HELM VHELM - Blink Perception - Semantic Correspondence: leaderboard

VHELM (Stanford CRFM HELM for vision-language models, run v2.1.2) BLINK Semantic Correspondence task: matching semantically corresponding points across images, one of BLINK's perception tasks that humans solve at a glance, asked as multiple choice; 139 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
1O3 (2025-04-16)83.45#125
2O1 (2024-12-17)78.42#157
3O4 Mini (2025-04-16)77.7#183
4GPT-4.576.98#278
5GPT-4.1 (2025-04-14)74.1#257
6Gemini 2.0 Flash Lite74.1#455
7Gemini 2.5 Pro (Preview 03-25)73.38#126
8Gemini 2.0 Flash (Preview)73.38#376
9Gemini 1.5 Pro (002)71.22#368
10Gemini 2.0 Pro (Preview 02-05)71.22#318
11Gemini 2.0 Flash68.35#351
12GPT-4o (2024-08-06)68.35#343
13GPT-4o (2024-05-13)66.91#337
14Llama 4 Maverick Instruct FP866.91#424
15Claude 3.5 Sonnet (20240620)65.47#308

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-perception-semantic-correspondence · How It Works · Data refreshed daily, snapshot 2026-11-05.