Markdown to Word Count Converter

The Markdown to Word Count Converter strips Markdown syntax (headings `#`, emphasis `**bold**` and `*italic*`, links `[text](url)`, fenced code blocks ` ``` `, inline code `` ` ``, blockquotes `>`, lists `-`/`*`/`1.`, horizontal rules `---`, HTML tags) and returns accurate counts of words, characters (with and without spaces), sentences, paragraphs, plus estimated reading time at configurable speeds (default 225 WPM — Medium's industry-standard reading speed). Why strip Markdown first: raw character counts on Markdown source inflate by 10–30% from syntax (`**bold**` adds 4 characters, `[link](https://example.com)` adds 16+ characters that aren't part of the prose). Stripping yields counts that match what a reader actually sees on the rendered page — the metric writers, editors, and SEO tools care about. The converter uses a CommonMark-compliant tokenizer, so output matches what most renderers (GitHub, VS Code, Obsidian, Notion's export) produce. Reading time uses words ÷ WPM. Defaults: 225 WPM (Medium/standard publication, average adult reading prose at moderate pace), 200 WPM (web/SEO time-on-page estimate — Google's data suggests slightly slower reading on screens), 150 WPM (technical content with dense vocabulary), 300 WPM (light fiction or skimming). Sentence and paragraph counts help writers respect publication style guides that limit paragraph length (5–7 sentences for blogs, 2–3 for newsletters). Who this is for: Bloggers checking SEO-friendly post length (Google rewards 1,500–2,500 words for most topics), freelance writers billing per word, students respecting essay limits, content marketers planning newsletter segments, novelists tracking daily word goals, and technical writers checking documentation density. The tool runs entirely in-browser; paste sensitive drafts safely.

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1. 1Step 1 — Paste your Markdown into the input area
2. 2Step 2 — Calculator strips syntax: `#` headings, `**bold**`/`*italic*`, `[links](url)`, fenced code blocks, inline code, blockquotes, lists, HTML tags
3. 3Step 3 — Counts words by splitting stripped text on whitespace and filtering empty tokens
4. 4Step 4 — Counts characters two ways: with spaces (total) and without spaces (typographic content)
5. 5Step 5 — Counts sentences by detecting terminal punctuation outside abbreviations and decimals
6. 6Step 6 — Counts paragraphs as blocks separated by blank lines after stripping
7. 7Step 7 — Computes reading time = words ÷ configurable WPM (default 225)

• !Counting raw Markdown source (including syntax) instead of stripped prose — inflates count 10–30%
• !Forgetting to strip fenced code blocks for prose-only word counts (matters for tutorials)
• !Using one WPM for all content types — technical content needs lower WPM for accurate estimates
• !Forgetting that Medium and Google Docs count hyphenated words differently
• !Padding word count with filler to hit SEO targets — readers and Google both detect thin content