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# JT3.5 and JT-VL-Chat tie for first on the MVBench video test at 74.1%; the top 15 span 23 points
- URL: https://aiadoption.org/data/mvbench-video-understanding-leaderboard/
- Published: 2026-10-19T15:55:38.000Z
- Updated: 2026-10-21T06:02:49.000Z
- Description: Two models, JT3.5 and JT-VL-Chat, share the top MVBench score of 74.1% in early 2026. The 15th-placed model, VideoChat2, scored 51.1%, 23 points lower.
- Author: Manu
- Tags: Data, AI Benchmarks, Computer Vision, #chart, #ai-index-2026, #hub-benchmarks

74.1%

MVBench average accuracy of JT-VL-Chat and JT3.5, tied first in early 2026; Woven-VLLM 73.8%

JT-VL-Chat74.1%

JT3.574.1%

Woven-VLLM73.8%

## What the chart shows

MVBench tests whether multimodal models can follow motion, sequences and changing context across video frames rather than in single images. In early 2026 JT3.5 and JT-VL-Chat tied at the top with 74.1% average accuracy, just ahead of Woven-VLLM (73.8%).

The top 15 span 23 points, down to VideoChat2 at 51.1%. Later variants of the same model score higher: VideoChat2\_mistral reached 60.4% and VideoChat2\_HD\_mistral 62.3%.

## Key numbers

| Measure            | Value   |
| ------------------ | ------- |
| JT3.5              | 74.075% |
| JT-VL-Chat         | 74.075% |
| Woven-VLLM         | 73.81%  |
| Video-CCAM-7B-v1.2 | 69.23%  |
| VideoChat2 (15th)  | 51.1%   |

## How to read it

- Snapshot of the MVBench leaderboard (OpenGVLab) in early 2026, top 15 models only.
- Accuracy is averaged across the benchmark’s multiple-choice video tasks.
- Several entries are variants of the same model family (VideoChat2, Video-CCAM).

**Source:** [MVBench Leaderboard](https://aiadoption.org/sources/#mvbench), 2026, via the AI Index Report 2026 (Stanford HAI), figure 2.3.1: *MVBench: average accuracy*. Chart and text by AI Adoption.  
Numbers checked against the published data on 20 October 2026.  
[Download the data (CSV)](https://charts.aiadoption.org/ai-index/csv/mvbench-video-understanding-leaderboard.csv?ref=aiadoption.org) · [Cite this chart](#source)