VeriOCRBench - Contextual Dimension (Proactive): leaderboard

The contextual verification dimension of VeriOCRBench (Zhou et al., 2026): 400 OCR-grounded tasks with the traps domain mismatch and hallucinatory target, where the provided context imposes a wrong domain on the image or refers to a target that does not exist in it. Diagnosis accuracy is the percentage on which the model both rejects the task and names the correct trap mechanism, judged by GPT-4o; higher is better. Proactive setting: the standard prompt, with no instruction to verify the task.

Metric: Diagnosis accuracy (%; the 400 trap-injected tasks of the contextual verification dimension (domain mismatch and hallucinatory target: the provided context imposes a wrong domain on the image or refers to a target that does not exist in it); the model must reject the task and name the ground-truth trap mechanism; judged by GPT-4o at temperature 0; proactive paradigm: no verification instruction). Source: arxiv.org. Saturation forecast: Estimated already saturated. 15 models tracked.

Top models

#ModelScoreOverall rank
1Claude Sonnet 4.586.75#244
2Qwen 3.5 397B A17B60.75#242
3Qwen 3.5 35B A3B60.75#480
4Gemini 3.1 Pro (Preview)56.25#91
5Qwen 3.5 122B A10B55.25#329
6Llama 4 Scout46#1787
7Llama 4 Maverick34.75#1105
8InternVL3-38B28.75#905
9GPT-4o22.5#741
10GPT-518.5#163

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

Interactive version: theaggregate.ai/benchmark?slug=veriocrbench-contextual-dimension-proactive · How It Works · Data refreshed daily, snapshot 2026-11-12.