Visual Reasoning Benchmark Results for 15 Multimodal AI Models

Benchmark Overview

AIMultiple conducted a visual reasoning benchmark of 15 leading multimodal AI models using 200 visual-based questions. The benchmark was split into two distinct tracks: 100 chart understanding questions focused on data visualization interpretation, and 100 visual logic questions covering pattern recognition and spatial reasoning.

Methodology

Each question was run 5 times to ensure statistical reliability. The benchmark specifically tested models' ability to interpret data visualizations and solve visual logic problems requiring pattern recognition and spatial reasoning.

Results

The overall leaderboard shows Gemini-3.1-pro-preview and Gemini-3-pro-preview leading, followed by GPT-5.2, Kimi-K2.5, and GPT-5.2-pro. The results reveal a consistent pattern across most systems: models perform better on data-driven chart interpretation tasks than on visual logic problems, where performance drops significantly.

For developers working with multimodal AI systems, this benchmark provides concrete data on relative strengths in different types of visual reasoning tasks. The performance gap between chart interpretation and visual logic suggests current models have stronger capabilities in processing structured visual data than in abstract spatial reasoning.