10 Best Document Parsing APIs for Developers in 2026

A parser that handles clean digital PDFs may struggle with a scanned lease, a 2-column report, or a table that spans several pages. Misread reading order and missing table cells can leave garbled RAG chunks, incorrect invoice totals, or missing contract parties. 

“Document parsing” covers 3 distinct jobs that most roundups treat as one. 

1. Layout parsing returns document structure as Markdown or JSON, including headings, tables, reading order, and often bounding boxes. 
2. Field extraction returns named values against a schema or prebuilt model, the invoice_total or party_name your pipeline actually needs. 
3. OCR turns images of text into selectable text so the other 2 jobs can run. Most APIs are built around one of these jobs and bill each differently.

Pricing units alone make comparison hard. On Amazon Textract, the same page costs $1.50 per 1,000 pages for plain text detection and $70 per 1,000 pages when you add Forms, Tables, and Queries together. 

LlamaParse bills credits per page by tier, Reducto bills per product per 1,000 pages, and the Foxit PDF Services API bills 1 credit per request regardless of page count.

In this article, we compare 10 document parsing APIs based on their features, pricing, usage limits, and deployment options.

Our top picks by use case 

The best document parsing API depends on whether you need layout structure, named fields, or OCR, and where your documents can be processed. These picks match common developer workflows to the APIs that support them. 

All prices and availability are current as of 16 Oct 2026.

Each API appears once, under the use case it fits best. Several handle more than 1 job, and the feature table in each entry shows their coverage.

The Foxit Developer plan includes 500 credits a year, enough to run Structural Extraction on a sample of your hardest pages. Create a free developer account to start.

How we compared these document parsing APIs

We reviewed public documentation, pricing pages, official price feeds, model cards, and GitHub repositories as of October 16, 2026. 

Each API uses the same 8 comparison rows: outputs, scanned pages and OCR, tables and layout, field extraction, pricing, free allowance, limits, and deployment and data. Prices identify the billing unit and relevant tier or mode. Security and compliance statements remain vendor claims. 

The picks reflect use-case fit, not an overall ranking. Benchmark results appear separately, with their publisher, product version, and date. 

The 10 document parsing APIs in detail

Foxit PDF Services API

Our PDF Services API covers cloud-based PDF extraction, conversion, OCR, redaction, and compression. The extraction endpoints covered here are PDF Structural Extraction (Trial) and PDF to Markdown. 

Key features at a glance

Where Foxit PDF Services API stands out

Per-request billing charges the same 1 credit for a 200-page report as a 1-page receipt, within supported limits. The cost advantage depends on document length and how much of your annual credit pool you use. 

These extraction endpoints share an account and credit pool with our other document services, including OCR, conversion, redaction, and eSign. If your workflow already uses those services, you can add extraction without a separate vendor integration. 

Our MCP server exposes structural extraction tools, so AI agents can call the API through the same interface as other PDF operations. 

What to consider

Our PDF Structural Extraction API is currently in trial and uses schema version v1.0.7. The API may change, so validate responses against the schema your application expects.

The API has no schema-based field extraction and no prebuilt invoice or contract models. You receive a typed element tree and map fields yourself, which the invoice parsing tutorial and contract metadata tutorial walk through. Teams that need named fields returned directly from the API should evaluate LandingAI, Reducto Extract, or Azure prebuilt models alongside this.

Paid credits expire at year-end without rollover. Forecast annual usage before choosing a plan. 

Best for

Pipelines that already convert, OCR, redact, or sign PDFs and need structure-aware JSON or Markdown from the same account, particularly on long documents where per-request billing is favorable.

Getting started

Create a free Foxit developer account, set up a sandbox application, and send your first PDF to the Structural Extraction endpoint. The Developer plan includes 500 credits a year with no credit card required.

Our API plans and included credits.

Sandbox and production request limits for our APIs.

LlamaParse

LlamaParse is the document parsing service from LlamaIndex, the company that also publishes the ParseBench benchmark. 

It accepts 130+ file types, returns Markdown or JSON, and offers 4 parse tiers that let you route documents by cost and quality.

Key features at a glance

Where LlamaParse stands out

Fast costs $1.25 per 1,000 pages, compared with $56.25 per 1,000 for Agentic Plus. Routing simple scanned pages to Fast and complex layouts to Agentic Plus can substantially cut costs in mixed-document pipelines. 

Date-pinned tier versions keep the output format reproducible when you rerun the same job weeks later, supporting reliable regression testing on parser output. 

