What is the pricing for OpenParser?

Parsing costs $1 per 1,000 pages after a one-time free allotment of 1,000 pages. Extraction adds per-token costs based on the LLM model you select. There are no minimums.

What model does OpenParser use?

It uses PaddleOCR-VL-1.6, an open-weight model that ranks highest on independent document parsing benchmarks.

Can I self-host the OCR model instead of using the API?

Yes, PaddleOCR-VL is open-weight and can be self-hosted, replacing page fees with your own GPU and operational costs.

How does extraction differ from parsing?

Parsing returns blocks and markdown from the document. Extraction fills a user-defined schema by applying an LLM to those blocks, and is billed per token.

Does OpenParser support asynchronous or batch processing?

Yes, the API supports sync, async, and batch requests for the /parse and /extract endpoints.

How can I verify the accuracy of OCR results?

Each parsed block includes a confidence score and bounding box. You can route low-confidence pages for manual review before they are used downstream.

OpenParser — Cheapest OCR API for document layout & tables

OpenParser is an OCR API specializing in layout and table parsing, powered by the open-weight model PaddleOCR-VL-1.6. It offers the lowest public list rate for structure-preserving OCR at $1 per 1,000 pages after 1,000 free pages, significantly undercutting competitors like AWS Textract and Azure Layout. Designed for developers who need reliable extraction of blocks, tables, and markdown from documents such as invoices, statements, and forms, OpenParser provides a transparent, page-based pricing model with no minimums.

The service solves the problem of high-cost, complex OCR for layout-heavy documents. By separating parse and extract metering—pages for structure recognition and tokens for schema filling—it gives users control over spending. The same API supports sync, async, and batch requests, and includes a playground for testing documents before integration. OpenParser is built for teams that require confidence scores, block citations, and the ability to self-host the underlying model.

OpenParser

Key Features

Flat Page Pricing

Open-Weight Model (PaddleOCR-VL-1.6)

Block-Level OCR with Confidence Scores

Separate Parse and Extract Meters

Block Citations for Extracted Values

Playground and Console

Same API for Parse and Extract

Use Cases

FAQ

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