# Docling vs LlamaParse vs DocsAPI: 1,200-Doc Benchmark (2026) > Docling vs LlamaParse in 2026: free self-hosted converter vs hosted parser. Benchmark results, LlamaParse credit prices, and our Docling bank statement test. **In short:** Docling is a free, open-source converter you run on your own machines; LlamaParse is a hosted parser billed in credits, from $1.25 per 1,000 pages. Choose Docling for private, offline or technical documents, LlamaParse for LlamaIndex RAG pipelines and messy scans. For financial tables, also test a document API. **Canonical URL:** https://docsapi.co/resources/blogs/docling-vs-llamaparse-vs-docsapi **Author:** Nupura Ughade — Content Marketing Lead, DocsAPI **Author LinkedIn:** https://www.linkedin.com/in/nupura-ughade/ **Published:** 2026-06-17T00:00:00.000Z **Updated:** October 12, 2026 **Primary topic:** docling **Site:** https://docsapi.co (DocsAPI — Document AI & OCR API for SMB Lending) --- Short answer: Docling if your documents must stay on your own servers or you want no per-page fee, LlamaParse if you want a managed parser that plugs into LlamaIndex. Both turn documents into Markdown or JSON for AI pipelines. Below are our benchmark results, LlamaParse's actual prices, and what Docling did with a 3-page bank statement we ran. ## Is Docling better than LlamaParse? Neither wins everywhere. In our 1,200-document benchmark Docling led on academic and technical PDFs (94% against 89%), while LlamaParse led on phone-scanned receipts (78% against 58%). Docling is free and runs offline; LlamaParse costs credits but needs no servers. So the docling vs llamaparse choice comes down to your documents and whether they may leave your network. If you are building a retrieval-augmented generation (RAG) system, where an AI answers questions from your own documents, both produce the Markdown most pipelines want. If you need specific values, such as every transaction on a bank statement, read the test below first: reading the text was not the hard part. ## Docling vs LlamaParse at a glance The facts below come from each project's own pages, checked on September 28, 2026. | Dimension | Docling | LlamaParse | | --- | --- | --- | | What it is | Open-source document converter, started at IBM Research Zurich, now hosted by the LF AI and Data Foundation | Hosted parsing service from LlamaIndex, part of LlamaCloud | | License and cost | MIT, free; you pay for the machines | Credits: $1.25 per 1,000 credits; free plan includes 10,000 credits a month | | Where it runs | Your laptop or servers, including air-gapped ones | LlamaIndex's cloud; private cloud deployment offered to enterprise customers | | Inputs | PDF, DOCX, PPTX, XLSX, HTML, images, email files, audio and more | Documents uploaded through the API or web app | | Outputs | Markdown, HTML, lossless JSON, DocTags | Markdown by default, JSON on request | | OCR for scans | Pluggable engines; our install used RapidOCR by default, and EasyOCR and Tesseract are options | Managed for you; accuracy depends on the tier you pay for | | Latest version | 2.130.0, September 22, 2026 | Managed service; tiers are versioned | | Best for | Private or offline documents, technical PDFs, no per-page fee | LlamaIndex RAG pipelines, messy scans, no servers to run | ## What happened when we ran Docling on a 3-page bank statement Docling read every transaction correctly, on both a clean PDF and a scanned copy, but it returned three separate tables, one per page. Joining them took four lines of code, and a running-balance check confirmed all 110 rows. The work you must plan for is joining and checking, not reading. Here is exactly what we did on September 28, 2026: - Setup: Docling 2.130.0 on Python 3.12, default settings, on an Apple M5 laptop CPU with no GPU. - Document: a 3-page bank statement PDF we generated with 110 transactions (date, description, debit, credit, balance), with the table header repeated at the top of each page, as real statements do. - Scanned copy: the same pages turned into images at 200 DPI, tilted by up to 1.2 degrees, blurred and speckled with noise, then saved as an image-only PDF, so Docling had to run OCR. | File | Tables returned | Rows