Best document parsing APIs for RAG (2026)
Your retrieval system only answers with what the parser handed it. If a table came through scrambled, no prompt gets that number back.
The short answer
LlamaParse is the strongest default for mixed document sets: layout-aware tables on four tiers, credit pricing. Reducto is the better pick when tables carry the answer and you'll pay more per page.
Unstructured is the right choice when documents must stay self-hosted for compliance. Amazon Textract fits a team already on AWS that needs forms and tables. Provider-native parsing sends the PDF straight to Claude or Gemini: the fastest way to start, and the first thing to replace once volume grows.
We build the parsing stage into client document pipelines before we touch retrieval. A RAG system diagnosed as a model problem is usually a parsing problem in disguise.
Key takeaways
3 things that decide this
- 01LlamaParse ranks first as the default for mixed documents. Reducto wins when tables and forms carry the answer and you can pay more per page.
- 02We ranked five options on table fidelity, layout handling, cost per page at volume and self-hosting. We checked every vendor's own docs on 11 November 2026.
- 03Before you commit to any of them, run your own hardest pages through two parsers and check the tables by hand.
How we ranked
Verified
We ranked on what breaks a RAG answer downstream. Does a table survive as a table? Does a multi-column page keep its order, and what does a page cost at volume? For every entry we read the vendor's own docs and pricing page on 11 November 2026. Independent accuracy benchmarks were left out, because most are run by a competing vendor.
Chunkr, on the August version, is gone: its own docs say its hosted API shuts down on 15 November 2026 as it joins Extend. Amazon Textract takes its place.
Hashlogics publishes this page. We build custom software and are not one of the tools ranked here. No company paid for its place.
- Table fidelity
- Whether a table comes out as structured rows and columns or as scrambled text.
- Layout handling
- Whether multi-column pages, headers and footnotes stay in the right reading order.
- Cost per page
- What production volume actually costs, not the free tier.
- Self-hosting
- Whether documents can stay inside your own infrastructure.
The five compared
Read from vendor documentation on 11 November 2026.
| Parser | Shape | Table handling | Pick it when |
|---|---|---|---|
| LlamaParse | Hosted API, credit-based pricing | Layout-aware table extraction, cell structure preserved | You want one default for mixed documents |
| Reducto | Hosted, agentic document platform | Complex tables, charts, scans and handwriting | Tables carry the answer |
| Unstructured | Open-source library or hosted API | Layout detection, configurable per element | Documents must stay self-hosted |
| Amazon Textract | Hosted AWS API, billed per page | Tables with cell boxes, forms as key-value pairs | You already run on AWS and parse forms |
| Provider-native (Claude, Gemini) | Send the PDF to the model directly | Reads the page, no separate parse step | Volume is low and you want to start today |
The ranking
Ordered by how many document sets each one suits as a starting point.
Hosted parser with layout-aware table extraction across four accuracy tiers
Start here for a mixed set of PDFs, slides and scanned pages. LlamaParse documents layout-aware table extraction that keeps cell structure intact. It runs on four tiers, from Fast to Agentic Plus for the hardest pages.
Pricing is credit-based, with basic parsing at about one credit per page, agentic parsing costing more, and free credits each month for new users. That structure is where most parsers lose the data that answers your question.
It's a hosted dependency, so your documents leave your infrastructure to be parsed. Weigh that against the usual alternative: engineering time spent maintaining an open-source parser.
Best for
- Teams parsing a mixed set of document types
- RAG pipelines where tables matter but are not the whole document
- Getting a production parser running without building one
Not for
- Documents that must stay inside your own infrastructure
- Teams that already have a working self-hosted pipeline
- Pricing model
- Credit-based, monthly free credits
- Tiers
- Fast, Cost Effective, Agentic, Agentic Plus
Built for documents where forms and tables are the point
Pick this when your document is dense with tables and numbers, and getting a figure wrong is worse than being slow. Reducto calls itself a complete agentic document platform, for parsing, extracting, splitting and classifying across 30-plus file types.
New accounts start with free credits, then per-product standard pricing or custom plans. Per-page cost runs higher than LlamaParse's basic tier. Run your own documents through both before deciding.
Best for
- Insurance, financial and audit documents dense with tables
- Pipelines where a wrong number is worse than a missed sentence
- Teams that want structured extraction as well as raw text
Not for
- Simple documents where table structure barely matters
- Budget-constrained pipelines processing high page volumes
- Pricing
- Credits, then standard or custom plans
- Shape
- Agentic document platform
Open-source library, or a hosted API on top of it
Choose this when documents can't leave your own infrastructure. Unstructured's open-source library is Apache-2.0 licensed on GitHub. Self-hosting has no per-page fee, at the cost of running and maintaining it yourself.
A hosted option also exists for teams that want the same partitioning without running it. Its pricing page bills per page after 10,000 free pages to start.
We reach for the self-hosted path on client work where documents are regulated and can't cross into a third-party API. Budget the maintenance hours honestly; open source still costs you engineering time.
Best for
- Regulated documents that cannot leave your infrastructure
- Teams with engineering capacity to run and tune the pipeline
- High document volume where a per-page fee adds up fast
Not for
- Teams wanting a working parser without infrastructure work
- Small document volumes where hosting overhead isn't worth it
- License
- Apache 2.0, open source
- Hosted API
- Per page, free pages to start
AWS's own parser for tables, forms and handwriting
Use this if your stack already runs on AWS and your documents are forms. Textract returns tables with cell positions, forms as key-value pairs, and answers to plain-language queries. It also detects layout elements and reads printed and handwritten text.
You pay per page processed, with no minimum fee, and the bill lands in your AWS account. Output comes back as blocks you'll still turn into clean text for retrieval, so plan that step.
