OCR-MetaReasoning - Field Dependency: leaderboard

OCR-MetaReasoning answer score on the field dependency OCR-object category of its text-rich image questions (arXiv 2608.30678v1, Table 2). Percentage (100 times the normalized score), higher is better; reasoning modes follow the paper's Table 24.

Metric: Answer Score (%). Source: arxiv.org. Saturation forecast: Estimated already saturated. 18 models tracked.

Top models

#ModelScoreOverall rank
1Gemini 3.1 Pro (Preview) (Thinking)91.7#64 (Gemini 3.1 Pro (Preview))
2GPT-5.4 (Medium)90#83 (GPT-5.4)
3Claude Sonnet 4.6 (Thinking)89.1#94 (Claude Sonnet 4.6)
4GPT-5.4 (Non-reasoning)88#83 (GPT-5.4)
5GPT-5.4 Mini (Medium)87.2#228 (GPT-5.4 Mini)
6Qwen 3 VL 235B A22B (Thinking)82.7#236
7Kimi K2.5 (Non-reasoning)81.2#152 (Kimi K2.5)
8Qwen 3 VL 235B A22B Instruct80.7#280
9Grok 4.20 (Non-reasoning)79.8#179 (Grok 4.20)
10GLM-4.6V (Reasoning)79.3#330 (GLM-4.6V)
11Qwen 3 VL 30B A3B (Thinking)77.8#348
12Qwen 3 VL 8B (Thinking)77.3#423
13GPT-5.4 Mini (Non-reasoning)76.9#228 (GPT-5.4 Mini)
14Qwen 3 VL 30B A3B Instruct67#390
15Qwen 3 VL 8B Instruct62.4#419

No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.

Interactive version: theaggregate.ai/benchmark?slug=ocr-metareasoning-field-dependency · How It Works · Data refreshed daily, snapshot 2026-11-05.