.2026 | Report | Unverified |
| 6 |
Qwen3.5-4B
Qwen
|
64.9%
|
01.02.2026 | Report | Verified |
| 7 |
LFM3-VL-2B
LiquidAI
|
52.9%
|
— | Report | Verified |