Best document parsing and OCR for RAG

Combined ranking

1. 1
   LlamaParse—17 pts

   GPT #1Claude #4Gemini #1Grok #1

   Best RAG-native balance of PDF parsing, layout preservation, tables, images, markdown output, chunking ergonomics, and developer workflow around LlamaIndex

   To stay #1 Improve raw OCR accuracy and speed on messy scans enough to beat specialist OCR engines

2. 2
   Mistral OCRincumbentnew11 pts

   GPT #2Claude #2Gemini #3Grok —

   Excellent document-to-markdown quality, strong multilingual OCR, good tables/images/math handling, fast API, and strong fit for multimodal RAG pipelines

   To rank higher Add more mature RAG ingestion controls like deterministic chunking, metadata schemas, and enterprise document workflows

3. 3
   Doclingincumbent▼19 pts

   GPT —Claude #5Gemini #2Grok #2

   Best-in-class open-source, local-first parser with high-fidelity layout analysis, hierarchical document structuring, and excellent preservation of tables.

   To rank higher Optimize CPU processing speeds to reduce the computational overhead when running on non-GPU instances.

4. 4
   Reducto▼16 pts

   GPT #3Claude —Gemini —Grok #3

   Very strong modern parser for complex PDFs, tables, forms, layout, and structured extraction with output that is easy to feed into retrieval systems

   To rank higher Prove itself at larger enterprise scale with broader integrations, compliance posture, and long-run reliability

5. 5
   Azure AI Document Intelligenceincumbentnew5 pts

   GPT #4Claude #3Gemini —Grok —

   the enterprise workhorse — prebuilt and custom layout models, reliable tables/key-value extraction, compliance certifications, VNET/on-prem containers, and now markdown output mode built for RAG

   To rank higher simplify its dated model/SKU sprawl and pricing into one modern layout-to-markdown API that matches LLM-parser ergonomics

6. 6
   Google Geminiincumbentnew5 pts

   GPT —Claude #1Gemini —Grok —

   vision-LLM parsing has overtaken legacy OCR for RAG — best accuracy on messy scans, handwriting, and complex layouts, native markdown/structured output, huge context for whole-document coherence, and per-page cost far below dedicated OCR APIs

   To rank higher provide deterministic, grounded output with bounding boxes and hallucination guarantees so regulated pipelines can trust it without a verification layer

7. 7
   Unstructured▼34 pts

   GPT #5Claude —Gemini #5Grok #4

   Broadest format (60+) and connector support combined with semantic element classification and purpose-built RAG chunking strategies, making it the most complete production ETL platform for diverse enterprise ingestion pipelines.

   To rank higher Close the raw extraction accuracy gap on intricate layouts and tables where newer VLM-native tools now lead.

8. 8
   MMarkernew2 pts

   GPT —Claude —Gemini #4Grok —

   Extremely fast, GPU-accelerated batch parsing optimized specifically for converting large PDF archives into clean markdown and LaTeX formulas.

   To rank higher Improve accuracy on low-contrast scanned documents and handwritten annotations.

9. 9
   Google Document AIincumbent▼41 pts

   GPT —Claude —Gemini —Grok #5

   Enterprise-grade OCR across 200+ languages with Gemini-powered context/intent understanding, custom processors, strong compliance, and scalability proven for large-scale scanned/structured document RAG at global organizations.

   To rank higher Default outputs to richer hierarchical Markdown/LLM-native structures instead of primarily JSON that requires heavy downstream transformation for optimal RAG use.

Rank history

By model

Tracked by ModelsAgree · rank 1 = 5 pts … rank 5 = 1 pt · re-polled continuously