We ran 16 AI Models on 9,000+ Real Documents. Here's What We Found.

The Intelligent Document Processing (IDP) Leaderboard offers a comprehensive evaluation of various document AI models across three benchmarks—OlmOCR, OmniDocBench, and IDP Core—using over 9,000 real documents to assess tasks like OCR, table extraction, and visual QA. This approach allows users to explore the strengths and weaknesses of different models, such as the cost-effective Nanonets OCR2+ which performs well against more expensive models, particularly for large-scale OCR tasks. Gemini 3.1 Pro leads in reasoning-heavy tasks, outperforming others in Visual QA, while cheaper models like Sonnet 4.6 remain competitive in extraction tasks. The Results Explorer provides hands-on comparisons, allowing users to see model predictions and ground truths to better understand model performance. The platform encourages users to choose models based on specific document needs rather than relying solely on headline accuracy figures, highlighting that while some models excel in structured data, challenges persist with sparse tables and handwritten text. The leaderboard and Results Explorer are designed to provide transparency, enabling informed decisions based on detailed model analysis.

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