What is the best PDF OCR API for structured JSON with page-level citations?

As of October 2026, Reducto Extract ($20 per 1,000 pages, parsing included) and LandingAI Agentic Document Extraction (Extract API with word-level atomic citations) are the best APIs when every JSON field must point back to a page and location. LlamaExtract (LlamaIndex) is the best value for RAG teams and returns citations and confidence scores. Azure Document Intelligence and Google Document AI return page numbers and bounding regions for every field and are the safe picks inside Microsoft or Google Cloud. Mistral Document AI ($5 per 1,000 pages) is the cheapest schema-based option, with paragraph-level bounding boxes.

What does a page-level citation in OCR output mean?

Each extracted value carries the page number it came from and usually a bounding box (coordinates on that page), sometimes down to the line or word. It lets a reviewer click a field and see the source, lets you redact or highlight exact regions, and lets an auditor verify a number without re-reading the document.

Can I use an LLM instead of an OCR API to get JSON with citations?

Yes for small volumes. Claude's Citations feature returns page_location citations (start and end page numbers) when you send a PDF as a document block, and works with structured prompts. It costs normal token prices and does not return bounding boxes, so for high volumes, coordinates or review UIs a document API is cheaper and more precise.

Quick Answer

Best PDF OCR API for Structured JSON With Page Citations

Published: October 7, 2026

The short answer

For JSON where every field carries a page and a location, use Reducto Extract or LandingAI Agentic Document Extraction. For the lowest cost per page with bounding boxes, use Mistral Document AI. Inside Azure or Google Cloud, use their native extractors. All prices below are list USD per 1,000 pages as of October 7, 2026.

The shortlist

API	JSON to your schema	Citation granularity	Price per 1,000 pages	Best for
Reducto Extract	Yes, any schema	Extraction citations + bounding boxes	$20 (parsing included) · $40 Deep Extract · 20% off batch	Hardest documents, regulated workflows
LandingAI ADE (Extract v2)	Yes, any schema	Word-level atomic citations (via DPT-3 grounding)	$0.01/credit; parse ≈ 1.5 credits median per business doc (Standard) + extract by characters	Audit trails, human-review UIs
LlamaExtract (LlamaIndex)	Yes; per document, per page or per table row	Citations + confidence scores	Credits at $1.25 per 1,000 credits; 10,000 free credits/month	RAG teams, generous free tier
Azure Document Intelligence	Prebuilt models + custom extraction	Page number + bounding polygon per field	Prebuilt $10 · custom $30 · query fields add-on $10	Microsoft shops
Google Document AI	Custom Extractor (generative or trained)	Page anchors + bounding boxes	$30 up to 1M pages, $20 above	Google Cloud shops
Mistral Document AI	Yes, annotations to your schema	Paragraph-level boxes, block labels, confidence	$5 · OCR alone $4 ($2 batch)	Cheapest schema JSON at scale

How the leaders differ

Reducto splits the job into Parse ($10), Extract ($20, parsing included) and Deep Extract ($40, an agentic loop for the hardest documents). Its pricing page lists bounding box support and extraction citations on every plan, $150 of free usage on the Standard plan, and migration credits of up to $5,000 for teams moving from another processor.

LandingAI ADE went furthest on traceability in its Gen2 release (2026): DPT-3 Pro grounds every line and DPT-3 Verity every word with a confidence score, and the Extract API returns citations drawn from that grounding — so a field points to a specific word on a specific page. Parse is priced by output characters (DPT-3 Pro: 0.5 credit per page + 0.25 per 1,000 characters on the asynchronous Standard tier). Detail: LandingAI vs Mistral OCR.

LlamaExtract sits on LlamaParse. Its plans list structured JSON output to a custom schema, extraction targets per document, per page or per table row, and citations with confidence scores. 1,000 credits cost $1.25; the free plan includes 10,000 credits a month.

Azure and Google are not the most accurate on messy documents, but every field already comes with a page number and coordinates, and they inherit your cloud’s compliance, private networking and billing. Azure prices custom extraction at $30 per 1,000 pages; Google’s Custom Extractor and Form Parser cost $30 per 1,000 pages up to 1 million, then $20.

Mistral Document AI returns annotations against your schema for $5 per 1,000 pages on top of OCR 4.1’s paragraph-level bounding boxes and block-level confidence scores. It is the cheapest way to get schema JSON with locations, but its citations are paragraph-level, not word-level.

How to choose

Regulated or audited (finance, insurance, healthcare): Reducto or LandingAI ADE; both offer zero data retention and a HIPAA BAA on paid plans.
Volume over 1 million pages a month, simple documents: Mistral Document AI, or Azure/Google if you are already there.
RAG pipeline that also needs a few fields: LlamaExtract.
Under a few thousand pages: send PDFs to an LLM with citations enabled — Claude’s Citations API returns page_location citations with page numbers — and skip a document vendor.

Whatever you pick, test on your own 50 worst documents and score field accuracy and citation accuracy: a correct value with the wrong page reference fails an audit just as a wrong value does.

The full ranking by accuracy and price is in best OCR and document extraction APIs; measured accuracy is in most accurate document parsing API; enterprise buying criteria are in best enterprise AI OCR and document processing vendors.

Last verified: October 7, 2026. Prices are list USD from vendor pricing pages.

The short answer

The shortlist

How the leaders differ

How to choose

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