Why convert Markdown to plain text
Markdown is great for writing and terrible for pasting. Emails, LinkedIn, X, WhatsApp, Instagram and most forms do not render it: you paste your text and stray ##, asterisks and brackets show up everywhere. This converter strips all that formatting and leaves only the content, ready to paste anywhere.
The number one use case today is cleaning up answers from ChatGPT, Claude or Gemini: AI assistants almost always reply in Markdown, and copying that output into a document or a message leaves symbols scattered all over. Paste it here and walk away with clean text in a second.
On top of that, the character and word counter measures the final result, not the draft full of symbols. That is the real number that counts against the 280-character limit on X, the 150 characters of an Instagram bio or the 220 of a LinkedIn headline. Everything runs in your browser: your text never leaves your device.
Reference: what each Markdown element becomes
| Element | Markdown syntax | Becomes |
|---|---|---|
| Heading | ## Title | Title |
| Bold | **text** / __text__ | text |
| Italic | *text* / _text_ | text |
| Link | [Genfy](https://example.com) | Genfy — or "Genfy (https://example.com)" with the URLs option |
| Image |  | logo (the alt text) |
| Inline code | `npm install` | npm install |
| Code block | ```js ... ``` | the inner code, without delimiters or language tag |
| List | - item | item (or "• item" with the bullets option) |
| Numbered list | 1. step | step |
| Table | | a | b | | a b — cells separated by spaces; the |---| separator row is removed |
| Quote | > phrase | phrase |
| Horizontal rule | --- / *** | (removed) |
The cleanup order matters
This is not just deleting symbols: the processing order avoids breaking your content. Code blocks are extracted first and protected, so an asterisk or a pipe inside code is never mistaken for formatting. Then images fall before links (they share syntax), followed by headings, bold and italics, quotes, lists and tables. Underscores are only treated as formatting at word boundaries, so snake_case and variable names survive whole. Finally, runs of multiple blank lines are collapsed to a maximum of two, preserving paragraphs without leaving giant gaps.
Typical use cases
- Cleaning a ChatGPT or other AI answer before sending it by email.
- Pasting a README or docs into LinkedIn, X or WhatsApp without stray symbols.
- Counting the real characters of a post before publishing it.
- Turning notes written in Markdown (Obsidian, Notion, Bear) into text for a formal document.
- Preparing text for systems that reject formatting: forms, CRMs, classified ads.