Model comparison
Compare Mistral OCR 4.1 and Google Gemini Layout Parser 1.6 using the same provider-sourced document ai rubric. No mystery score and no invented benchmark ranking.
Facts checked September 4, 2026
Set your usage. Your estimate updates as you type.
Counts pages, not files. A 10-page PDF counts as 10 pages. The estimate uses the document-processing rate available for each model.
Estimates exclude taxes, tools, cache storage/writes, free allowances and custom discounts. Image estimates cover output only, not prompt or reference-image charges. Quality modes differ by model. Unlisted settings are not treated as free.
| Model | Access | Estimated total (USD) |
|---|---|---|
| Mistral OCR 4.1Mistral AI | Mistral AI | $0.04 |
| Google Gemini Layout Parser 1.6Google Cloud | Google Cloud | $0.10 |
Quick take
Mistral's current Document AI OCR model for preserving reading order, tables, structure, locations, and confidence across multilingual files.
Do not assume the regional chat endpoints support every OCR feature. Verify the exact endpoint, retention mode, page limits, and structured-annotation cost for production.
Google Cloud's Gemini-assisted parser for preserving document hierarchy and creating context-rich chunks for enterprise search and RAG.
Version 1.6 is a release candidate and uses a global Gemini endpoint. Google explicitly says it does not satisfy its normal data-residency standard, even when the request enters through a US or EU location.
Compare the published facts
Values use each provider's own published units and limits. A blank means the provider did not publish a directly comparable value in the sources reviewed.
| Document AI | Mistral OCR 4.1 | Google Gemini Layout Parser 1.6 |
|---|---|---|
| Price per pageA current provider rate and the exact page or annotation unit it covers. | $4 / 1K OCR pages · $5 / 1K annotated | $10 / 1K pages, including initial chunking |
| Accepted documentsThe file and document types the listed endpoint is designed to process. | PDF, DOCX, PPTX, PNG, JPEG, AVIF and more | PDF, HTML, DOCX, PPTX, XLSX, XLSM |
| What you get backMarkdown, tables, chunks, fields, descriptions, or another machine-readable result. | Markdown, separate Markdown/HTML tables, structured annotations | DocumentLayout hierarchy, contextual chunks, table/figure descriptions |
| Layout & confidenceWhether the output preserves boxes, hierarchy, labels, reading order, or confidence information. | Paragraph boxes, block labels, page/block/word confidence | OCR-grounded headings, tables, figures, lists, headers and footers |
| Language coverageProvider-documented language support without inferring unlisted languages. | 40+ documented | Not enumerated for this version |
| Where it runsDirect API, batch service, private environment, marketplace, or regional cloud route. | Mistral OCR API and batch; verify regional endpoint support | Google Cloud Document AI; global Gemini endpoint |
How to choose
Start with the job you need to complete, then validate cost, access, and policy details on your exact provider route.
Mistral's commercial terms exclude model training by default except opt-in and preview exceptions. Standard API input and output retention is generally 30 rolling days unless zero data retention is enabled.
Provider and API links
Google says Document AI customer documents and predictions are not used to train its models. Online documents are processed in memory without disk persistence; batch inputs have a failsafe retention of up to one day.
Provider and API links
Frequently asked questions
Mistral OCR 4.1: Mistral's current Document AI OCR model for preserving reading order, tables, structure, locations, and confidence across multilingual files. Google Gemini Layout Parser 1.6: Google Cloud's Gemini-assisted parser for preserving document hierarchy and creating context-rich chunks for enterprise search and RAG.
Consider Mistral OCR 4.1 when your priority is Layout-aware OCR for RAG. Consider Google Gemini Layout Parser 1.6 when your priority is RAG over reports, manuals, and filings. Test both with your own data and provider route before committing.
No. This comparison aligns provider-published facts for the Document AI category. It does not claim a universal winner or combine incompatible third-party benchmark scores.