LlamaExtract adds JSON schema-based field extraction on top of parsing, with citations and confidence scores, at no separate integration. The same credential covers both.

What to consider

Concurrency caps are a real constraint on lower plans. One practitioner in the r/RAG community thread (June 1, 2026) reported throttling at 5 to 10 concurrent jobs, which can stall batch pipelines during peak ingestion. The 500-page Extract cap is also worth checking against your largest documents before committing.

LlamaParse’s own API reference confirms the Fast tier skips OCR on images and skips table and heading reconstruction, so its output is closer to spatially-laid-out raw text than the fully reconstructed Markdown the Balanced and Premium tiers produce. The HIPAA BAA scope is Enterprise only, so teams with PHI must confirm the plan level before sending protected documents.

The 48-hour file cache means documents persist by default unless you explicitly opt out per request, which is worth reviewing against your data policy.

Best for

RAG ingestion where you want to trade cost against quality on a per-document basis.

Getting started

Create an account at llamaindex.ai and start with the 10,000 free monthly credits.

LlamaParse plans, pricing, and included credits.

Reducto

Reducto offers parsing, extraction, splitting, classification, and a document-editing endpoint behind one API. Its r-1 model is its newest parsing tier, currently in preview. 

Pricing moved to a per-product structure on 1 Sep 2026, replacing the previous credit-based model.

Key features at a glance

Where Reducto stands out

Teams can consolidate these document operations under one API and authentication credential rather than chaining vendors. The Edit endpoint also supports document filling. 

Hybrid VPC deployments let you keep documents in your own storage while Reducto handles compute. That arrangement splits the data-custody question in a way that SaaS-only providers can’t match without a full VPC agreement.

r-1 returns Markdown for simple tables and HTML for complex merged-cell tables, so your downstream parser can handle each format appropriately. 

What to consider

The r-1 tier is preview and opt-in via the API, meaning its behavior may change before general availability. Pricing changed 6 weeks before the check date, so re-verify rates before signing a volume commitment. The Standard plan’s training data policy is not explicitly stated, so teams with sensitive documents should ask before sending production data.

The Standard plan’s free allowance is $150 of usage, worth 15,000 credits at the standard $10-per-1,000-page r-1 Parse rate.

Best for

Teams that need parsing, extraction, and splitting in one place with a path to a private deployment.

Getting started

Sign up at reducto.ai and start with the Standard plan’s free usage.

Reducto’s Standard, Growth, and Enterprise plans.

LandingAI Agentic Document Extraction

LandingAI Agentic Document Extraction (ADE) made its v2 APIs generally available on 24 Jul 2026. Its DPT-3 model family returns extracted values linked to their exact location in the source document, which lets downstream processes verify or review what the model read.

Key features at a glance

Where LandingAI Agentic Document Extraction stands out

Grounding is the defining feature. DPT-3 Pro links every extracted value to the line that contains it, and DPT-3 Verity links to the word. The Ground API then lets you query those positions without spending additional credits, so your review UI or audit log can show exactly where in the document each field value came from.

Each table cell has a bounding box, so reviewers can highlight a tax line on a complex form rather than inspect a bare string. This helps regulated workflows where someone must sign off on what the model read. 

What to consider

Per-page cost moves with output character count, so dense documents with a lot of extracted text cost more than the per-page headline rate suggests. LandingAI quotes a 1.5-credit median for typical business documents on standard, but your actual distribution will depend on document type.

Free credits expire after 90 days from sign-up, not at month end, so plan your evaluation window accordingly. DPT-3 Verity is still in preview and limited to Latin scripts without scan support, so don’t route scanned or non-Latin documents to it.

Enabling ZDR disables the Ground API, which means you must choose between the strongest data-residency guarantee and the traceability feature. That tradeoff is worth understanding before choosing a plan.

Best for

Regulated extraction where each field value must be traceable to its location in the source document.

Getting started

Sign up at landing.ai and start with the 1,000 Explore credits (90-day expiry).

LandingAI’s free Explore tier and credit-based pricing.

Mistral OCR

Mistral OCR is Mistral’s document-to-Markdown service, now at model version OCR 4.1 (released 16 Jul 2026). 

It bills one flat price per page and cuts that price by 90% for batch jobs, with confidence scores and bounding boxes included in both modes.

Key features at a glance

Where Mistral OCR stands out

A single flat page price already includes blocks, bounding boxes, confidence scores, and reading-order labels. Most providers charge separately for bounding boxes or confidence, so the all-in-one pricing simplifies budget math for large batch jobs.

