MMGist - Spatial Understanding: leaderboard

MMGist (2026) is a curated multimodal benchmark: 7,262 items kept from 18 vision-language benchmarks after dropping items that can be answered without the image, items nearly all models solve, and items with faulty labels. This board is accuracy in percent on the Spatial Understanding items, drawn from EmbSpatialBench and CountBench; higher is better.

Metric: Accuracy (%) on the Spatial Understanding dimension (items kept from EmbSpatialBench and CountBench); MMGist: 7,262 curated items from 18 vision-language benchmarks after removing items answerable without the image, items nearly every model solves and items with faulty labels; eight samples per item at temperature 1.0, step-by-step answer in a boxed block, 16,384 output tokens, medium effort where configurable; higher is better. Source: arxiv.org. Saturation forecast: Around February 2027. 27 models tracked.

Top models

#ModelScoreOverall rank
1Gemini 3.1 Pro (Preview) (Medium)79.7#94 (Gemini 3.1 Pro (Preview))
2Qwen 3.6 Plus79.3#222
3Qwen 3.6 35B A3B77.2#462
4Seed 2.0 Pro74.9#211
5Seed 2.0 Lite74.8#289
6Gemma 4 31B73.7#394
7Qwen 3.5 9B73.5#803
8Seed 2.0 Mini72#508
9Gemma 4 26B A4B70#615
10Qwen 3.5 4B68.7#1175
11Gemini 3.1 Flash Lite (Medium)68.2#364 (Gemini 3.1 Flash Lite)
12Qwen 3.5 27B67.8#359
13GPT-5 (Medium)63.6#167 (GPT-5)
14GPT-5 Mini (Medium)60.5#348 (GPT-5 Mini)
15Claude Sonnet 4.6 (Medium)58.2#161 (Claude Sonnet 4.6)

No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.

Interactive version: theaggregate.ai/benchmark?slug=mmgist-spatial-understanding · How It Works · Data refreshed daily, snapshot 2026-11-13.