PDF to Markdown
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Modelis:
+ 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.
Extracting layout-aware Markdown… ~5-10 sec/page
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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.
Kaip vartoti PDF to Markdown
1
Įveskite įvedinį
Įveskite tekstą, įkelkite failą arba apibūdinkite ką norite. Sąskaitos nereikia.
2
Spustelėkite generavimą
Mūsų AI apdoroja Jūsų užklausą per kelias sekundes, naudodami geriausius atviro kodo modelius.
3
Atsisiųsti ir dalintis
Atsisiųskite, nukopijuokite arba pasidalinkite savo rezultatais. Nemokamas asmeniniam ir komerciniam naudojimui.
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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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