Is the Mistral OCR 3 the best OCR model?

Getting text in a messy PDF file is more trouble than helpful. The problem is not in the ability to transform pixels to text, but rather in preserving the structure of the document. Tables, headings and figures should be in the correct order. Using Mistral OCR 3 is no longer about converting text, but about producing commercially usable information. A new AI document extraction tool will be designed to improve complex file extraction.

This guide covers the Mistral OCR 3. We’ll also discuss its new features and how to use them, and conclude with a comparison to the open-weight DeepSeek-OCR as well.

Understanding Mistral OCR 3

Mistral presents its new instrument OCR3 as universal. It deals with a large number of documents present in organizations and is not limited to clean scanning of invoices using OCR. Mistral provides the most important improvements that address some common OCR glitches.

• Manuscript: The model gets a better job of printing and hand typing text on printers.
• Forms: Handles complex structures of boxes, labels and mixed text types. This is typical for invoices, receipts and government documents.
• Scanned documents: The system is less affected by scanning artifacts such as distortion, distortion, low resolution, etc.
• Complex tables: Provides an improved reconstruction table. This will include a combination of cells as well as multiple rows. The output is in HTML tags to preserve the original layout.

Mistral says it tested the model against internal benchmarks that represent real business cases.

What’s new in OCR 3?

The final release offers two significant tweaks to developers: output quality and control. These characteristics enhance the organized extraction capabilities of the model.

1. New controls for document elements: Changelog Mistral OCR 3 combines the new model with new parameters and outputs. Tableformat is now able to choose between markdown and HTML. Extract headers, extract footers and hyperlinks will also help in handling special sections of the document. This is one of the foundations of an AI system for documents.

2. UI playground for rapid testing: Mistral OCR 3 has its OCR API and “Document AI Playground” in Mistral AI Studio. The playground allows you to quickly test challenging scenarios such as faulty scans or scribbles. Before automating the process, you can adjust such parameters as the table format and check the outputs. Successful OCR projects should have feedback that is fast.

3. Backwards compatibility: Mistral confirms that OCR 3 is compatible with the rest of the previous version. This will allow teams to upgrade their systems over time without rewriting their pipelines.

Models and prices

OCR 3 is said to be mistral-ocr-2512. The documentation also refers to the mistral-ocr-latest alias. Pricing will be determined on a per page basis.

• $2 for 1000 pages
• $3 for 1000 annotated pages

The second cost would be when you use annotations for structured extraction. Teams should include these costs in the budget in time.

Practical use of the Document AI Playground

You can access Mistral OCR 3 through the Document AI Playground in Mistral AI Studio. This allows for quick and practical testing.

1. Open the Document AI Playground in Mistral AI Studio. Go to console.mistral.ai/build/document-ai/ocr-playground

If you see “Select a plan”, then register with your number and you will see the following

2. Upload a PDF file or image. Start with a complex document, such as a scanned form with a spreadsheet.

Why this picture?

Clean invoice with table (great first test for OCR 3 table reconstruction)

Use this to check:

• read order (header fields vs. line items)
• extraction table (rows/columns, totals)
• header/footer extraction

3. Choose an OCR 3 model that can be mistral-ocr-2512 or the latest.
4. Select the table format. Use html for structural accuracy or markdown if your pipeline uses it.

5. Run the process and check the output. Check the read order and table structure.

Exit:

• This first run of OCR 3 is essentially flawless for a pure digital invoice.
• All key fields, layout sections and fee summary table are captured correctly with no typos or hallucinations.
• Spreadsheet structure and numerical consistency are maintained, which is essential for financial automation.
• It shows that OCR 3 is ready to produce standard invoices.

Handy with the OCR API

Option A: OCR document from URL

The OCR API supports document URLs. Returns text and structured elements.

Here is a Python example using the official SDK.

import os 
from mistralai import Mistral, DocumentURLChunk 

client = Mistral(api_key=os.environ("MISTRAL_API_KEY")) 

resp = client.ocr.process( 
   model="mistral-ocr-2512", 
  document=DocumentURLChunk(document_url="https://arxiv.org/pdf/2510.04950"), 
   table_format="html", 
   extract_header=True, 
   extract_footer=True, 
) 

print(resp.pages(0).markdown(:1000))

exit:

Option B: Upload files and OCR with file_id 

This method works for private documents, not for a public URL. Mistral’s API has a /v1/files upload endpoint.

