HoloCount - Categorical Cardinality: leaderboard

Counting how many distinct kinds of object are present, such as kinds of fruit: a subset of HoloCount (Meituan, 2026), a visual counting benchmark for multimodal LLMs. Models answer with a single integer, zero-shot; exact-match accuracy in percent, higher is better.

Metric: Accuracy (%; exact-match accuracy of the single integer count a multimodal model returns zero-shot for an image and a counting question, with a strict integer-only system prompt; counting the number of distinct object kinds present, such as kinds of fruit (101 questions); higher is better). Source: arxiv.org. Saturation forecast: Around February 2027. 30 models tracked.

Top models

#ModelScoreOverall rank
1Kimi K2.689.1#178
2Claude Opus 4.784.2#79
3Gemini 2.5 Pro82.2#266
4Kimi K2.582.2#256
5Claude Opus 4.882.2#56
6Qwen 3.5 35B A3B82.2#482
7Qwen 3.5 122B A10B82.2#331
8GPT-5.581.2#49
9Qwen 3.5 9B81.2#803
10Gemini 3 Flash (Preview)81.2#144
11Qwen 3.5 397B A17B80.2#246
12Qwen 3.5 27B80.2#359
13Qwen 3.5 4B80.2#1175
14GPT-4.177.2#495
15MiniMax-M375.2#269

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

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