Best for
- Teams already on AWS parsing forms and invoices
- Pipelines that need key-value pairs as well as text
Not for
- Teams that want markdown ready for chunking without extra work
- Documents that must stay off a cloud provider
- Pricing
- Per page, no minimum
- Runs in
- AWS
- 05
Provider-native parsing
Send the PDF straight to Claude or Gemini
This is the fastest way to get a document into a model with no separate parsing service. Claude's API accepts a PDF as a document and reads its text, charts and tables. Gemini's API takes native PDF input up to 1,000 pages.
It works well at low volume and on documents a vision model reads in one pass. There's no separate infrastructure to run before you can test whether the answers are any good.
It's the first thing to replace once volume grows. A model reading a PDF page by page costs more than a dedicated parser. That gap widens fast at thousands of documents a day.
Best for
- Prototyping a document pipeline before committing to a parser
- Low volume, ad hoc document reads
- Documents a general-purpose model reads correctly in one pass
Not for
- Production volume of thousands of documents a day
- Cost-sensitive pipelines where per-page price compounds fast
- Setup
- No separate parsing service
- Cost at volume
- Highest per page of the five
- ParseThe document becomes text and tables.
- ChunkText is split into retrievable pieces.
- RetrieveA search finds the relevant chunks.
- AnswerThe model reads what it was given.
A broken table at step one produces a wrong number at step four, and it looks like a model problem.
When a better parser will not fix your RAG problem
A clean parse still gets split apart badly if the chunking step ignores the document's structure. A table that survived parsing intact can still get cut in half at the chunk boundary. That puts the question in one chunk and the answer in another.
Test the two stages separately. Paste the parsed output into a prompt by hand and check the table and numbers are right before touching retrieval at all. If parsing is clean and answers are still wrong, the problem has moved downstream.
- 01Scanned, low-quality images defeat every parser here to some degree. Budget time for manual review on the worst documents.
- 02Handwriting and stamps are still the weak point across the field in 2026.
- 03A parser that scores well on clean PDFs can still fail on a form your business actually uses. Test on your own documents, not a vendor benchmark.
When none of these is the answer
A handful of clean, single-column PDFs a month does not need a parsing API at all. Open the file, copy the text, done. Adding a hosted parser to that workflow is a dependency with nothing behind it.
At the other end, some document sets need a human in the loop no matter which parser you pick. The cost of a wrong number is too high to automate past. A benchmark score is not a substitute for review when an error carries legal or financial consequences.
Between those extremes, when parsing feeds a larger RAG pipeline with retrieval and a review step of its own, that pipeline is what we build.
- 01Under a few dozen documents a month, a person reading the PDF is faster to ship than any API integration.
- 02If a wrong extracted number carries legal or financial risk, keep a reviewer on the highest-stakes documents no matter which parser you pick.
- 03If your documents are already structured data (CSV, JSON, a database export), none of these five apply. You do not have a parsing problem.
A document pipeline built on this exact problem
“I am extremely happy with the results and would highly recommend Hashlogics to anyone.”
Daniel Khin · CEO, PremiumAudit.io
Not sure which parser fits your documents?
We test candidates against your own hardest pages before a pipeline gets built around one. Scoping calls cost nothing.
Questions teams ask
01What is the best document parsing API for RAG?+
LlamaParse is the best default for mixed documents, on layout-aware tables and credit pricing. Reducto is the better pick when tables and forms carry the answer and you can pay more per page.
02Should I use LlamaParse or Unstructured?+
LlamaParse if you want a hosted parser that keeps table structure intact and no infrastructure to run. Unstructured if documents must stay inside your own systems and you have engineering time to maintain the pipeline. Regulated data usually decides this one.
03Can I just send PDFs straight to Claude or Gemini instead of using a parser?+
Yes, for low volume. Both accept a PDF as a direct input and read the pages without a separate parsing step. It gets expensive and slow once you move from testing to processing thousands of documents a day.
04Why does my RAG system get answers wrong even though retrieval looks fine?+
Check parsing first. A scrambled table or a multi-column page read in the wrong order hands the model broken input before retrieval ever runs. Paste the parsed text into a prompt by hand and check it reads correctly.
05Do these parsers handle scanned documents and handwriting?+
Scanned text generally comes through well across the field. Textract and Reducto both say they read handwriting, but stamps and messy handwriting still trip every parser here. Spot-check results on those documents rather than trusting them blind.
06Is a more expensive parser always the better choice?+
No. Cost tracks how hard the documents are, not how good the vendor is. A clean single-column PDF does not need Reducto's table handling. Test your actual documents before paying for capability you will not use.
07Why did you leave out Google Document AI and Chunkr?+
Chunkr's own docs say its hosted API shuts down on 15 November 2026 as it joins Extend, so we dropped it. Google Document AI is a fair pick if you run on Google Cloud; Textract covers the same cloud-native slot here.
08How do I compare document parsing APIs fairly?+
Run the same ten of your hardest pages through each parser. Check every table and number by hand. Then price your monthly volume on each vendor's own pricing page.
09How were these parsers ranked?+
We ranked five options on table fidelity, layout handling, cost per page and self-hosting, checked on vendor docs on 11 November 2026. Hashlogics publishes this page. We build custom software and are not one of the tools ranked here. No company paid for its place.
Related reading
- Why your RAG returns wrong answers →Most RAG errors are retrieval failures wearing a model costume, and parsing is the step before that.
- What chunking is →The step right after parsing, and the most common place answers get cut apart.
- RAG development →How we build the full pipeline, parsing included.
- Best RAG frameworks →The layer that sits above parsing and retrieval.
