ZoomGainBench - English Attributes: leaderboard

ZoomGainBench - English Attributes: 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 October 2027. 25 models tracked.

Top models

#ModelScoreOverall rank
1GPT-5.6 Sol56.57#29
2GPT-5.552.75#48
3Gemini 3.6 Flash51.27#72
4Kimi K3 (Thinking)49.15#53 (Kimi K3)
5Claude Opus 548.09#20
6Qwen 3.8 27B (Non-reasoning)40.25#199 (Qwen 3.8 27B)
7Qwen 3.5 397B A17B (Non-reasoning)33.47#242 (Qwen 3.5 397B A17B)
8Qwen 3.6 27B (Non-reasoning)33.47#339 (Qwen 3.6 27B)
9Qwen 3.5 27B (Non-reasoning)33.05#358 (Qwen 3.5 27B)
10Qwen 3.5 35B A3B (Non-reasoning)28.18#480 (Qwen 3.5 35B A3B)
11Qwen 3.6 35B A3B (Non-reasoning)26.91#461 (Qwen 3.6 35B A3B)
12GLM-4.6V (Non-reasoning)24.36#698 (GLM-4.6V)
13Qwen 3.5 9B (Non-reasoning)21.82#803 (Qwen 3.5 9B)
14Qwen 3.5 4B (Non-reasoning)21.4#1180 (Qwen 3.5 4B)

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

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