DiCoBench - Category Commonality: leaderboard

Accuracy on the category-commonality task (matching objects of the same category under changed appearance) of DiCoBench (2026), a fine-grained multi-image perception benchmark of five-option multiple-choice questions on near-2K images, with a no-visible-cue option among the choices. Chance is 20 percent; higher is better.

Metric: Accuracy (%) on the category-commonality task (match objects of the same category under changed appearance) of DiCoBench (765 multi-image samples at near-2K resolution): 5-option multiple choice (four candidate image pairs or instructions plus a no-visible-commonality option, chance 20%), answer letter matched exactly, VLMEvalKit settings at temperature 0; higher is better. Source: arxiv.org. Saturation forecast: Around April 2027. 18 models tracked.

Top models

#ModelScoreOverall rank
1Gemini 3 Flash63.6#164
2Gemini 3 Pro63.6#137
3GPT-4.1 Mini60.9#779
4O4 Mini60.9#353
5Qwen 2.5 VL 7B Instruct59.1#1833
6Qwen 3.5 35B A3B59.1#482
7GPT-553.6#167
8Qwen 2.5 VL 32B Instruct48.2#1053
9Gemma 3 27B (IT)44.6#1289
10Gemma 3 12B (IT)39.1#1837
11GPT-4.126.4#495
12GPT-4o24.6#741

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

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