per table | Amounts and balances read correctly | Time for 3 pages | | --- | --- | --- | --- | --- | | Digital PDF (text layer) | 3 | 35, 39, 36 | 110 of 110 | 23 to 31 seconds | | Scanned PDF (OCR by RapidOCR) | 3 | 35, 39, 36 | 110 of 110 | 22 seconds | Three caveats. Our statement is cleaner than most real ones: no merged cells, no rows split across a page break, no stamps. Docling did recognize the repeated header on each page as a header, so no header rows leaked into the data. And on a laptop CPU it took 7 to 10 seconds a page; in our benchmark, on a cloud machine, it averaged 0.8 seconds, so hardware matters a lot. The code we ran joins the per-page tables and checks every running balance against the opening balance printed on the statement: ``` from docling.document_converter import DocumentConverter import pandas as pd result = DocumentConverter().convert("statement_scan.pdf") doc = result.document tables = [t.export_to_dataframe(doc=doc) for t in doc.tables] statement = pd.concat(tables, ignore_index=True) print(len(doc.tables), "tables,", len(statement), "rows") def money(s): return float(str(s).replace(",", "")) if str(s).strip() else 0.0 balance = 12406.18 # opening balance printed on the statement problems = [] for i, row in statement.iterrows(): balance = round(balance - money(row["Debit"]) + money(row["Credit"]), 2) if balance != money(row["Balance"]): problems.append(i) print("rows where the running balance does not match:", problems) ``` Output: "3 tables, 110 rows" and an empty list of problems. Keep the balance check even when the reading is perfect; on real statements it is what catches a dropped or duplicated row at a page break. Our [PDF parser guide](/resources/blogs/pdf-parser-online) shows those page-break failures, and our [bank statement OCR guide](/resources/blogs/ocr-bank-statement-technology-revolutionizing-financial-data-processing) lists the other checks lenders run. ## What did our 1,200-document benchmark find? Each tool won a different category. Docling led on academic papers, LlamaParse on phone-scanned receipts, and DocsAPI on bank statements and invoices. The table shows the full results from our [2026 OCR benchmark](/resources/blogs/ocr-accuracy-benchmark-2026), which used 300 anonymized real documents per category. | Document category | Docling | LlamaParse | DocsAPI | | --- | --- | --- | --- | | Academic papers (arXiv PDFs) | 94% | 89% | 87% | | Bank statements (multi-page tables, row-level) | 76% | 71% | 91% | | Invoices (line items) | 81% | 87% | 93% | | Phone-scanned receipts | 58% | 78% | 74% | | Average time per page | 0.8 s (self-hosted, cloud machine) | 1.4 s (API) | 1.1 s (API) | Why did Docling score 76% on real bank statements when it read our synthetic one perfectly? Real statements break tables in the middle of a row, merge cells, print notes between transactions and change layouts between banks. That is exactly where the joining and checking work sits. Treat any docling benchmark, ours included, as a starting point and test on 50 of your own documents. ## How much does LlamaParse cost? LlamaParse bills in credits at $1.25 per 1,000 credits, and each page costs 1 to 45 credits depending on the tier: Fast 1, Cost-effective 3, Agentic 10 and Agentic Plus 45. That works out to $1.25 to $56.25 per 1,000 pages. The free plan includes 10,000 credits a month; paid plans start at $50 a month. | LlamaParse tier | Credits per page | Cost per 1,000 pages | Pages in the free 10,000 credits | LlamaIndex suggests it for | | --- | --- | --- | --- | --- | | Fast | 1 | $1.25 | 10,000 | Plain text, high volume | | Cost-effective | 3 | $3.75 | 3,333 | Text-heavy pages with simple tables | | Agentic | 10 | $12.50 | 1,000 | Tables, scans, multi-column layouts, charts | | Agentic Plus | 45 | $56.25 | 222 | Dense financial reports, papers with equations | A worked example: 10,000 pages a month on the Agentic tier is 100,000 credits, or 100 x $1.25 = $125 a month. Docling costs nothing to license, but you pay for the machines and the engineering time to run them. LlamaIndex's plans are Free (10,000 credits a month), Starter at $50 a month (40,000 credits) and Pro at $500 a month (400,000 