HELM VHELM - Mmmu - Marketing: leaderboard

VHELM (Stanford CRFM HELM for vision-language models, run v2.1.2) MMMU Marketing: college-level multiple-choice questions over images from the MMMU validation set for this subject; 29 questions, prefix quasi-exact match. Percent correct, 0-100, higher is better; a small sample.

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)96.55#126
2O4 Mini (2025-04-16)96.55#183
3O3 (2025-04-16)96.55#125
4O1 (2024-12-17)89.66#157
5Llama 4 Maverick Instruct FP882.76#424
6Gemini 1.5 Pro (002)79.31#368
7Claude 3.5 Sonnet (20240620)68.97#308
8Gemini 2.0 Flash (Preview)68.97#376
9Claude 3.5 Sonnet (20241022)65.52#302
10GPT-4.565.52#278
11GPT-4.1 Mini62.07#363
12GPT-4.1 (2025-04-14)62.07#257
13GPT-4o (2024-08-06)62.07#343
14Gemini 2.0 Pro (Preview 02-05)62.07#318
15Gemini 2.0 Flash Lite62.07#455

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-mmmu-marketing · How It Works · Data refreshed daily, snapshot 2026-11-05.