For pipelines that can wait for asynchronous results, the Batch API’s 90% discount brings the price to $0.40 per 1,000 pages, making it one of this list’s cheapest options for converting archives to Markdown. 

What to consider

OCR 4 is not intended for low-latency, real-time processing. If your pipeline needs synchronous sub-second responses on individual documents, test latency at your volume before committing.

Model versions retire quickly. OCR 4.0 was retired on 30 Sep 2026, less than 3 months after release. Pin your API calls to a specific model version and monitor Mistral’s release notes so you’re not caught by an automatic cutover. Published scores in the OCR 4 announcement are Mistral’s own reproductions of competitor benchmarks, not independent measurements.

Mistral officially supports 42 languages for OCR. Test on your target languages before committing.

Best for

Large batch conversions of documents to Markdown for RAG at a flat per-page price.

Getting started

Sign up at mistral.ai and generate an API key.

Mistral’s general pricing page. OCR rates are listed separately in its API pricing documentation.

Azure Document Intelligence

Azure Document Intelligence (previously Form Recognizer, then Azure AI Document Intelligence) is Microsoft’s cloud extraction service, now available as Azure Document Intelligence in Foundry Tools (API v4.0, 2024-11-30). 

It covers layout parsing, a broad catalog of prebuilt models, custom training, and container deployments for environments that cannot call a cloud API.

Key features at a glance

Where Azure Document Intelligence stands out

Azure’s prebuilt model catalog is among the broadest on this list. Its dedicated models already map named fields, so you don’t have to write field-extraction logic for the standard document types listed above. 

Container deployments let you run Read and Layout processing inside your own infrastructure without sending documents to Azure endpoints. Disconnected containers extend that to air-gapped environments, though availability varies by model and API version.

Volume commitment tiers reduce per-page costs substantially for high-volume workloads. The $190/month tier covers 20,000 prebuilt pages at $9.50 per 1,000 pages rather than the list price.

What to consider

The F0 free tier analyzes only the first 2 pages of each document, which means a 30-page invoice only returns extraction on pages 1 and 2. Testing during evaluation must account for this limit, or you’ll get misleading accuracy results.

For multi-page tables, budget development time to stitch the extracted sections together during post-processing. 

Azure Content Understanding (GA with API version 2025-11-01) is a newer generative extraction layer on the same cloud that’s worth evaluating alongside Document Intelligence for use cases that benefit from generative models.

Best for

Invoices, receipts, IDs, and tax forms on Azure infrastructure, with an option for container or disconnected deployments.

Getting started

Sign up at portal.azure.com and create a Document Intelligence resource. The F0 tier gives 500 free pages a month.

Azure Document Intelligence pricing and its F0 free tier.

Google Document AI

Google Document AI is Google Cloud’s document processing platform, covering OCR, layout parsing with chunking, form parsing, and specialized prebuilt parsers. The Layout Parser’s built-in chunking feature makes it a direct fit for RAG pipelines on Google Cloud.

Key features at a glance

Where Google Document AI stands out

Layout Parser includes context-aware chunks with parent-heading metadata at $10 per 1,000 pages, removing a separate chunking step from a standard RAG pipeline on Google Cloud. 

Image and table annotations became generally available on 27 May 2026, so the Layout Parser now returns visual assets alongside text structure in one pass.

What to consider

As of 16 Oct 2026, every Gemini-based Layout Parser version is in preview; only v1.0 from 2024 is stable. Most processors cap standard online requests at 15 pages, or 30 in supported imageless mode. Documents above those limits require batch processing, which typically takes 12 to 24 hours, excluding queue time. 

Multi-page tables may be split across 2 table objects, requiring post-processing stitching similar to Azure Document Intelligence. The per-document pricing on Invoice and Expense parsers ($0.10 per document up to 10 pages) is competitive for short invoices but doesn’t scale linearly beyond 10 pages.

Best for

RAG pipelines already running on Google Cloud that want layout-aware chunking included at the page price.

Getting started

Enable Document AI in your Google Cloud project at cloud.google.com/document-ai.

Per-page pricing for Google Document AI processors.

Amazon Textract

Amazon Textract charges per feature rather than per page tier, so you pay for Tables only if you call Tables, and Queries only if you call Queries. 

That pay-per-feature structure makes it flexible for pipelines that need different capabilities from different document types.

Key features at a glance

Where Amazon Textract stands out

Text-only pipelines pay $1.50 per 1,000 pages without bundling unused field or table extraction. For one-off or low-volume extraction, Queries retrieve specific values using questions such as “What is the total due?” without a full extraction schema. 