credits), with custom Enterprise plans. ## Which is best for RAG pipelines? For RAG, the LlamaParse vs Docling choice is mostly about your stack. LlamaParse fits most easily if you already build on LlamaIndex, because it is part of the same ecosystem and returns Markdown by default. Docling fits when documents must stay private or you want no per-page fee, and it also exports Markdown. RAG quality depends more on how you split and retrieve text than on which of the two parsed it. Match the parser to the documents in your knowledge base. Scientific papers suit Docling, which keeps section structure, equations and citations in its output. Messy scans and mixed consumer documents suit LlamaParse's higher tiers. Financial documents with tables are better stored as structured data you can query, not as Markdown text chunks. ## Docling vs Textract and Docling vs Tesseract They sit at different levels. Tesseract is an OCR engine that returns plain text. Docling is a converter that finds layout, tables and reading order, and calls an OCR engine (it can use Tesseract) only for scanned pages. AWS Textract is a hosted service that returns text, forms and tables, billed per page. So Tesseract vs Docling is not really either-or: Docling can call Tesseract to read scanned pages and then adds the structure on top. | Question | Docling | Tesseract | AWS Textract | | --- | --- | --- | --- | | What you get | Markdown or JSON with layout, tables and reading order | Plain text | Text, key-value pairs, tables, receipt and US ID fields | | Where it runs | Your machines | Your machines | AWS cloud | | Cost | Free license | Free license | $1.50 per 1,000 pages for text, $15 with tables | | Languages | Depends on the OCR engine you plug in | More than 100 | Six (English and five Western European) | For the aws textract vs docling decision, the question is usually whether documents may go to AWS and whether you want to run servers. Our [Textract guide](/resources/blogs/aws-textract-vs-docsapi) has Textract's full price list and limits, and our [PaddleOCR vs Tesseract test](/resources/blogs/paddleocr-vs-tesseract-vs-docsapi) shows how the underlying OCR engines differ on hard images. ## What about Unstructured? If you are comparing docling vs llamaparse vs unstructured, Unstructured is the third common option: an open-source library (Apache 2.0) for turning documents into clean elements for AI pipelines, with a hosted platform alongside it. Like Docling it can run on your own machines; like LlamaParse it has a paid hosted option. We did not include it in our benchmark, so test it on your own documents before choosing. ## Should you self-host or use a hosted parser? Self-host (Docling or open-source Unstructured) when documents may not leave your network or volume is high and you have engineers to run it. Use a hosted parser (LlamaParse, or LlamaParse's private cloud option for enterprises) when you would rather pay per page than run servers. Our laptop test showed how much self-hosting speed depends on hardware. For a regulated team, the real checklist is data residency, retention, audit logging and who can see the files, not the parser's feature list. ## Where DocsAPI fits Disclosure: DocsAPI is our product. It is a hosted API for financial and business documents: it classifies each file, returns named fields as JSON, and joins transaction tables across pages, which is why it led on bank statements (91%) and invoices (93%) in our benchmark. It lost on academic papers and receipts. It costs from $0.02 per page, with a free 30-day trial for qualified lenders after a demo. It is not a general RAG parser and cannot run offline; for those, use Docling or LlamaParse. See our [VLM vs OCR guide](/resources/blogs/vlm-vs-ocr) for handwriting, which none of these three handles reliably. ## What to do next - Documents must stay private or offline: install Docling and run the code above on 20 of your own files. - You build on LlamaIndex: start LlamaParse on the Cost-effective tier and move up a tier only for pages that fail. - You process bank statements or invoices: compare the joined tables from Docling, LlamaParse and a DocsAPI trial on the same