ZoomGainBench - Chinese OCR: leaderboard

ZoomGainBench - Chinese OCR: Fine-grained perception accuracy (%). ZoomGain arXiv 2611.14076v1: ZoomGainBench bilingual fine-grained perception scores, weighted within-language by category counts. Table 3 author experiments train Qwen3.5-4B and 9B on 9K ZoomGain samples; official inclusionAI release identifies them as ZoomGain-4B/9B. ZwZ-retrained controls do not identify released checkpoints and are held. Table 3 Gemini-3-Flash (51.27) conflicts with Table 2 Gemini-3.6-Flash; that row is held, never inferred equivalent. Qwen3-VL labels are held absent Instruct/Thinking binding. Other source-named released models retain their exact labels and unreported effort. Appendix F Table 2 settings are retained per observation: native temperature, output budgets, thinking flags or question suffix, and agentic turn/tool-call limits. Provider-default temperature and thinking remain unspecified where the source gives no value.

Metric: Fine-grained perception accuracy (%). Source: arxiv.org. Saturation forecast: Around May 2027. 25 models tracked.

Top models

#ModelScoreOverall rank
1Gemini 3.6 Flash71.42#72
2Claude Opus 571.29#20
3GPT-5.6 Sol65.84#29
4GPT-5.560.96#48
5Qwen 3.5 397B A17B (Non-reasoning)50.41#242 (Qwen 3.5 397B A17B)
6Qwen 3.5 27B (Non-reasoning)48.67#358 (Qwen 3.5 27B)
7Qwen 3.5 35B A3B (Non-reasoning)47.95#480 (Qwen 3.5 35B A3B)
8Kimi K3 (Thinking)46.22#53 (Kimi K3)
9Qwen 3.6 35B A3B (Non-reasoning)46.17#461 (Qwen 3.6 35B A3B)
10Qwen 3.6 27B (Non-reasoning)45.33#339 (Qwen 3.6 27B)
11Qwen 3.8 27B (Non-reasoning)43.05#199 (Qwen 3.8 27B)
12Qwen 3.5 9B (Non-reasoning)42.52#803 (Qwen 3.5 9B)
13Qwen 3.5 4B (Non-reasoning)34.87#1180 (Qwen 3.5 4B)
14GLM-4.6V (Non-reasoning)28.51#698 (GLM-4.6V)

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

Interactive version: theaggregate.ai/benchmark?slug=zoomgainbench-chinese-ocr · How It Works · Data refreshed daily, snapshot 2026-11-12.