Best LLM for Document AI & Processing (2026)
Document AI covers extraction, classification, summarization, and question-answering over structured and unstructured documents — PDFs, contracts, invoices, research papers, and more. The best models combine long-context handling, vision capability (for scanned documents), and reliable structured output.
Top picks for document ai
Best-in-class instruction following for complex extraction schemas. 200K context handles large documents. Vision capability processes scanned PDFs. Highest accuracy on nuanced document tasks.
1M-token context processes entire document repositories. Native vision handles mixed text/image documents. Strong structured extraction with JSON schema support.
$0.15/1M input with 1M-token context and vision. For high-volume document pipelines (invoices, receipts, forms), the cost advantage over frontier models is 5–10×.
Reliable JSON schema adherence and structured outputs. Mature ecosystem with extensive document processing libraries. Best for teams already on OpenAI.
Model comparison — document ai
0 models| # | Model | Provider | Tier | Intelligence | MMLU | GPQA | Input/1M | Context | Confidence |
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Which model for which task?
Legal documents require careful reading and nuanced comprehension. Claude Opus 4.5's instruction following and long-context performance are best-in-class for complex legal extraction.
High-volume, structured extraction from standardized documents. Both models handle vision + structured output reliably at low cost. Gemini 2.5 Flash is cheaper; GPT-4o mini has a larger ecosystem.
Both handle long academic papers with strong comprehension. Gemini 2.5 Pro for very long papers (>100 pages); Claude Sonnet 4.5 for better narrative summarization quality.
Both have strong vision capabilities for scanned documents. Gemini 2.5 Pro handles larger documents; GPT-4o has more mature vision APIs and better ecosystem support.