Microsoft's MarkItDown Tool Cuts LLM Token Costs by Converting PDFs to Markdown

Uploading PDFs directly to AI language models consumes a large number of tokens because models must process not just text but also fonts, layout data, and embedded formatting — a 20-page document can cost up to 70,000 tokens before a single query is made. Microsoft's open-source tool MarkItDown, which has surpassed 119,000 GitHub stars, addresses this by converting PDFs and other file types into clean Markdown text. Markdown strips away formatting noise while preserving meaningful structure such as headings, lists, and tables, reducing token usage by as much as 70 percent in favorable cases. Beyond token savings, mainstream LLMs are trained heavily on Markdown and tend to process it more accurately than raw PDF output. MarkItDown also ships an MCP server package that allows compatible AI clients and editors to trigger file conversion automatically, removing the need for manual export and re-upload steps.