First, upload the file using Python.

import os 
from mistralai import Mistral 

client = Mistral(api_key=os.environ("MISTRAL_API_KEY")) 

uploaded = client.files.upload( 
   file={"file_name": "doc.pdf", "content": open("/content/Resume-Sample-1-Software-Engineer.pdf", "rb")}, 
   purpose="ocr", 
) 

resp = client.ocr.process( 
   model="mistral-ocr-2512", 
   document={"file_id": uploaded.id}, 
   table_format="html", 
) 

print(resp.pages(0).markdown(:1000))

exit:

Manipulation of figures and tables

Markdown images and tables are characterized by placeholders used by Mistral’s OCR output. The actual content that is extracted is returned in various fields. This layout gives you the option of having markup as your primary document view. Sources of images and tables can then be saved to the desired location.

Simple OCR is the first step. Structured extraction gives real value. The idea annotation feature is covered in Mistral’s Artificial Intelligence Platform document. It allows you to schema and de-structure documents using JSON. This is how you end up with dependent extraction pipelines that cannot be broken by changing the vendor’s invoice layout. One solution is more practical, which is to use OCR 3 to enter text and annotations into specific required fields, such as invoice numbers or totals.

Augmentation using batch derivation

Dosing is required for high-volume processing. The Mistral batch system allows you to send a large number of API requests in a.jsonl file. They can then be run as a single task. The documentation suggests this /v1/ocr is one of the supported endpoints for batch jobs.

How to choose the right model

The best choice depends on your documents and constraints. Here is a clean way of rating.

What to measure

1. Text accuracy: Use character or word error rates on sample pages.
2. Structure quality: Reconstruction of the scoring table and the correctness of the reading order.
3. Reliability of extract: Measure field accuracy for your target data points.
4. Operating performance: Monitor latency, throughput, and failure modes.

Let’s compare

Use the following image as a reference to compare the two models. We selected this image as:

Hard stress test with boxed boxes + mixed handwriting + printed text (great for comparing OCR 3 vs DeepSeek-OCR).

We’ll use this for comparison:

• handwriting accuracy (cursive + numbers)
• frame/field alignment (numbers inside small squares)
• resistance to dense layouts and small text

Mistral OCR 3

exit:

This result is impressive considering the difficulty of the assignment.

• Mistral OCR 3 correctly identifies document structure, headers, and most handwritten numbers and text, converting dense handwriting into usable markup.
• There are some duplication and minor alignment issues in the tables, which is to be expected with heavy handwriting grids.
• Overall, it demonstrates strong handwriting recognition and layout awareness, making it suitable for real-world form digitization with light post-processing

Deepseek OCR

The result has been prettified, making it easier to navigate than the previous answer. Here are a few other things I noticed:

• DeepSeek OCR shows solid handwriting recognition, but struggles more with semantic accuracy and layout fidelity.
• Key fields are misinterpreted, such as “City” and “State ZIP”, and the table structure is less faithful with incorrect headers and duplicate rows.
• Character-level recognition is decent, but spacing, grouping, and field meaning deteriorate under dense handwriting.

Result:

Mistral OCR 3 clearly outperforms DeepSeek OCR in this handwriting-intensive form. It preserves document structure, field semantics, and table alignment much more accurately, even under dense handwritten grids. DeepSeek OCR reads characters reasonably well, but breaks the layout, headers, and meaning of fields, resulting in higher cleaning efforts. For real-world form digitization and automation, Mistral OCR 3 is the clear winner.

Which one should you choose?

Choose Mistral OCR 3 if you require a complete OCR product that includes a user interface and a clear OCR API. It is optimal for highly reliable and predictable SaaS costs and spreadsheet reconstruction valuations.

Select DeepSeek-OCR if it is required to be hosted locally or separately. It provides flexibility and control of the derivation process to teams willing to manage operations. It is possible that many teams resort to both: Mistral as the primary channel and DeepSeek as a backup for sensitive documents.

Conclusion

Structure and workflow have become major issues due to changes in Mistral OCR 3. Table controls, JSON extraction annotations, and playgrounds have features such as user interface and can reduce development time. It is one of the powerful productization of document intelligence. DeepSeek-OCR provides another way. OCR considers the LLM-related compression problem and provides users with infrastructure freedom. These two models demonstrate the future separation of OCR technology.

Frequently Asked Questions