HoloCount - Visual Distractor Illusion: leaderboard

Counting real objects among look-alike distractors, such as real and plastic 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 real objects among look-alike distractors such as plastic fruit (200 questions); higher is better). Source: arxiv.org. Saturation forecast: Not forecast. 30 models tracked.

Top models

#ModelScoreOverall rank
1Qwen 3.5 397B A17B80#246
2Kimi K2.678.5#178
3Qwen 3.5 122B A10B78#331
4Qwen 3.5 35B A3B77.5#482
5Qwen 3.5 9B76.5#803
6GPT-5.576#49
7Kimi K2.573.5#256
8Claude Opus 4.873.5#56
9GPT-4.173#495
10Qwen 3.5 27B73#359
11Gemini 2.5 Pro71#266
12Qwen 3 VL 32B Instruct71#585
13Claude Opus 4.770.5#79
14Qwen 3.5 4B70.5#1175
15MiniMax-M369#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-visual-distractor-illusion · How It Works · Data refreshed daily, snapshot 2026-11-13.