anydoc vs Docling: Rust Speed or Python's Deep PDF Understanding

anydoc vs Docling: Rust Speed or Python's Deep PDF Understanding

Two very different document-to-Markdown converters keep coming up in the same conversations: Firecrawl's anydoc, a Rust library built for raw speed across office formats, and Docling, the Python project (originally from IBM Research, now under the LF AI & Data Foundation) built around deep PDF layout understanding. They solve overlapping but genuinely different problems, and picking the wrong one for your workflow means either waiting minutes for documents a faster tool would have handled in milliseconds, or getting flat text back from a scanned PDF that needed real layout analysis.

If you'd rather skip the library decision entirely, file2markdown converts Word, PowerPoint, Excel, CSV, PDF, EPUB, HTML, and more through a web upload or its MCP server — no Rust toolchain, no Python environment, no model downloads. Try it free.

The Quick Answer

Use anydoc when your pipeline ingests mostly office documents (Word, PowerPoint, Excel, OpenDocument, RTF, EPUB, CSV) plus text-based PDFs, and speed matters — it has no ML models to load and converts most documents in single-digit milliseconds.

Use Docling when PDFs are the hard part of your pipeline: scanned pages, multi-column layouts, tables that span pages, charts you need as data, or documents in formats anydoc doesn't touch at all (HTML, images, audio, Apple Pages/Numbers/Keynote, email).

What Each Tool Actually Is

anydoc (Firecrawl, MIT, 22.7k GitHub stars) is a Rust engine with bindings for Node.js, Python, Rust, and WebAssembly. It parses 14 formats — Word (.doc/.docx/.docm), PowerPoint, Excel, OpenDocument, RTF, EPUB, CSV, and PDF — into one shared document model, then renders Markdown through a single serializer. There are no ML models in the pipeline; format detection reads file-content markers rather than extensions, and the project's own benchmark (in its README, checked 2026-10-12) claims a median conversion time under 5ms. Text-based PDFs convert locally through Firecrawl's pdf-inspector; scanned pages fail with a NeedsOcr error unless you opt into hosted OCR via Firecrawl Parse.

Docling (LF AI & Data Foundation, MIT, 68.7k GitHub stars, checked 2026-10-12) is a pure-Python project built around "advanced PDF understanding" — page layout, reading order, table structure, formula and code recognition, and image classification, with optional Visual Language Models (like IBM's GraniteDocling) for harder documents. Its format list goes well beyond office documents: PDF, DOCX, PPTX, XLSX, HTML, EPUB, Apple Pages/Numbers/Keynote, audio (WAV, MP3) with ASR transcription, video (MP4, AVI, MOV, MKV, WebM) as of its latest release, email (EML, MSG), images, LaTeX, and more. It ships a CLI, a Python API, an MCP server, and an API server (docling-serve) for running it as a standalone service, and has native integrations with LangChain, LlamaIndex, Crew AI, and Haystack.

Head-to-Head Comparison

Where Each One Wins

anydoc wins on throughput. If you're processing thousands of Word, Excel, and PowerPoint files in a batch job or an API endpoint with a tight latency budget, anydoc's lack of ML models means no cold-start model load and no GPU dependency — just a Rust binary doing format-specific parsing. Firecrawl's own benchmark (self-reported in the anydoc README, so treat it as a vendor claim rather than a neutral third party) puts it ahead of docling, markitdown, pandoc, LibreOffice, Unstructured, and mammoth on both speed and an LLM-judged quality score across the formats they overlap on — but that comparison used an older docling release and judged a narrower slice of docling's current format range, so it's worth re-running on your own documents before trusting it for a procurement decision.

Docling wins on anything that needs real document understanding. A scanned contract, a financial report with multi-column tables, a slide deck with charts you need extracted as data rather than a rasterized image reference — these need the layout model Docling runs locally, not a format parser. Docling also simply covers more ground: if your pipeline ever needs to ingest audio, video, email threads, or Apple's iWork formats, anydoc doesn't support those at all.

A Third Option: Skip the Library

Both anydoc and Docling assume you're willing to manage a runtime — a Rust toolchain or Node/Python environment for anydoc, a Python environment with Docling's model downloads for the VLM path. If you want the Markdown without maintaining either, file2markdown handles PDF, Word, Excel, PowerPoint, EPUB, CSV, and more through a web upload, an API, or an MCP server that Claude, Cursor, and other agents can call directly — including OCR for scanned PDFs on the Pro plan. No library choice, no model downloads, no Rust or Python version to pin in CI.

If you're building a RAG pipeline and the document format isn't the hard part — chunking and retrieval quality is — see our guides on preparing PDFs for RAG and Markdown for RAG pipelines.

Related Comparisons

Docling shows up in a lot of these conversations, so if anydoc isn't the right contrast for your use case, see how Docling stacks up against MarkItDown, Unstructured, LlamaParse, pdfplumber, and MinerU, or browse our roundup of Docling alternatives. For anydoc specifically, we've also compared it against MarkItDown.

Frequently Asked Questions

Is anydoc or Docling better for scanned PDFs?

Docling, without qualification — it runs OCR locally as part of its standard pipeline. anydoc has no built-in OCR: a scanned or image-only PDF page returns a NeedsOcr error unless you explicitly opt into Firecrawl's hosted OCR service, which sends that document over the network.

Can I use anydoc and Docling in the same pipeline?

Yes, and it's a reasonable split: route plain office documents and text-based PDFs through anydoc for speed, and send anything flagged as scanned, image-heavy, or structurally complex to Docling for its layout model. The tradeoff is maintaining two runtimes (Rust/Node/Python bindings for anydoc, a Python environment with Docling's models) instead of one.

Does anydoc support the same formats as Docling?

No — anydoc covers 14 formats (Word, PowerPoint, Excel, OpenDocument, RTF, EPUB, CSV, PDF), while Docling covers 20-plus, adding HTML, images, audio, video, Apple Pages/Numbers/Keynote, email (EML/MSG), and LaTeX. If your pipeline needs any of Docling's extra formats, anydoc simply won't parse them.

Which one is easier to deploy?

anydoc is lighter to deploy: it has no ML models, so there's no multi-gigabyte model download and no GPU consideration, just a Rust binary or a small Node/Python package. Docling's base PDF pipeline runs on CPU, but the optional VLM-based path (for the hardest documents) needs more memory and benefits from a GPU. If you want neither deployment, file2markdown runs the conversion for you through a web upload or MCP server, with no runtime to manage.