Gemini's huge context window makes it tempting to throw whole PDFs at it. That works — but Gemini gives better answers with Markdown input, where structure is explicit and the token count is lower.
Gemini 1.5 and 2.x can hold a million tokens of context, so a 200-page PDF technically fits. But filling the window with raw PDF text doesn't make the answers better — it makes them slower and more expensive. Markdown trims out the layout overhead (column wraps, page headers, repeated footers) so what's left is signal. Gemini's reasoning on a 30K-token Markdown doc is sharper than on the 60K-token PDF text it would otherwise extract.
Drop the PDF on t0md, copy the Markdown, paste into the Gemini web app or Vertex AI. For Vertex AI Studio and Gemini API workflows, save the Markdown as a .md file and upload as a regular text file — the model handles it natively. If you're building agentic flows on Gemini, t0md's HTTP API at /convert lets you call the conversion from your backend.
It can read the PDF, but you pay token-by-token for the layout cruft. Markdown is the same content, half the tokens, and Gemini parses it natively.
Yes — Gemini outputs Markdown by default and renders it inline in the web app. Feeding it Markdown input matches what it's natively producing.
Yes. Convert the PDF to Markdown with t0md, paste into a Doc, and Gemini-in-Docs sees structured text instead of an embedded binary.