VA-Bench: leaderboard

VA-Bench (Zhang et al., 2026) tests whether general-purpose multimodal models can carry out simulated robot manipulation from visual demonstrations: the model watches an RGB-only expert video, chooses camera viewpoints, issues metric Cartesian arm commands for a fixed controller and revises them from execution feedback. Terminal task success in percent, macro-averaged over 14 task families (11 single-arm, 3 dual-arm) with 20 seeds each and averaged over three runs; higher is better.

Metric: Task success (%; all 14 base task families (11 single-arm, 3 dual-arm); 20 physically verified seeds per task family in RoboTwin simulation; the model watches an RGB-only expert video, writes its own task summary, chooses camera viewpoints and issues metric Cartesian arm commands that a fixed model-agnostic controller executes, with execution feedback; terminal success from the environment checker; task-family macro-average, mean of three runs). Source: arxiv.org. Saturation forecast: Around February 2027. 12 models tracked.

Top models

#ModelScoreOverall rank
1Qwen 3.8 Max53.93#55
2Claude Opus 552.86#20
3GPT-5.6 Sol51.55#29
4GPT-5.6 Terra23.1#60
5Seed 2.1 Turbo15#156
6Gemini 3.6 Flash14.17#72
7GPT-5.6 Luna13.93#117
8Claude Sonnet 59.05#108
9Qwen 3.7 Max8.1#142
10Qwen 3.7 Plus7.14#203
11MiniMax-M34.05#265
12MiMo-V2.53.33#353

No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.

Interactive version: theaggregate.ai/benchmark?slug=va-bench · How It Works · Data refreshed daily, snapshot 2026-11-12.