Measure reading difficulty 6 ways
Six classic readability formulas — Flesch reading ease and five grade-level scores side by side.
How to measure readability
Six classic formulas computed in parallel. Some are character-based (Coleman-Liau, ARI), others syllable-based (Flesch, FK, Fog, SMOG).
- 01
Paste text
Works best with at least a few sentences.
Tokenisation is Unicode-aware via
\p{L}\p{N}property classes — so accented letters, ideographs, and emoji code points all count correctly.paragraphUTF-8Unicodecode pointsliveinput · texttext · UTF-8The quick brown fox…
loaded · UTF-8UTF-8 · ready - 02
Six scores
All formulas compute side-by-side. Higher Flesch = easier; higher grade = harder.
Live re-counts run on every keystroke through a memoised reducer, so even multi-megabyte inputs stay smooth in the editor.
6 scoreslivememoisedoptionssnappyscores · optionsgrid · liveFlesch: 72 · FK: 8.4
options applied · livelive - 03
Interpret
Flesch 60-80 = standard adult reading. Grade 8-10 = popular press. Grade 12+ = technical writing.
Output preserves the original line endings (
\nvs\r\n) and trailing whitespace is reported separately so you can spot diff-only changes.interpretline endingswhitespacediff-cleanreproducibletarget · counted8 · readygrade 8 = popular
counted · livecounted - 04
Copy scores
Multi-line report ready for PR descriptions or editorial notes.
Copy writes via the Async Clipboard API; downloads ship as
text/plain; charset=utf-8— nothing leaves your tab.copyclipboardtext/plainblobclient-sideexport · copiedready · done✓ Copied
shipped · ready to shipsuccess
Text-processing fundamentals worth knowing
Tokenise with Unicode classes
Use \p{L}\p{N} with the u flag so accents, ideographs, and emoji count properly.
Normalise line endings
Convert \r\n to \n before diffing or hashing — Windows-vs-Unix endings produce phantom changes.
Mind invisible characters
Zero-width joiners, BOMs, and non-breaking spaces look identical to ASCII space. Strip them before comparison or storage.
Stream don’t slurp
For multi-megabyte text, iterate line-by-line rather than loading everything to memory — keeps the tab responsive.
Choose collation deliberately
Default JS sort is code-point order, not human alphabetical. Use Intl.Collator for locale-aware sorting.
Re-run after normalisation
NFC vs NFD changes byte length but not visible glyphs. String.prototype.normalize() before counts and comparisons.
When to measure readability
Anywhere clarity matters.
Blog editing
Hit 8-10 grade for general audience. Tokenisation uses Unicode property classes (\p{L}, \p{N}) with the u flag so accents, ideographs, and emoji code points all count correctly. Surrogate-pair-aware counts mean 🚀 weighs 1, not 2.
Unicode-correctMarketing copy
Target Flesch >60 for accessibility. Result updates as you type — paste and watch totals settle in milliseconds even on multi-megabyte inputs. Heavy work runs through a memoised reducer so the editor never janks.
diff-cleanDocumentation
Verify docs aren't too dense. Output normalises line endings to LF for clean diffs across platforms. Windows-vs-Unix CRLF mismatches disappear, which keeps git blame honest and review comments focused on real changes.
instant countsLegal contracts
Some jurisdictions require Flesch >50. Code-point counts are stable across NFC/NFD normalisation. Pair with the Reading Time Calculator to layer scan-time on top of raw counts, useful for editorial budgets and SEO meta-description limits.
platform-awareAcademic writing
Confirm appropriate complexity for journal target. All processing runs client-side — paste sensitive or pre-publish copy without it leaving the tab. The clipboard API writes UTF-8 text/plain so it survives transit to any editor or CMS.
code-point-accurateEmail outreach
Match the recipient's reading level. Same input plus same options always produces identical output, so the tool is safe to embed in CI scripts or doc-generation pipelines without race conditions.
losslessFrequently asked questions
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