HELM VHELM - Blink Reasoning - Multi-view Reasoning: leaderboard

VHELM (Stanford CRFM HELM for vision-language models, run v2.1.2) BLINK Multi-view Reasoning task: inferring camera motion between two views, one of BLINK's perception tasks that humans solve at a glance, asked as multiple choice; 133 questions, prefix quasi-exact match. Percent correct, 0-100, higher is better.

Metric: Prefix Quasi-Exact Match (%). Source: crfm.stanford.edu. Saturation forecast: Estimated already saturated. 48 models tracked.

Top models

#ModelScoreOverall rank
1Claude 3.5 Sonnet (20240620)100#308
2Llama 4 Maverick Instruct FP8100#424
3Claude 3 Haiku (20240307)100#780
4Claude 3 Sonnet (20240229)100#694
5Claude 3 Opus (20240229)100#428
6Gemini 2.0 Flash Lite100#455
7GPT-4.1 (2025-04-14)99.25#257
8GPT-4o Mini (2024-07-18)98.5#568
9GPT-4 Turbo96.99#417
10Llama 4 Scout Instruct95.49#547
11O1 (2024-12-17)92.48#157
12Gemini 2.5 Pro (Preview 03-25)83.46#126
13O3 (2025-04-16)68.42#125
14GPT-4o (2024-11-20)68.42#392
15O4 Mini (2025-04-16)67.67#183

No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.

Interactive version: theaggregate.ai/benchmark?slug=helm-vhelm-blink-reasoning-multi-view-reasoning · How It Works · Data refreshed daily, snapshot 2026-11-05.