HELM VHELM - Blink Perception - Counting: leaderboard

VHELM (Stanford CRFM HELM for vision-language models, run v2.1.2) BLINK Counting task: counting objects in an image, one of BLINK's perception tasks that humans solve at a glance, asked as multiple choice; 120 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
1Gemini 2.5 Pro (Preview 03-25)85#126
2Gemini 2.0 Flash (Preview)81.67#376
3GPT-4.581.67#278
4O3 (2025-04-16)80.83#125
5O4 Mini (2025-04-16)80#183
6Gemini 2.0 Flash Lite79.17#455
7Llama 4 Scout Instruct78.33#547
8GPT-4 Turbo78.33#417
9Gemini 2.0 Pro (Preview 02-05)78.33#318
10Gemini 2.0 Flash77.5#351
11Llama 4 Maverick Instruct FP877.5#424
12GPT-4o (2024-05-13)76.67#337
13O1 (2024-12-17)76.67#157
14GPT-4o (2024-08-06)75.83#343
15Claude 3.5 Sonnet (20240620)74.17#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-counting · How It Works · Data refreshed daily, snapshot 2026-11-05.