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PDF to Markdown

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Model:
+ GPT-5, Claude, Gemini
Drop a PDF — AI converts it into clean GitHub-flavored Markdown with headings, paragraphs, lists, tables, and code blocks all preserved. Powered by IBM Granite-Docling-258M (Apache 2.0). Faster + smarter than plain text extraction.

Drop a PDF here or click to upload

PDF up to 50 MB. ~200 tokens per page.

— —
Extracting layout-aware Markdown… ~5-10 sec/page
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Want better results? Premium models (GPT-5, Claude, Gemini) deliver higher quality. View Plans

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Convert any PDF into clean GitHub-flavored Markdown with headings, tables, lists, and code blocks preserved. Powered by IBM Granite-Docling. Free, unlimited, no signup.

Yadda ake amfani da shi PDF to Markdown

1
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Ka rubuta rubutu, ka shigar da fayil, ko ka bayyana abin da kake so. Babu bukatar asusun.

2
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Our AI processes your request in seconds using the best open-source models.

3
Download & share

Ka saukar, ka kwafe, ko ka raba sakamakonka. Kyauta ga amfanin kai da na kasuwanci.

Yi amfani da wannan kayan aiki ta hanyar API

Yi amfani da wannan kayan aiki daga lissafinka na kai tsaye. REST na ƙarshe mai haɗin kai na OpenAI, amincewa da token mai ɗaukar kaya, babu buƙatar SDK na musamman. Kudin token sun haɗu da haɗin yanar gizo.

API Documentation Get API Key 
curl -X POST https://api.free.ai/v1/chat/ \
  -H "Authorization: Bearer sk-free-..." \
  -H "Content-Type: application/json" \
  -d '{"model": "qwen7b", "messages": [{"role": "user", "content": "Use the PDF to Markdown tool on: ..."}]}'

@ item: inlistbox

PDF Extractor
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Academic Paper Extractor
Research Paper to Text
arXiv PDF Extractor

PDF to Markdown — FAQ

Drop in any PDF and the AI converts it into clean GitHub-flavored Markdown — headings stay headings, tables stay tables, lists stay lists, code blocks stay code blocks. Goes way beyond plain text extraction; the document's structural hierarchy is preserved so you can drop the output straight into a docs site, an LLM RAG pipeline, or a search index.

IBM Granite-Docling-258M (Apache 2.0). Tiny vision-to-sequence model fine-tuned for layout-aware document conversion — beats pdftotext + much faster + smarter than running a generic vision-language model on each page.

pdftotext is a flat dump — paragraphs and tables collapse into a wall of words. Adobe Export to Word preserves layout but produces .docx + costs ~$15/mo. Docling preserves the SEMANTIC structure (heading levels, lists as lists, tables as Markdown tables) and outputs a format LLMs and dev tools can both consume natively.

LlamaParse and unstructured both have free tiers but cap pages/month and require an API key. Docling-258M runs locally on our GPU + is fully self-hosted Apache 2.0, no per-page metering, no key signup. Quality is competitive with LlamaParse on standard documents.

Yes — tables come back as proper Markdown pipe-tables. Complex multi-column / nested tables are flattened more aggressively (a fundamental Markdown limitation, not the model's fault). For perfect table fidelity, we also support `format=html` via the API which preserves rowspan/colspan.

Granite-Docling does the OCR step itself — works on born-digital AND scanned PDFs alike. Scanned at lower DPI (<150) loses some text accuracy; rescan at 200+ DPI for best results.

Most LaTeX-rendered equations come through as inline `$...$` Markdown math. For research papers with heavy math, we also offer the academic-paper-extract tool (Nougat) which is specifically tuned for equations and citations.

About 5-10 seconds per page on our H200. A 30-page report is ~3-5 minutes. Tiny model means batches of small PDFs are essentially free in the daily pool.

200 tokens per page, with a 500-token floor. A 5-page contract = 1,000 tokens. A 30-page report = 6,000 tokens. The 5K daily free pool covers most typical use.

PDF — born-digital + scanned both supported. Max 50 MB upload. Other document formats (DOCX, EPUB, HTML, etc.) are on the roadmap; for now upload-and-convert with the pdf-conversion tool first.

Processed immediately, the Markdown output is kept (24h anonymous / 7d paid share-link expiry), the source PDF is deleted right after extraction. Never used for training. /privacy/ for the full policy.

Yes — POST a multipart `file` to /v1/document/pdf-to-markdown/. Returns {markdown_url, pages, preview, tokens, share_url}. Bearer auth (sk-free-…) gives 10K free tokens/month. /api/ has the curl example.

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