HELM VHELM - Mme - Landmark: leaderboard

VHELM (Stanford CRFM HELM for vision-language models, run v2.1.2) MME landmark subtask: yes/no questions about recognizing landmarks in images; 400 questions, prefix quasi-exact match. Percent correct, 0-100, higher is better; guessing scores 50.

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

Top models

#ModelScoreOverall rank
1GPT-4o (2024-08-06)97.25#343
2GPT-4.597#278
3GPT-4o (2024-11-20)95.5#392
4Gemini 1.5 Flash (002)95.25#587
5Gemini 2.5 Pro (Preview 03-25)94.25#126
6GPT-4o (2024-05-13)94.25#337
7Gemini 1.5 Pro (002)93.75#368
8GPT-4.1 (2025-04-14)92.5#257
9Gemini 2.0 Pro (Preview 02-05)92.25#318
10Claude 3.5 Sonnet (20241022)92#302
11GPT-4.1 Mini91.5#363
12Llama 4 Scout Instruct88#547
13Gemini 2.0 Flash (Preview)87.75#376
14GPT-4o Mini (2024-07-18)87.5#568
15Gemini 2.0 Flash86.25#351

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-mme-landmark · How It Works · Data refreshed daily, snapshot 2026-11-05.