How Files2.AI compares to MarkItDown, Docling, LlamaParse, and Unstructured
MarkItDown, Docling, LlamaParse, and Unstructured are excellent tools. They are developer libraries and APIs, and if you are building a document pipeline in code, one of them is probably the right choice. Files2.AI does the same job for a different situation: you have a file, you need clean AI-ready text, and you want nothing to install and nothing uploaded. It is a zero-install, privacy-first browser app.
For anyone choosing a document-to-text tool, technical or not.
Feature comparison
This table reflects each tool's documented behavior as of June 2026. All four alternatives are actively developed, so check their own docs for the latest details.
| Feature | Files2.AI | MarkItDown | Docling | LlamaParse | Unstructured |
|---|---|---|---|---|---|
| Processing location | Your browser | Your machine or server (Python) | Your machine or server (Python) | Vendor cloud (LlamaIndex) | Vendor cloud, or self-hosted for enterprise |
| Install required | None. Open the web app | pip install (Python environment) | pip install, plus model downloads | API key and SDK or HTTP calls | API key, SDK, or container deployment |
| Privacy: does content leave your device? | No. Files stay in the browser tab | No. Runs wherever you run it | No. Runs wherever you run it | Yes. Documents are sent to their cloud | Depends on deployment. Hosted API sends content to their cloud |
| Formats | PDF, Word, Excel, PowerPoint, CSV, EPUB, EML, ODF, and more | Very broad, including images, audio transcription, and zip archives | PDF, DOCX, PPTX, XLSX, HTML, and images, with strong layout analysis | PDF-centric, plus many common document types | Many formats plus 30+ source connectors |
| OCR for scanned documents | Built in, runs in the browser | Via optional integrations such as Azure Document Intelligence | Yes, integrated OCR options | Yes, strong on scans and complex layouts | Yes, part of the pipeline |
| PII redaction | Built-in privacy cleanup labels emails, phone numbers, and SSNs | Not built in. Add your own step | Not built in. Add your own step | Not built in. Add your own step | Available through pipeline configuration |
| Chunking and RAG export | Structure-aware chunking with JSONL metadata export | Not built in. Pair with your own splitter | Yes, chunking utilities aimed at RAG | Yes, designed to feed LlamaIndex pipelines | Yes, chunking is a core pipeline stage |
| UI for non-developers | Yes. Drag, drop, copy | No. Python library and CLI | No. Python library and CLI | Web playground, but built for API use | Enterprise platform UI; core is developer tooling |
| Price model | Free tier, Pro subscription for higher limits | Free, open source (MIT) | Free, open source (MIT) | Free tier, then usage-based credits | Open source core, paid platform and API |
A note on accuracy
We do not publish our own benchmark numbers for other tools. Independent third-party comparisons report F1 scores of roughly 82% for MarkItDown, 88% for Docling, and 92% for LlamaParse on complex layouts; see the source comparison for methodology. Results vary a lot by document type, so the honest advice is to run a few of your own files through any tool before relying on it.
When to use which
- MarkItDown
- Pick it when you are building a Python pipeline and want maximum format breadth for free. It is a well-maintained Microsoft project with MIT licensing, and it handles unusual inputs like audio and zip archives that most converters skip.
- Docling
- Pick it when complex table extraction at scale is the job. Its layout models are widely regarded as best in class for tables and multi-column PDFs among open source tools, and it runs entirely on your own hardware.
- LlamaParse
- Pick it when accuracy on difficult layouts matters more than keeping documents local. It is a hosted API, so content goes to their cloud, but for scanned forms and dense reports it is a strong choice inside LlamaIndex pipelines.
- Unstructured
- Pick it when you need an enterprise ingestion pipeline. With 30+ connectors to sources like SharePoint, S3, and Google Drive, it is built for teams wiring document ETL into a data platform.
- Files2.AI
- Pick it when you want the same job done with nothing to install and nothing uploaded. It is a browser app with a UI anyone can use, built-in OCR and redaction, and a developer API when you outgrow the UI.
Frequently asked questions
- Is Files2.AI a replacement for MarkItDown or Docling?
- Not for developer pipelines. MarkItDown and Docling are excellent open source Python libraries, and if you are writing code they are the right starting point. Files2.AI does the same conversion job for people who want a browser app with no install, a visual interface, and processing that stays on their own device.
- Do my files get uploaded when I use Files2.AI?
- No. The standard conversion flow runs entirely in your browser, so file contents are not uploaded to Files2.AI servers. The optional developer API processes files in memory only and never writes them to disk or a database.
- Which document conversion tool is the most accurate?
- It depends on the documents. Independent third-party comparisons report F1 scores of roughly 82% for MarkItDown, 88% for Docling, and 92% for LlamaParse on complex layouts. For clean digital documents the differences shrink, so test each tool on your own files before committing to one.
- Can I automate Files2.AI like a library?
- Yes. Files2.AI offers a developer API in beta with key-based authentication, so you can convert documents programmatically. See the documentation page for details on supported formats and the conversion workflow.
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