AMVICC - MMVP Pair 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 pair accuracy in percent on the 35 image pairs of MMVP's Presence of Specific Features category, where a pair counts only when both of its questions are answered correctly; higher is better.
Metric: Pair accuracy (%; the 35 MMVP image pairs in the Presence of Specific Features category, a pair credited only when both of its questions are answered correctly; 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 | 94.29 | #1749 |
| 2 | Llama 4 Scout | 91.43 | #1776 |
| 3 | GPT-4o | 82.86 | #741 |
| 4 | Llama 4 Maverick | 80 | #1099 |
| 5 | Gemma 3 27B | 80 | #1631 |
| 6 | Qwen 2.5 VL 72B Instruct | 80 | #846 |
| 7 | Gemini 2.5 Pro | 77.14 | #266 |
| 8 | pixtral-large-2411 | 77.14 | #1309 |
| 9 | Claude Sonnet 4 | 74.29 | #400 |
| 10 | Claude Opus 4.1 | 74.29 | #272 |
| 11 | Grok 4 | 71.43 | #307 |
Interactive version: theaggregate.ai/benchmark?slug=amvicc-mmvp-pair-accuracy-presence-of-specific-features · How It Works · Data refreshed daily, snapshot 2026-11-13.