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Document OCR and parsing models turn PDFs, scans, and images into clean, structured text software can use. The catch: a model can nail clean invoices and fall apart on dense tables or handwriting. The 15 picks below are ordered by normalized ParseBench score and practical access tradeoffs.
Best Document OCR & Parsing Models
#ModelBest for
How to Choose
When choosing between these models, consider:- Access: First decide whether you want a hosted app, an API call, or a model you run yourself, because that choice drives cost, privacy, latency, and how much setup you own. Proprietary parsers are the fastest to start; open-weight models keep documents on your own hardware.
- Quality: We use the ParseBench overall score as the main measure. It tests how well parsed output preserves tables, charts, content faithfulness, semantic formatting, and on-page visual grounding across real enterprise documents, not just whether the text looks similar to a reference.
- Price: We compare on USD per 1,000 pages processed, the cleanest way to line up hosted parsers. Self-hosted open-weight models carry no per-page fee, but you pay in hardware and setup instead.
- Parser Type: The field splits into specialized parser APIs, general VLM APIs, open-weight VLMs, and cloud OCR APIs. Specialized parsers and open-weight VLMs lead on hard layouts, cloud OCR APIs stay steady on clean structured forms, and general VLMs add reasoning but are not purpose-built.
Other Models We Considered
Frequently Asked Questions
What is the best document OCR and parsing model right now?
What is the best document OCR and parsing model right now?
LlamaParse in its agentic mode is the strongest all-round pick. It reconstructs complex layouts into clean, RAG-ready Markdown more reliably than anything else, and it is available as a simple API. If you need every extracted value to carry a citation for audit, Reducto is the more specialized choice.
What is the best document parser for most people?
What is the best document parser for most people?
For a hosted default, LlamaParse is the safest starting point. If you are processing large volumes and want to keep costs down, Mistral OCR 4 or Datalab Parser give you most of the quality for far less per page. Test two or three on your own documents before committing.
What is the best open-weight parser you can run locally?
What is the best open-weight parser you can run locally?
MinerU2.5-Pro and PaddleOCR-VL are the standouts because they run well on a typical machine, MinerU for technical documents and PaddleOCR-VL for multilingual work. Surya OCR 2 is the lightest option, even on CPU or Apple Silicon. KDL-Frontier-Parser-nano, Infinity-Parser2-Pro, and Chandra OCR 2 score higher but need a high-end GPU.
Should I use a general model like GPT-5.5 or Gemini 3 Flash, or a dedicated parser?
Should I use a general model like GPT-5.5 or Gemini 3 Flash, or a dedicated parser?
Use a dedicated parser when parsing is the whole job, since purpose-built models handle hard tables and dense layouts more reliably. Reach for a general VLM when parsing is one step inside a larger reasoning or agent task and you want everything in a single call.
Do these benchmark scores match real-world use?
Do these benchmark scores match real-world use?
Roughly, but not perfectly. Scores predict which models handle complex layouts, tables, and faithfulness well, yet performance swings with your specific document types, scan quality, and languages. Even the best parsers miss or invent content on a small share of pages, so verify critical fields and run a short test on your own files.
What should I use instead of AWS Textract or Azure Document Intelligence?
What should I use instead of AWS Textract or Azure Document Intelligence?
They are still fine for clean, structured forms and key-value extraction. But if you need semantic, RAG-ready output from messy or complex documents, a VLM parser like LlamaParse, Gemini 3 Flash, or an open-weight model like MinerU2.5-Pro will serve you much better.
What matters most when choosing a model for document parsing?
What matters most when choosing a model for document parsing?
Three things: how you want to access it (app, API, or self-hosted), the kind of documents you actually process, and your tolerance for cost versus accuracy. Match the model to your hardest real documents, not the cleanest ones, because that is where the differences show up.