GovCloud availability and PrivateLink support make Textract an option for US government workloads and VPC-isolated architectures that other providers on this list can’t reach.

What to consider

6 supported OCR languages with English-only handwriting and English-only Queries is a hard constraint. If your document set includes French invoices with handwritten annotations, Textract won’t serve that use case. 

Synchronous API calls accept only single-page PDFs up to 10 MB, so multi-page documents must go asynchronous through S3.

The data-use opt-out requires an AWS Organizations policy, not an individual account setting. If your organization doesn’t have that policy in place, AWS may use your documents to improve its services by default.

For generative extraction, also evaluate Amazon Bedrock Data Automation, AWS’s newer document layer. It costs $0.01 per page standard and $0.04 per page for custom models. 

Best for

English-language forms and tables already stored in S3, where you want to pay only for the specific features your pipeline calls.

Getting started

Enable Textract in your AWS account at aws.amazon.com/textract. The 3-month free tier includes 1,000 text pages a month.

Amazon Textract’s 3-month free tier allowances.

Amazon Textract pricing by feature and feature combination.

Adobe PDF Extract API

Adobe PDF Extract API is part of Adobe PDF Services. It returns a detailed element tree from native PDFs, including font data, reading order, and tables as CSV and XLSX, in a single ZIP output. 

Key features at a glance

Where Adobe PDF Extract API stands out

Font metadata alongside structural elements is uncommon in this category. If your pipeline needs to distinguish between a heading that is bold because of its font weight and one styled that way by position alone, the element tree carries that information. Tables export as CSV and XLSX in the same ZIP as the JSON, which removes a conversion step for pipelines that feed spreadsheet data into downstream systems.

The PDF to Markdown endpoint gives LLM-oriented pipelines a direct path from native PDF to LLM-ready text without a separate conversion step.

What to consider

The 400-page limit (150 for scanned files) is a hard ceiling per request, and table-heavy documents may hit a lower effective limit. 

Paid plan pricing is by sales quote only, so you can’t estimate costs from a public page before speaking with Adobe. Compliance scope and training use were not verified as of 16 Oct 2026, so confirm those terms directly with Adobe before sending sensitive documents.

If you need named fields returned directly, evaluate LandingAI, Reducto Extract, or Azure prebuilt models alongside Adobe. 

Best for

Teams already licensing Adobe PDF Services who need font-aware structure and table exports from native digital PDFs.

Getting started

Sign up at developer.adobe.com and start with the 500 free Document Transactions a month.

Docling

Docling is an MIT-licensed open-source document parsing library from IBM Research. It runs locally or in air-gapped environments via Docling Serve, a REST API available in CPU and CUDA containers. v2.140.0 was released on 16 Oct 2026.

Key features at a glance

Where Docling stands out

The MIT license means there are no per-page costs, no API keys, and no data leaving your infrastructure. For teams processing sensitive documents in air-gapped environments, or for high-volume pipelines where per-page costs compound quickly, self-hosting eliminates the billing variable entirely.

Docling Serve exposes the library as a REST API in Docker containers, so you can drop it into an existing microservices architecture without rewriting your integration layer.

What to consider

Self-hosting puts you in charge of scaling, uptime, and model updates. OCR quality depends on which backend you configure, and none of the pluggable backends match the accuracy of cloud OCR services on difficult scans. The DocumentExtractor schema-based extraction is less mature than the field extraction offerings from LandingAI, Reducto, or Azure.

Datalab’s Marker and Chandra publish Apache 2.0 code, but the model weights require a commercial license for organizations above $5M in revenue or $2M in funding, so factor that into your evaluation if you’re above those thresholds.

Best for

Air-gapped deployments, high-volume pipelines where per-page costs are prohibitive, and teams that need full data custody.

Getting started

Install via pip (pip install docling) or pull the Docling Serve container from the IBM Research GitHub repository.

What to compare before choosing a document parsing API

Pricing units are the first thing that makes these 10 hard to compare side by side, so the breakdown below starts there.

Per page, per credit or per request

All prices are from vendor pricing pages, checked 16 Oct 2026.

Worked example, long document. At full use of our Business plan’s 15,000 annual credits, Foxit allocates $0.30 per request. A 200-page document would cost $0.80 on Mistral OCR, $2.00 on Azure Layout or Reducto r-1, and $3.00 on Textract Tables. These are calculations from list prices, not equivalent-output benchmarks. 

