Well-funded French AI startup Mistral is content to go its own way.

In a sea of competing reasoning models, the company has introduced Mistral OCR, a new optical character recognition (OCR) API designed to provide advanced document understanding capabilities.

The API extracts content — including handwritten notes, typed text, images, tables and equations — from unstructured PDFs and images with high accuracy, presenting in a structured format.

Structured data is information that is organized in a predefined manner, typically using rows and columns, making it easy to search and analyze. Common examples include names, addresses and financial transactions stored in databases or spreadsheets. 

By contrast, unstructured data lacks a specific format or structure, making it more challenging to process and analyze. This category encompasses a wide range of data types, such as emails, social media posts, videos, images and audio files. Since unstructured data doesn’t fit neatly into traditional databases, specialized tools and techniques, like natural language processing (NLP) and machine learning (ML), are often employed to extract meaningful insights. 

Understanding the distinction between these data types is crucial for businesses looking to effectively manage and leverage their information assets.

With multilingual support, fast processing speeds and integration with large language models (LLMs) for document understanding, Mistral OCR is positioned to assist organizations in making their documentation AI-ready.

Given that — according to Mistral’s blog post announcing the new API — 90% of all business information is unstructured, the new API should be a huge boon to organizations seeking to digitize and catalog their data for use in AI applications or internal/external knowledge bases.

Mistral sets a new gold standard for OCR

Mistral OCR aims to improve how organizations process and analyze complex documents.

Unlike traditional OCR solutions that primarily focus on text extraction, Mistral OCR is designed to interpret various document typographical elements and characters, including tables, mathematical expressions and interleaved images, while maintaining structured outputs.

According to Mistral’s chief science officer Guillaume Lample, this technology represents a significant step toward wider AI adoption in enterprises, particularly for companies seeking to simplify access to their internal documentation.

The API is already integrated into Le Chat, which millions of users rely on for document processing.

Now, developers and businesses can access the model via la Plateforme, Mistral’s developer suite.

The API is also expected to become available through cloud and inference partners and will offer on-premises deployment for organizations with high-security requirements.

Advancing an early (70-year-old) computing technology

OCR technology has played a significant role in automating data extraction and document digitization for decades. The first commercial OCR machine was developed in the 1950s by David Shepard and his colleagues Harvey and William Lawless Jr., who founded Intelligent Machines Research Co. (IMR) to bring the technology to market.

The system gained traction when Reader’s Digest became its first major customer, followed by banks, telecom companies like AT&T and major oil firms.

In 1959, IBM licensed IMR’s patents and introduced its own OCR machine, formalizing the term as the industry standard.

Since then, OCR technology has continued to evolve, incorporating AI and ML to improve accuracy, expand language support and handle increasingly complex document formats, and can be found in such leading enterprise software as PDF reader Adobe Acrobat.

Mistral OCR represents the next step in this evolution, as it leverages AI to enhance document comprehension beyond simple text recognition.

Benchmarks show the power of Mistral OCR

Mistral highlights its OCR’s competitive edge over existing tools, citing benchmark tests where it outperformed major alternatives including Google Document AI, Azure OCR and OpenAI’s GPT-4o.

The model achieved the highest accuracy scores in math recognition, scanned documents and multilingual text processing.

Mistral OCR is also designed to operate faster than competing models and is capable of processing up to 2,000 pages per minute on a single node.

This speed advantage makes it suitable for high-volume document processing in industries such as research, customer service and historical preservation.

Sophia Yang, head of developer relations at Mistral, has been actively showcasing the OCR capabilities on her X account. Notably, she highlighted its top-tier performance benchmarks, multilingual support and ability to accurately extract mathematical equations from PDFs.

In a recent post, she shared an example of Mistral OCR successfully recognizing and formatting complex mathematical expressions, reinforcing its effectiveness for scientific and academic applications.

