Top picks for OCR / Document Parsing (2026)
Reading text out of images, PDFs, and scanned documents. Ranked from 422 live models on the OpenRouter catalog, weighted for vision input, structured output, context window.
Updated 2026-09-04 · prices checked at this morning's rebuild
Which should you use? Anthropic: Claude Opus 4.7 (batch) tops this ranking on blended score. If cost drives the decision, OpenAI: GPT-5.4 (batch) is the cheapest of the leaders at $1.25/M input.
| # | Model | Score | In / 1M | Out / 1M | Context | |
|---|---|---|---|---|---|---|
| 1 | Anthropic: Claude Opus 4.7 (batch)anthropic/claude-opus-4.7:batch | 154 | $2.50 | $12.50 | 1,000,000 | Details → |
| 2 | Anthropic: Claude Sonnet 4.6anthropic/claude-sonnet-4.6 | 153 | $3.00 | $15.00 | 1,000,000 | Details → |
| 3 | Anthropic: Claude Sonnet 4.6 (batch)anthropic/claude-sonnet-4.6:batch | 153 | $1.50 | $7.50 | 1,000,000 | Details → |
| 4 | Anthropic: Claude Opus 4.8 (batch)anthropic/claude-opus-4.8:batch | 149 | $2.50 | $12.50 | 1,000,000 | Details → |
| 5 | OpenAI: GPT-5.5 (batch)openai/gpt-5.5:batch | 149 | $2.50 | $15.00 | 1,050,000 | Details → |
| 6 | Anthropic: Claude Opus 4.7anthropic/claude-opus-4.7 | 149 | $5.00 | $25.00 | 1,000,000 | Details → |
| 7 | OpenAI: GPT-5.4openai/gpt-5.4 | 149 | $2.50 | $15.00 | 1,050,000 | Details → |
| 8 | OpenAI: GPT-5.4 (batch)openai/gpt-5.4:batch | 149 | $1.25 | $7.50 | 1,050,000 | Details → |
| 9 | Meta: Muse Spark 1.3meta/muse-spark-1.3 | 148 | $1.25 | $4.25 | 1,048,576 | Details → |
| 10 | Claude Opus 5 (batch)anthropic/claude-opus-5:batch | 148 | $2.50 | $12.50 | 1,000,000 | Details → |
| 11 | SpaceXAI: Grok 4.6x-ai/grok-4.6 | 147 | $2.00 | $6.00 | 500,000 | Details → |
| 12 | OpenAI: GPT-5.6 Solopenai/gpt-5.6-sol | 147 | $2.00 | $10.00 | 1,050,000 | Details → |
| 13 | OpenAI: GPT-5.6 Sol (batch)openai/gpt-5.6-sol:batch | 147 | $1.00 | $5.00 | 1,050,000 | Details → |
| 14 | Google: Gemini 3.1 Pro Previewgoogle/gemini-3.1-pro-preview | 147 | $2.00 | $12.00 | 1,048,576 | Details → |
| 15 | Google: Gemini 3.1 Pro Preview (batch)google/gemini-3.1-pro-preview:batch | 147 | $1.00 | $6.00 | 1,048,576 | Details → |
How we ranked these
For OCR / Document Parsing, we weight models on vision input, structured output, context window. Scores combine each model's public specs with independent benchmark results (Aider Polyglot coding scores, Artificial Analysis intelligence/coding/agentic indices) and live pricing. See full methodology →
About OCR / Document Parsing
OCR (Optical Character Recognition) and document parsing extract readable text from images, PDFs, and scanned documents. You need this when source material exists only as visual files but your downstream workflow requires structured, machine-readable text. Good models handle skewed pages, poor lighting, handwriting, and mixed layouts (tables, multi-column text, graphics). Bad models fail on degraded scans, non-Latin scripts, or documents with complex formatting. The key tradeoff: cloud-based models (Claude with vision, GPT-4V) cost per image and require network calls, while local models like PaddleOCR are free but need GPU resources and handle fewer edge cases.
When to use: Use this when you have physical documents, scanned papers, screenshots, or PDFs that need to become searchable text or structured data for downstream processing.
Common questions
What is the difference between basic OCR and document parsing?
Basic OCR extracts raw text from an image with minimal structure. Document parsing goes further: it identifies layout elements (headers, tables, page numbers), segments content into logical blocks, and outputs structured formats like JSON or markdown. Modern models like Claude 3.5 Sonnet and GPT-4V do both simultaneously, returning text plus positional metadata.
How much does it cost to OCR a large batch of documents?
Cloud vision APIs typically charge $0.001 to $0.05 per image depending on resolution and model. A 10,000-page batch runs $10-500. Local models like Tesseract or PaddleOCR cost zero per image but require upfront infrastructure and are slower on CPU. For high-volume, low-accuracy-tolerance work, local is cheaper; for complex documents where accuracy matters, cloud APIs justify the cost.