⌘ K
frequency · readability · density

Analyze text: readability, frequency, density

Words, sentences, paragraphs, unique-word count, most-frequent word, and Flesch reading ease — all live, all local.

Text Statistics Workspace
Words
29
20 unique
Sentences
3
1 paragraph
Avg word length
4.31
chars
Flesch ease
89.1
standard
input · text
output · statistics
Words29Sentences3Paragraphs1Avg word length4.31 charsUnique words20Most frequentthe (5×)Flesch reading ease89.1 (standard)
Quick Start

How to analyze text statistics

Beyond word count — this tool surfaces vocabulary diversity (unique words), most frequent word, and the Flesch reading ease score for any prose.

  1. 01

    Paste text

    Anything — blog drafts, emails, reports.

    Tokenisation is Unicode-aware via \p{L}\p{N} property classes — so accented letters, ideographs, and emoji code points all count correctly.

    any textUTF-8Unicodecode pointslive
    input · texttext · UTF-8
    The quick brown fox…
    loaded · UTF-8UTF-8 · ready
  2. 02

    Live counts

    Words, sentences, paragraphs, average word length update as you type.

    Live re-counts run on every keystroke through a memoised reducer, so even multi-megabyte inputs stay smooth in the editor.

    livecountsmemoisedoptionssnappy
    counts · optionslive · live
    Words: 42
Sentences: 5
    options applied · livelive
  3. 03

    Frequency analysis

    Unique-word count and most-frequent word reveal vocabulary diversity.

    Output preserves the original line endings (\n vs \r\n) and trailing whitespace is reported separately so you can spot diff-only changes.

    uniquetopline endingswhitespacediff-clean
    frequency · counteddiversity · ready
    Unique: 38
Top: "the" (5×)
    counted · livecounted
  4. 04

    Flesch reading ease

    90+ = very easy; 60-90 = standard; 30-60 = difficult; under 30 = very difficult. Higher = simpler prose.

    Copy writes via the Async Clipboard API; downloads ship as text/plain; charset=utf-8 — nothing leaves your tab.

    readabilityclipboardtext/plainblobclient-side
    score · copiedease · done
    Flesch: 72.4 (standard)
    ready · ready to shipsuccess
Pro tips

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.

Related toolsWord CounterCharacter CounterReadability ScoreKeyword DensityWord FrequencyReading Time
Use Cases

When to analyze text statistics

Anywhere prose quality, readability, or vocabulary depth matters.

Blog SEO

Hit target word/sentence count and target Flesch ease for your 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-correct

Documentation

Aim for Flesch >60 — technical docs that don't read like research papers. 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-clean

Marketing copy

Cut filler — identify the words you overuse. 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 counts

Email drafts

Confirm tone is appropriately accessible before sending. 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-aware

Academic writing

Target 30-50 Flesch for academic but readable. 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-accurate

Press releases

PR usually targets standard readability (60-80) for journalist accessibility. 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.

lossless
FAQ

Frequently asked questions

A standard readability score: 206.835 − 1.015 × (words/sentences) − 84.6 × (syllables/words). Higher = easier prose. Range typically 0-100.
Heuristic — vowel groups, minus silent e, plus a few edge cases. Not perfect but good enough for trend analysis.
Flesch is calibrated for English. Word/sentence counts still work for other languages but the readability score won't be meaningful.
Yes — frequency analysis lowercases all words first.
No. All analysis runs locally.
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