ZoomGainBench - Chinese Overall: leaderboard

ZoomGainBench - Chinese Overall: 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 November 2027. 25 models tracked.

Top models

#ModelScoreOverall rank
1GPT-5.6 Sol60.07#29
2Claude Opus 559.84#20
3Gemini 3.6 Flash58.2#72
4GPT-5.557.06#48
5Kimi K3 (Thinking)48.26#53 (Kimi K3)
6Qwen 3.8 27B (Non-reasoning)45.78#199 (Qwen 3.8 27B)
7Qwen 3.5 397B A17B (Non-reasoning)45.78#242 (Qwen 3.5 397B A17B)
8Qwen 3.6 27B (Non-reasoning)44.07#339 (Qwen 3.6 27B)
9Qwen 3.5 27B (Non-reasoning)43.02#358 (Qwen 3.5 27B)
10Qwen 3.5 35B A3B (Non-reasoning)38.51#480 (Qwen 3.5 35B A3B)
11Qwen 3.6 35B A3B (Non-reasoning)37.54#461 (Qwen 3.6 35B A3B)
12Qwen 3.5 9B (Non-reasoning)33.66#803 (Qwen 3.5 9B)
13Qwen 3.5 4B (Non-reasoning)30.23#1180 (Qwen 3.5 4B)
14GLM-4.6V (Non-reasoning)26.37#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-overall · How It Works · Data refreshed daily, snapshot 2026-11-12.