BenchLM recommendation
Best Document AI Models in 2026
As of August 22, 2026, the top model in best document ai models on the BenchLM leaderboard is Qwen3.7 Plus with a score of 90.3.
Last verified: August 22, 2026
This reporting page focuses on document workflows: OCR, office artifacts, long documents, and enterprise-style grounded reasoning over PDFs, slides, tables, and screenshots.
This page ranks models using only sourced document AI and OCR benchmarks in the reporting family.
Bottom line: Document AI benchmarks (OfficeQA Pro, OmniDocBench, CC-OCR) are newer and coverage is still building. Check the multimodal leaderboard for broader document understanding.
Qwen3.7 Plus leads this ranking with a score of 90.3, followed by Qwen3.8-27B (89.8) and Qwen3.8 Max (87.3). The top three are separated by just a few points — any of them would perform well for this use case.
The best open-weight option is Qwen3.8-27B (ranked #2 with a score of 89.8). Open-weight models are highly competitive in this category — self-hosting is a viable alternative to proprietary APIs.
This ranking uses provisional overall weighted scores from the active scoring formula. For detailed model profiles, click any model name below. To compare two specific models head-to-head, use the "vs #" links.
How to choose
Full Rankings (6 models)
Key Takeaways
The top model on this sourced reporting-family slice is Qwen3.7 Plus by Alibaba with an average of 90.3.
The best open-weight model is Qwen3.8-27B at position #2.
6 models are listed with sourced benchmark coverage in this reporting family.
Score in Context
What these scores mean
This is a reporting family ranking, not a weighted category. It averages sourced document AI and OCR benchmarks to give a focused view of this capability.
Known limitations
Models must have sourced results on at least a quarter of the benchmarks in this family to be included. Coverage varies — a model with 2 benchmark scores is less reliable than one with 5.
Explore More
Know when it’s worth switching models
The model to choose, the cheaper alternative, and the release we would wait on.
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