Best OCR Models in 2026: Scanned PDFs, Tables, and API Cost

An extracted invoice can contain every digit and still attach a price to the wrong item. Optical character recognition, or OCR, reads text from an image. Recovering the order and relationships on the page is an additional requirement—and often the one that decides whether the result is usable.

We would start with Mistral OCR 4.1 for a hosted document-parsing trial, Google Enterprise Document OCR for a text-recognition baseline, and PaddleOCR when maintaining local processing is a real requirement. Add Google Layout Parser when document structure matters. Mistral OCR 3 remains a lower-priced comparison for existing workflows. This is a shortlist by output requirements, not an independently measured accuracy ranking.

Sources and prices were checked on September 7, 2026. We compare OCR models and the services that deliver them, identifying when a named option is a processor or toolkit rather than a downloadable model. PDF chat applications and extraction from already readable text are outside this comparison.

Choose the output before the model

Mistral's OCR processor guide documents block extraction for OCR 4 and newer. Keep the exact model ID in your records: a moving latest alias is convenient for discovery but makes an old test less reproducible.

PaddleOCR is not one interchangeable model with one score. The repository offers recognition and document-parsing components, including PaddleOCR-VL. Pin the version and configuration you deploy. Downloadable software removes a hosted per-page invoice, not the cost of compute, installation, upgrades, or review.

If a PDF already has an accurate text layer, inspect that layer before paying to recognize the page again. For a scanned document, sample the actual scan quality. A clean digital export and a skewed phone photograph should not share an undifferentiated accuracy claim.

What 10,000 pages would cost

These are USD usage examples for 10,000 processed pages, before tax, storage, retries, optional features, or negotiated discounts. The Google OCR row shows its paid rate before applying any allowance so the unit remains visible.

Google's current standard OCR pricing includes the first 1,000 pages at no charge; if the entire allowance is available, 10,000 pages cost $13.50 for that processor. Layout Parser and OCR add-ons are separate billing choices. The $1.50 rate does not buy every Document AI capability.

Likewise, Mistral's OCR-only rate is not its annotated-page rate. Decide which output you will consume before comparing costs. A dollar difference between services processing different outputs is not an efficiency result.

A table needs more than correct characters

Use a small, invented purchase order to define the acceptance check:

Print or render your own test document, then include a scan and a tilted photograph in the evaluation. The expected subtotal is $54.00. A system that returns all six numbers but swaps the unit prices has not recovered the table correctly.

Check at least three things separately: the text itself; which row and column each value belongs to; and the reading order around headings, notes, and totals. If the original has an empty cell, an invented value is an error. If a footnote changes whether shipping is included, losing it may matter more than a cosmetic spacing difference.

Do not ask a second model to silently repair the OCR result before scoring it. That would evaluate an OCR-plus-repair workflow. Such a workflow may be useful, but its extra usage and opportunities to invent data must remain visible.

For a representative batch, include both ordinary documents and the cases your workflow struggles with: faint scans, merged table cells, multiple columns, rotated text, or handwritten additions. Record results by document type. A high average dominated by easy pages can hide a category that still needs manual processing.

Use benchmarks to find candidates, then inspect your pages

OmniDocBench provides document-parsing data and evaluation across elements such as text, tables, formulas, and reading order. Its value here is the separation of tasks. Check the benchmark release, tested checkpoint, and evaluation setup before quoting a score.

We have not rerun that benchmark or tested all current endpoints on the purchase-order example. A model's result on a public set is a reason to investigate it, not a guarantee that it handles your supplier's layout. Provider-reported results should stay labeled as provider evidence.

Operationally, compare the cost of a batch that passes your checks. Suppose a hypothetical service costs $40 to process 10,000 pages. If 200 pages each require two minutes of correction at an assumed $30 per hour, review adds $200, making the combined cost $240. Those error and labor assumptions are not measurements of any listed service.

That example explains why a small page-rate saving can be less important than one recurring table error. Track correction time alongside pages processed, and retain failed pages in the cost calculation.

After the text and layout are reliable, a separate information-extraction model can map the result into fields such as supplier, amount, and delivery date. Keep the original page available for verification. A structured record is useful only if someone can trace a questionable value back to what the document actually says.

References

• Mistral OCR 4.1 and OCR 3: exact endpoints, availability, and page rates.
• Mistral OCR processor guide: document input and block extraction.
• Google Document AI pricing: processor-specific rates and allowances.
• PaddleOCR repository: deployable recognition and parsing components.
• OmniDocBench: document-parsing evaluation and dataset scope.