AMVICC - MMVP Individual Accuracy - Presence of Specific Features: leaderboard
AMVICC (Basappa, Goel et al., 2026) asks vision-language models the original MMVP questions about pairs of nearly identical images, grading free-form answers with GPT-4 against the authors' rubric. This board is accuracy in percent on the 70 questions of MMVP's Presence of Specific Features category (35 image pairs), each question scored on its own; higher is better.
Metric: Individual accuracy (%; the 70 MMVP questions on 35 image pairs in the Presence of Specific Features category, free-form answers graded by GPT-4 against the authors' rubric). Source: arxiv.org. 11 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Llama 3.2 90B Vision Instruct | 97.14 | #1749 |
| 2 | Llama 4 Scout | 95.71 | #1776 |
| 3 | GPT-4o | 91.43 | #741 |
| 4 | Llama 4 Maverick | 90 | #1099 |
| 5 | Gemma 3 27B | 90 | #1631 |
| 6 | Qwen 2.5 VL 72B Instruct | 90 | #846 |
| 7 | Gemini 2.5 Pro | 88.57 | #266 |
| 8 | pixtral-large-2411 | 88.57 | #1309 |
| 9 | Claude Sonnet 4 | 87.14 | #400 |
| 10 | Claude Opus 4.1 | 87.14 | #272 |
| 11 | Grok 4 | 82.86 | #307 |
Interactive version: theaggregate.ai/benchmark?slug=amvicc-mmvp-individual-accuracy-presence-of-specific-features · How It Works · Data refreshed daily, snapshot 2026-11-13.