Markdown vs HTML for LLMs

The token math

HTML carries a lot of structural noise that adds nothing for the model: <div class="foo bar baz">, inline styles, data attributes, repeated wrapper tags. Markdown encodes the same hierarchy in a fraction of the characters. Run any webpage through Markdown Converter and check the difference with the Token Counter.

Side-by-side

When Markdown is the right call

• You're pasting webpage content into ChatGPT or Claude for summarization or Q&A.
• You're building RAG over scraped websites — Markdown makes better chunks.
• The destination is also a Markdown system (Obsidian, Notion, GitHub).
• Token cost matters at all (it usually does).

When HTML is worth keeping

• The model needs to reason over attributes — pulling structured data out of microdata or schema.org markup.
• You're asking the model to produce HTML output (write a landing page, fix a broken tag).
• Tables are deeply nested and the row/column semantics matter precisely.

The conversion step

Use Markdown Converter to turn HTML, DOCX, or webpage text into clean Markdown in one paste. It strips classes, inline styles, and SPA wrapper divs while preserving headings, lists, and code. The output drops straight into any LLM chat with 30–60% fewer tokens than the original.

One more layer: compression

After converting to Markdown, run the result through Context Compressor for another 20–40% reduction. For very long inputs, the combination of HTML → Markdown → compressed often shrinks a prompt by 70%+ without changing what the model can answer.