statements, and run the balance check on each. - You are still choosing an OCR engine: the Which OCR should I use tool asks six questions, and our OCR for PDFs guide explains how to test fairly. ## Sources and how we checked this - Docling: features, formats, license and history from the Docling GitHub repository; version and release date from its releases, read September 28, 2026. - LlamaParse: plans and credit price from the LlamaIndex pricing page; credits per page from the LlamaParse pricing documentation; tier guidance from the tiers guide. - Unstructured: license and description from the Unstructured GitHub repository. - Our Docling test: run September 28, 2026 as described above, on a synthetic statement, so it shows behavior, not an accuracy rate. Benchmark numbers come from our OCR accuracy benchmark. Textract prices from AWS's public price list. ## Frequently Asked Questions ### Is Docling free? Yes. Docling is open source under the MIT license, so it is free for commercial use. It started at IBM Research Zurich and is now hosted by the LF AI and Data Foundation. Your costs are the machines that run it and the time to set it up and keep it running. ### Is LlamaParse free? It has a free plan with 10,000 credits a month. A page costs 1 credit on the Fast tier, 3 on Cost-effective, 10 on Agentic and 45 on Agentic Plus, so the free credits cover 10,000 Fast pages or 1,000 Agentic pages. Extra credits cost $1.25 per 1,000. ### Does Docling join tables that run across pages? Not in our test. On a 3-page bank statement it returned three tables, one per page, with every row read correctly. Joining them took a pandas concat, and a running-balance check confirmed all 110 rows. Plan that step yourself for statements and long invoices. ### Can Docling read scanned PDFs? Yes. For image-only pages it runs an OCR engine; our install used RapidOCR by default, and EasyOCR and Tesseract are options. It read all 110 transactions on our noisy, slightly tilted scanned statement. In our benchmark its weakest category was phone-photographed receipts, at 58%. ### Can Docling run offline or in an air-gapped environment? Yes. Docling runs entirely on your own machines once its models are downloaded, and its maintainers describe local execution for sensitive data and air-gapped environments as a design goal. That is the main reason regulated teams choose it over hosted parsers. ### How fast is Docling? It depends heavily on hardware. On a laptop CPU with default settings we measured 7 to 10 seconds a page on a 3-page statement. In our benchmark on a cloud machine it averaged 0.8 seconds a page. Time a sample of your own pages on the hardware you will use. ### Which is best for RAG pipelines? LlamaParse if you already use LlamaIndex, because it plugs in directly and returns Markdown. Docling if documents must stay private or you want no per-page fee; it also exports Markdown. Chunking and retrieval usually matter more to answer quality than the parser. ### Is Docling better than Tesseract? They do different jobs. Tesseract only turns images into plain text. Docling understands layout, tables and reading order and exports Markdown or JSON, and it can use Tesseract as its OCR engine for scanned pages. If you only need text from clean scans, Tesseract alone is simpler. ### Is Docling better than LlamaParse? For academic and technical PDFs, yes: Docling led at 94% against 89% in our benchmark. For phone-scanned receipts LlamaParse led at 78% against 58%. Docling is free and self-hosted; LlamaParse is managed and billed in credits. Choose by your documents and where they are allowed to go. ### Which document parser is best for bank statements? In our benchmark DocsAPI, our product, led at 91% row-level accuracy against 76% for Docling and 71% for LlamaParse, because it joins transaction tables across pages. With Docling you can get the same joined table using the code on this page, plus a running-balance check. Test on your own statements. --- **Source URL (cite this):** https://docsapi.co/resources/blogs/docling-vs-llamaparse-vs-docsapi **Author profile:** https://docsapi.co/author/nupura-ughade **Published by:** DocsAPI (https://docsapi.co)