Worked example, short document. A 2-page invoice still costs $0.30 on Foxit at full credit utilization, versus $0.008 on Mistral OCR, $0.02 on Azure Layout or Reducto r-1, and $0.025 on LlamaParse Agentic. 

Where the lines cross. At full credit utilization, the $0.30 request cost breaks even at 20 pages against Textract Tables, 24 against LlamaParse Agentic, 30 against the $10-per-1,000-page services, and 75 against Mistral OCR. These are calculations from list prices. 

Commitment. Our paid plans use annual credit pools, while most per-page services bill pay-as-you-go. Unused credits raise the effective cost per request. Each successful PDF Services processing call costs 1 credit within supported limits, including each separate call in a batch. 

Capabilities still differ. These are price comparisons, not like-for-like capability comparisons. Use the job-coverage table below to check whether you need layout parsing, field extraction, or OCR. 

Estimate cost from your actual document lengths and annual volume before choosing a billing model. 

Parsing, field extraction and OCR as distinct jobs

Document parsing turns a document into machine-readable structure, including text, headings, tables, reading order, and sometimes positions. 

The table below shows which of the 3 jobs each API covers.

Choose layout parsing when you need document structure for chunking, search, or LLM input. Choose field extraction when you need named values for a database or business workflow. 

Field extraction does not always cost more than layout parsing. Compare the specific product and mode rather than assuming a fixed premium. 

What happens to your documents

Compare retention, training policies, and private deployment before sending sensitive documents. 

The table uses vendor security and data-use documentation checked on October 16, 2026. “Not published” means the reviewed pages did not state a policy. 

For regulated documents, confirm permitted regions, retention, training opt-outs, and deployment options before evaluating accuracy. A strong benchmark score cannot override a data-handling restriction. 

Which accuracy claims you can trust

Who runs the benchmarks. The callout below names the organizations behind the main tests. LongArray-Extract is run by Extend, and Tensorlake ran its own test. Treat vendor-run results as evidence about those tests, not independent validation. 

Versions go stale. OmniDocBench’s only Mistral entry uses the March 2025 model, which Mistral had retired by October 16, 2026. RD-TableBench was run in November 2024. Mistral’s OCR 4 announcement compares OCR 4 against competitor models, using Mistral’s own reproductions of competitor scores rather than results from the benchmark owners. Scores attached to retired model versions don’t tell you how the current API performs.

Benchmark scores. LlamaIndex’s ParseBench leaderboard reports Azure Layout at 59.6, Docling at 50.6, Google Layout Parser at 50.4, Textract at 47.9, and its own LlamaParse Agentic at 84.9. 

Coverage gaps. As of October 16, 2026, we found no public benchmark covering our PDF Structural Extraction API or Adobe PDF Extract API.

Test 20 to 50 of your hardest pages, including scans, merged-cell tables, 2-column layouts, and handwritten annotations, through 2 or 3 shortlisted APIs. Compare the output with values you have checked by hand. This measures performance on your documents rather than a vendor-curated dataset. 

Benchmark ownership. ParseBench is run by LlamaIndex, which publishes LlamaParse. RD-TableBench is run by Reducto. OmniDocBench is published by OpenDataLab, makers of a competing parser. olmOCR-Bench is published by the Allen Institute for AI, another parser creator. Each benchmark publisher’s product wins or places strongly in its own benchmark. Vendor-run benchmark measurements are not independent validation.

Where Foxit fits

Setting the pricing and job-coverage comparisons aside, here is what the Foxit PDF Services API itself covers for a team evaluating it directly.

Extraction routes on the PDF Services API

Our extraction endpoints cover typed JSON, Markdown for RAG pipelines, tables as spreadsheets, and OCR for scanned files. Structural Extraction remains in Trial; its schema and limits are covered in the Foxit entry above. 

What else shares the same credit pool

From the same account, you can also convert Office files, redact sensitive values, compress or split PDFs, generate documents, and send them for signature. 

Tutorials and comparisons

The structured JSON tutorial, invoice parsing tutorial, and contract metadata tutorial show how to map Structural Extraction output to your own field schema in code. Our Google Document AI comparison covers that API’s layout and extraction capabilities alongside ours.

The free Developer plan

Our Developer plan includes 500 credits a year to try these endpoints before committing to a paid plan. 

Create a free Foxit developer account and turn your first PDF into structured JSON with the PDF Structural Extraction API.

What else to know before you pick a document parsing API

You may only need plain text, a free tier for a prototype, or a service you can run yourself.