How we ranked them
The list answers one question: which API would we hand an engineering team that has to ship document extraction this quarter and keep it running? Four criteria, weighted equally, each scored from the vendor's public documentation and from evaluations we have run or reviewed. Checked 11 November 2026.
- Output shape — does the API return typed JSON against a schema you define, with per-field confidence and a citation back to the page, or does it return text, markdown, or key-value blocks you still have to map?
- Accuracy evidence — does the vendor publish a benchmark you can rerun on your own documents, or only a headline number? A demo set is not evidence.
- Workflow depth — validation rules, review of low-confidence fields, webhook delivery, and an audit trail per field, or an extraction primitive you orchestrate yourself.
- Cost model — per-page or per-document pricing you can forecast from a document count, with a free tier to evaluate on. We cite no prices here because they change faster than this page does.
What the ranking is not: an accuracy leaderboard. Accuracy on your documents is the measurement that matters, and the only way to get it is to run your hardest 25 documents through the shortlist. The method is at the end of the page.
The ranking
1. Reducto
Developer-first parsing and schema extraction with citations. Strong where parsing quality on dense layouts, tables, and long documents is the first thing you measure, and where the consumer is an agent or a retrieval pipeline. Output is typed JSON against your schema, with each value cited back to the source region. Where it falls short: the workflow around extraction, meaning validation rules, review of low-confidence fields, and a per-field audit trail, is yours to build. Cogneris vs Reducto.
2. Google Document AI
Hyperscaler extraction with pretrained processors for invoices, receipts, identity documents, and more, a custom extractor you train on your own samples, and a layout parser for retrieval. Strong when you already run on Google Cloud and want a managed processor per document type. Where it falls short: output is processor-specific, the workflow layer is assembled from other GCP services, and leaving GCP means leaving the processors. Cogneris vs Google Document AI.
3. Azure Document Intelligence
Microsoft's document service, formerly Form Recognizer and now part of Azure AI Foundry alongside Content Understanding. The broadest set of prebuilt models among the hyperscalers (invoices, receipts, identity documents, tax forms, contracts, and more) plus custom models trained from labeled samples. Where it falls short: the same build-it-yourself workflow layer as Google, and a model catalog that keeps being reorganized under new product names, which makes it a moving target to depend on. Cogneris vs Azure Document Intelligence.
4. Extend
An API suite of parse, extract, classify, split, edit, and verify primitives, with multi-step workflows and a dashboard for iterating on a schema. Strong when you are composing your own pipeline from primitives and want to see each step. Where it falls short: a workflow built from primitives is still a workflow you own, and the review and audit layer stays yours. Cogneris vs Extend.
5. AWS Textract
OCR, forms, tables, and natural-language queries over a page, plus prebuilt analysis for expenses, identity documents, and lending packets. The default primitive for AWS-native teams, and a reasonable one. Where it falls short: Textract returns blocks, key-value pairs, and table cells, not a typed object against your schema; a second step, in your code or an LLM, turns those into fields, and review, validation, and audit are assembled from other AWS services. Amazon's newer Bedrock Data Automation covers part of that gap. AWS Textract alternatives, and when to keep it.
6. LlamaParse
LlamaIndex's parsing service, built for retrieval: PDFs and office documents to markdown and structured chunks, with LlamaExtract adding schema-based extraction on top. Strong when the destination is a LlamaIndex pipeline. Where it falls short: extraction is a newer surface than parsing, and there is no operational layer for review or audit. Cogneris vs LlamaParse.
7. Mistral OCR
An OCR model served as an API: pages in, markdown with tables and embedded images out, with strong multilingual coverage. Strong when you want a model rather than a platform and will prompt a second model for the fields. Where it falls short: it is OCR, not extraction. Schema, validation, confidence, and review are all downstream work.
8. Unstructured
Partitioning for retrieval across a long list of file types: elements such as titles, narrative text, tables, and images, with chunking strategies, served as an API or self-hosted. Strong when the job is making a corpus retrievable. Where it falls short: it is not a field extractor. A schema-based extraction and the workflow around it are not what it does.
9. Veryfi
Receipt, invoice, bank statement, and other financial-document APIs with prebuilt field sets and fast responses. Strong when your documents are financial and you want fields on the first call. Where it falls short: narrow by design. Documents outside its catalog need another tool, and the workflow layer is yours. Cogneris vs Veryfi.
Also evaluated, not ranked
ABBYY Vantage is low-code enterprise IDP for a different buyer than an API team. Mindee ships developer document APIs; our Mindee alternatives page covers that shortlist. Nanonets, Rossum, and Hyperscience are operations-led platforms where the API is one surface among several. Each is a credible choice for its buyer; none is the first thing we would hand an engineering team evaluating extraction APIs.
At a glance
| Rank | Tool | Output shape | Workflow layer | Best for |
|---|---|---|---|---|
| 1 | Reducto | Typed JSON against your schema, with citations | Yours to build | Parsing quality for agents and retrieval |
| 2 | Google Document AI | Processor-specific entities and layout | Assembled from GCP services | GCP teams wanting a managed processor per document type |
| 3 | Azure Document Intelligence | Prebuilt and custom model fields, layout | Assembled from Azure services | Microsoft shops with documents in the prebuilt catalog |
| 4 | Extend | Schema output from composable primitives | Workflows from primitives, review yours | Teams composing their own pipeline with a dashboard |
| 5 | AWS Textract | Blocks, key-value pairs, tables, query answers | Assembled from AWS services | AWS-native OCR, forms, tables, lending, and expense packets |
| 6 | LlamaParse | Markdown and chunks; schema via LlamaExtract | None | LlamaIndex retrieval pipelines |
| 7 | Mistral OCR | Markdown with tables and images | None | Multilingual OCR as a model, fields extracted downstream |
| 8 | Unstructured | Document elements and chunks | None | Making a corpus of many file types retrievable |
| 9 | Veryfi | Prebuilt financial-document fields | Yours to build | Receipts, invoices, bank statements |
Where Cogneris fits
Disclosure — this is our site, which is why Cogneris sits outside the numbered list. Judged on the same four criteria: output is typed JSON against your schema with per-field confidence and citations; accuracy evidence is a 7-day evaluation on your own documents that ends in a report you can show your CTO; workflow depth is where we spend most of our effort, with validation rules, review queues, portal intake, webhook delivery, tenant controls, and a per-field audit trail recording model version, prompt version, reviewer ID, and validation status; cost is pay-per-page, with 400 free credits and no card.
Where we fall short: a narrower vendor with a shorter track record than the hyperscalers, and not the cheapest raw OCR primitive if OCR is all you need. Choose Cogneris when the workflow around extraction is what you need to buy, not only the fields. See the extraction API, the pricing, or the platform.
How to run the evaluation
Take your hardest 25 documents, not a demo set, and run the same files through every API on your shortlist. Compare field accuracy, citation quality, schema stability, latency, review effort, and the total work to reach your system of record. Our document extraction benchmark guide describes the method, and we run a 7-day evaluation that produces an accuracy and latency report. No commitment, no sales pressure.