Introducing Mistral OCR 4

Thinking

Summary

Mistral OCR 4 introduces breakthrough document parsing with bounding boxes, block classification, and inline confidence scores, supporting 170 languages and running in a single container for self-hosted deployments. It outperforms leading OCR systems in human evaluations and benchmarks while offering cost-efficient, high-throughput processing for enterprise search, RAG, and agentic workflows. Available via API or Document AI, it provides structured outputs for custom pipelines or no-code applications, with self-hosting options for data privacy.

Today, we're releasing Mistral OCR 4, featuring bounding boxes, block classification, and inline confidence scores alongside extracted text. The model supports 170 languages across 10 language groups, runs in a single container for fully self-hosted deployments, and serves as an ingestion component for enterprise search, RAG, and domain-specific retrieval pipelines. OCR 4 is a small, focused model, and this post covers what's new, how it performs on public and internal benchmarks, the known limitations of those benchmarks, and guidance on when to use the model API versus Document AI.

Highlights

Overview

Mistral OCR 4 extracts and structures content from a wide range of documents. Where previous generations focused on converting a page into clean text and tables, OCR 4 returns a structured representation of the document. Each block is localized with a bounding box, classified by type, and inline confidence scores are generated per-page and per-word. Downstream systems, therefore, have access not only to what the document says but also to where each element sits, what role it plays, and how confident the model is in each region.

This structure supports several downstream workloads:

Benchmarks

To evaluate OCR 4, we compared it against leading AI-native OCR models, frontier general-purpose models, enterprise document services, and our own Mistral OCR 3.