Key features and use cases

Mistral OCR introduces several features that make it a versatile tool for businesses and institutions handling large document repositories:

• Multilingual and multimodal processing: The model supports a wide range of languages, scripts and document layouts, making it useful for global organizations. Yang emphasized this capability, calling it a game-changer for multilingual document processing.
• Structured output and document hierarchy preservation: Unlike basic OCR models, Mistral OCR retains formatting elements such as headers, paragraphs, lists and tables, ensuring extracted text is more useful for downstream applications.
• Document-as-prompt and structured outputs: Users can extract specific content and format it in structured outputs, such as JSON or Markdown, enabling integration with other AI-driven workflows.
• Self-hosting option: Organizations with stringent data security and compliance requirements can deploy Mistral OCR within their own infrastructure.

The Mistral AI developer documentation online also highlights document understanding capabilities that go beyond OCR. After extracting text and structure, Mistral OCR integrates with LLMs, allowing users to interact with document content using natural language queries. This feature enables:

• Question answering about specific document content;
• Automated information extraction and summarization;
• Comparative analysis across multiple documents;
• Context-aware responses that consider the full document.

What enterprise decision makers should know about Mistral OCR

For CEOs, CIOs, CTOs, IT managers and team leaders, Mistral OCR presents significant opportunities for efficiency, security and scalability in document-driven workflows.

1. Increased efficiency and cost savings

By automating document processing and reducing manual data entry, Mistral OCR cuts down on administrative overhead and streamlines operations. Organizations can process large volumes of documents faster and with higher accuracy, reducing the need for human intervention. This is particularly valuable for industries like finance, healthcare, legal and compliance, where extensive paperwork is a bottleneck.

2. Enhanced decision-making with AI-driven insights

Mistral OCR’s document understanding capabilities allow decision-makers to extract actionable insights from reports, contracts, financial documents and research papers. IT leaders can integrate the API into business intelligence platforms, enabling AI-assisted document analysis that supports faster, data-driven decision-making.

3. Improved data security and compliance

With an on-premises deployment option, Mistral OCR meets the security and compliance needs of enterprises handling sensitive or classified data. CIOs and compliance officers can ensure that proprietary information remains within internal infrastructure while leveraging AI for document processing.

4. Seamless integration with enterprise workflows

CTOs and IT managers can integrate Mistral OCR with existing enterprise systems, including content management platforms, CRM software, legal tech solutions and AI-driven assistants. The API’s support for structured outputs (JSON, Markdown) makes it easy to automate document-based workflows, improving overall productivity.

5. Competitive advantage through AI-driven innovation

For organizations looking to stay ahead in digital transformation, Mistral OCR offers a scalable AI-powered solution for making vast document repositories more accessible. By leveraging AI for information extraction, enterprises can enhance customer experiences, optimize internal knowledge bases and reduce operational inefficiencies.

Pricing and availability

Mistral OCR is priced at 1,000 pages per $1, with batch inference offering 2,000 pages per $1.

The API is available now on la Plateforme, and Mistral plans expansion to cloud and inference partners in the near future. The model is also free to try on Mistral’s website Le Chat, a conversational chatbot powered by its LLMs similar to and rivalrous of OpenAI’s ChatGPT, allowing users to test its capabilities before integrating it into their workflows. Mistral AI expects to make continued improvements to the model based on user feedback in the coming weeks.

When I briefly tested it on a short handwritten (and messy) note on a scrap of paper, it provided an accurate, structured text line back within less than one second.

What’s next?

With Mistral OCR, Mistral AI continues to expand its suite of AI-driven tools, targeting enterprises that require high-performance document processing solutions.

By integrating OCR with AI-powered document understanding, Mistral enables businesses to extract, analyze and interact with their documents in more intelligent ways.

Enterprise leaders, developers and IT teams can explore Mistral OCR through la Plateforme or request on-premises deployment for specialized use cases.

Developers can also check out Mistral AI’s documentation to get started with mistral-ocr-latest.