--- license: cc-by-nc-sa-4.0 language: - en pretty_name: VideoMemBench task_categories: - video-text-to-text - visual-question-answering size_categories: - n<1K tags: - video - multi-turn - memory - benchmark configs: - config_name: default data_files: - split: test path: annotations.jsonl ---

VideoMemBench: How Well Do MLLMs Remember in Video Conversations?

🍎 Project: VideoMemBench cs.CV: arXiv:2610.40131 GitHub: VideoMemBench 🏆 Leaderboard: Rank

## 🎬 Benchmark VideoMemBench evaluates **in-context video memory**: the ability to retrieve and reuse information established earlier in a multi-turn conversation about a video. Each sample pairs one video with an ordered dialogue, reference answers, and typed dependencies between turns. - **Episodic dependency:** a later question revisits a visual episode, entity, or event established in an earlier turn. - **Answer dependency:** a later question builds on an earlier answer, requiring information carried through the conversation. Dependencies are checked for necessity and non-leakage, and answers must be visually grounded. A planner, generator, and judger construct and check dialogues, followed by human expert review. See the [paper](https://arxiv.org/abs/2610.40131) for construction and collection details. ## 🏆 Leaderboard
Rank Model TurnAcc
All turns		 EpiDepAcc
Episodic dependency		 AnsDepAcc
Answer dependency		 Overall ↓
Mean score
1 Claude Opus 5 52.3 49.1 35.7 45.7
2 Gemini 3.8 Flash 50.4 46.6 30.3 42.4
3 Kimi K3 50.5 49.0 27.1 42.2
4 GPT-5.6-sol 50.0 47.8 27.1 41.7
5 Gemini 3.5 Flash 49.6 45.5 29.7 41.6
6 GPT-5.5 48.0 46.5 27.0 40.5
7 Claude Sonnet 5 47.8 45.3 27.3 40.2
8 Gemini 3.1 Pro Preview 45.7 44.8 27.0 39.1
9 GPT-5.4 44.4 42.6 21.9 36.3
10 Qwen3-VL-235B-A22B 43.1 41.6 18.6 34.4
11 Qwen3.5-27B 42.2 39.8 20.9 34.3
12 Qwen3.5-397B-A17B 42.3 40.1 20.3 34.2
13 Qwen3.5-122B-A10B 42.4 39.5 19.9 33.9
14 Qwen3.5-35B-A3B 38.2 36.3 18.0 30.8
15 Qwen3-VL-32B 39.3 35.9 17.2 30.8
16 Qwen3-VL-30B-A3B 36.4 35.1 15.8 29.1
17 Qwen3.5-9B 34.6 33.4 12.9 27.0
18 Qwen3-VL-8B 34.8 32.1 12.3 26.4
19 Qwen3.5-4B 33.8 32.6 12.5 26.3
20 Qwen3-VL-4B 32.4 29.4 12.9 24.9
21 Qwen2.5-VL-32B 31.7 32.6 9.6 24.6
22 Qwen2.5-VL-7B 26.2 24.9 8.4 19.8
23 Keye-VL-1.5-8B 25.4 24.5 8.2 19.4
24 Qwen2.5-VL-3B 23.5 24.4 5.7 17.9
25 Qwen3.5-0.8B 21.6 21.3 5.7 16.2
### 🎞️ Evaluation with fewer frames 13 open-weight models evaluated with 64 or 32 fixed frames per video.
64 frames
Rank Model TurnAcc
All turns		 EpiDepAcc
Episodic dependency		 AnsDepAcc
Answer dependency		 Overall ↓
Mean score
1 Qwen3-VL-235B-A22B 40.3 39.3 15.6 31.7
2 Qwen3.5-122B-A10B 37.8 36.5 15.2 29.8
3 Qwen3.5-27B 37.9 36.7 14.8 29.8
4 Qwen3-VL-32B 37.8 36.1 14.5 29.5
5 Qwen3.5-35B-A3B 37.0 36.7 13.5 29.1
6 Qwen3-VL-30B-A3B 33.6 32.9 11.5 26.0
7 Qwen3-VL-8B 32.7 32.0 11.9 25.5
8 Qwen3.5-9B 33.4 33.2 9.6 25.4
9 Qwen3-VL-4B 31.3 30.3 12.1 24.5
10 Keye-VL-1.5-8B 24.5 24.9 6.3 18.5
11 Qwen2.5-VL-7B 24.1 22.7 6.4 17.7
12 Qwen2.5-VL-3B 22.5 22.5 5.1 16.7
13 Qwen3.5-0.8B 21.0 21.1 4.7 15.6
32 frames
Rank Model TurnAcc
All turns		 EpiDepAcc
Episodic dependency		 AnsDepAcc
Answer dependency		 Overall ↓
Mean score
1 Qwen3-VL-235B-A22B 36.3 37.8 14.5 29.5
2 Qwen3.5-122B-A10B 35.0 35.2 10.7 27.0
3 Qwen3.5-35B-A3B 34.5 34.1 12.3 27.0
4 Qwen3.5-27B 34.9 34.2 11.7 26.9
5 Qwen3-VL-32B 34.1 32.6 13.1 26.6
6 Qwen3-VL-30B-A3B 32.1 32.5 8.0 24.2
7 Qwen3.5-9B 29.8 30.4 8.6 22.9
8 Qwen3-VL-8B 29.4 29.4 9.2 22.7
9 Qwen3-VL-4B 29.0 29.0 8.8 22.3
10 Qwen2.5-VL-7B 23.2 23.7 4.9 17.3
11 Keye-VL-1.5-8B 23.3 23.1 5.1 17.2
12 Qwen2.5-VL-3B 22.6 23.4 4.7 16.9
13 Qwen3.5-0.8B 20.1 21.8 4.7 15.5
For download and evaluation instructions, see the [GitHub documentation](https://github.com/Oliiveralien/VideoMemBench#readme). ## 📜 License and terms of use - Dataset annotations: [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). - For non-commercial academic research and evaluation only. Not for model training. - Source videos are not covered by this license; their original rights and terms apply. ## 📚 Citation ```bibtex @misc{hu2026videomembenchmllmsremembervideo, title={VideoMemBench: How Well Do MLLMs Remember in Video Conversations?}, author={Zhengdong Hu and Chao Wang and Kaituo Feng and Huanjin Yao and Mingfei Lu and Hehe Fan and Yi Yang}, year={2026}, eprint={2610.40131}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2610.40131}, } ```