In the past month, the AI community witnessed the launch of two much-anticipated OCR solutions—Mistral OCR by the Mistral team (known for their LLMs) and Agentic Document Extraction by Landing AI, Andrew Ng’s company. At Docsumo, we live and breathe Document AI. So when these releases hit the market, we couldn’t resist putting them to the test

At first glance, these tools appear to be direct competitors in the Intelligent Document Processing (IDP) space. But upon deeper analysis, it's clear they only address a narrow slice of what a full-fledged Document AI platform like Docsumo offers.

Both Mistral OCR and Landing AI focus primarily on layout-preserving OCR. While they position themselves as groundbreaking solutions in document understanding, their functionality is essentially limited to extracting text while attempting to retain document structure.

The Key Difference: Docsumo Is Built for End-to-End Document AI

Docsumo’s platform is more than just OCR. It is an end-to-end IDP system built to handle document ingestion, layout-aware text extraction, intelligent classification, and schema-driven information extraction.

Here’s how our native OCR stands apart:

• Proprietary OCR engine with spatial awareness and layout preservation.
  
• Advanced preprocessing including noise removal, image enhancement, and deskewing
  
• Structured information extraction using schemas with key-value pairs, line items, and Q&A logic
  
• Two-way human-in-the-loop review system for validation and corrections
  
• Auto-classification, document analytics, metadata extraction
  
• Seamless export options in JSON, CSV, Excel formats
  
• Integrations with tools like Salesforce, Google Drive, QuickBooks, SAP, and more in formats such as JSON, CSV, and Excel. (See all integrations here: Docsumo Integrations)

Evaluation Criteria

To fairly assess the capabilities of these three systems, we tested them on:

1. Text extraction quality: Layout preservation, accuracy, completeness
   
2. Information extraction: Accuracy of structured data extraction from OCR outputs using GPT-4o
   
3. Performance metrics: Specifically looking at the speed or latency of each system.
   

All evaluation results are publicly available at:
👉 https://huggingface.co/spaces/docsumo/ocr-results

1. Text Extraction Quality

We evaluated 120 document samples across invoices, forms, bank statements, and passports. Three human reviewers independently rated the outputs from all three systems.

📊 Unanimous Results:

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🔍 Limitations of Mistral OCR

Mistral OCR struggled significantly across a wide range of document types:

1. Pages misidentified as images‍

• Entire sections were returned as image placeholders like 
• For example, in the below bank statement, a full table was treated as an image, with no data extracted at all.

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2. Frequent hallucinations: In unclear or low-resolution scans, it often generated random, unrelated text.

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• Poor table recognition: Tables were treated as images or misparsed, failing to extract even basic cell content.
• Small fonts ignored: Text stamps and fine-print content were regularly missed or replaced with gibberish.
  
• Inconsistent results: Even with moderately clean documents, it often missed key data blocks or misinterpreted layout structures.
  

In short: while fast and cheap, Mistral OCR lacks the robustness required for production-grade document workflows.

⚠️ Issues with Landing AI's Agentic Document Extraction

Although better than Mistral, Landing AI also revealed multiple critical flaws:

1. Text summarization instead of extraction

• Instead of pulling exact content, the model paraphrased or over-described elements.
• Example: A logo containing just the text "ABC" was transformed into a verbose 130-word description.

2. Failure with vertical text

• The model consistently struggled with vertically aligned numbers.
• In the below case, the text "89000458" was misread as "80000456."

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3. Inaccurate field labeling

• Labels were assigned even when contextually incorrect.
• For example, a watermark number at the top of a table was mistakenly labeled as an invoice number—potentially leading to major downstream errors.

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4. Misclassification of tables:

• Tables with fewer than two rows were not recognized as tables and were broken down into key-value pairs.
• Invoices with minimal line items suffered most from this issue.

These limitations reinforce why generative OCR models like Mistral and Landing AI may not yet be suited for production environments where precision, consistency, and fidelity to the original document are critical.

By contrast, Docsumo's native OCR preserves every word, layout, and structure—exactly as it appears in the source document—while enhancing it for downstream processing.

2. Structured Information Extraction

To objectively measure performance in an IDP context, we evaluated how each OCR system’s output performed when used for automated key-value extraction with GPT-4o.

Workflow:

This method revealed the ripple effect of poor OCR output on downstream tasks. Once again, Docsumo’s native OCR yielded the highest extraction accuracy, reinforcing its suitability for enterprise-grade workflows.

3. Speed Comparison

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While Mistral OCR is affordable and quick, its low accuracy renders it unsuitable for anything beyond trivial use cases. Landing AI is significantly slower and frequently experiences timeouts, further reducing reliability.

Docsumo, by contrast, provides a balanced solution—fast, scalable, and consistently accurate.

Final Thoughts

The results are clear: Docsumo’s native OCR outperforms Mistral and Landing AI across all key benchmarks—layout preservation, information extraction accuracy, processing speed, and usability.

And we’re not just saying that—we’re showing it:
👉 Explore the results live on Hugging Face

If you're looking for a document AI system that’s production-ready, scalable, and accurate, Docsumo is built for you.

P.S. We’ll continue expanding this benchmark report with comparisons against other industry competitors to transparently showcase where Docsumo performs better—and where the gaps truly lie in the document AI landscape.