VideoMemBench/data

🎬 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 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.

📜 License and terms of use

• Dataset annotations: CC 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

@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},
}