Get a complete statistical breakdown of any text — characters, words, sentences, paragraphs, unique words, reading and speaking time, Flesch Reading Ease score, lexical density, and the top 10 most frequent words.
A raw word count answers exactly one question — how long is this text — but writers, editors, and content teams routinely need to answer several other questions at once: is this readable for my audience, does it repeat itself too much, how long will it take to read aloud, and which words dominate the piece. Pulling each of these answers from a separate single-purpose tool is slow and disjointed, which is exactly the gap this tool is built to close — one paste of text produces every commonly needed statistic simultaneously, computed consistently from the exact same input.
This is particularly useful during editing and revision, where several of these metrics tend to matter together: a piece that scores well on word count but poorly on lexical density and readability may look 'done' by length alone while still needing real editorial work, and seeing all these numbers side by side surfaces that kind of gap immediately rather than requiring several separate checks.
Lexical density, as computed here, is the percentage of your total words that are unique — a text of 100 words using 80 distinct words has a lexical density of 80%, while a text of 100 words that repeats the same 20 words over and over has a lexical density of just 20%. This is formally known as a type-token ratio in linguistics, and it's a well-established way of quantifying vocabulary variety in a piece of writing.
Higher lexical density generally correlates with richer, more varied vocabulary, which readers often (though not universally) associate with more sophisticated or engaging writing — but it's not a metric to blindly maximize. Very high lexical density in longer texts can also indicate a piece that avoids helpful repetition of key terms (which is sometimes genuinely useful for clarity and SEO), and very short texts naturally score high simply because there hasn't been room for words to repeat yet. It's most useful as a comparative metric — tracking how it shifts across drafts of the same piece — rather than as an absolute target number.
The Flesch Reading Ease score included in this tool's results comes from a formula developed by Rudolf Flesch in 1948 that combines two structural signals: average sentence length (a proxy for syntactic complexity) and average syllables per word (a proxy for vocabulary difficulty). Neither signal directly measures meaning or comprehension — the formula has no idea whether your sentences make logical sense — but decades of validation studies have shown these two structural proxies correlate strongly with how difficult text actually is to read for a general audience, which is why the formula remains widely used today across publishing, education, government, and healthcare communication guidelines.
A practical implication worth knowing: you can meaningfully raise your Flesch score just by shortening sentences and choosing shorter words, without necessarily making the underlying content any less sophisticated — this is a legitimate editing technique used by professional editors specifically to make complex ideas more accessible, not a way to dumb down content.
These two estimates exist for genuinely different use cases and shouldn't be used interchangeably. Reading time (200 words per minute) estimates how long a visitor will spend silently reading your article, blog post, or documentation — useful for setting reader expectations ('8 min read') or estimating how a long piece will perform against shrinking attention spans. Speaking time (130 words per minute) estimates how long the same text would take to deliver aloud at a natural, comprehensible pace — useful for scripting a presentation, timing a podcast segment, or making sure a voiceover script fits a video's target length.
The gap between the two — roughly 35% slower for speech than silent reading — reflects genuine physical constraints: speech requires breath, articulation, and pacing that reading doesn't, and a speaker who reads a script at silent-reading speed will sound rushed and be harder to understand. If you're adapting written content into a spoken format, always plan around the speaking-time estimate, not the reading-time one.
The top-10 most frequent words list is one of the fastest ways to spot unintentional repetition in a piece of writing — a specific word appearing far more often than expected, once you look past the usual high-frequency function words ('the,' 'and,' 'a'), often signals either a genuinely important recurring theme (which is fine) or an editing oversight where a word got overused simply because it was the first one that came to mind each time (which is worth fixing).
Because this tool counts every word including common function words, the very top of the list will almost always be dominated by those short, unavoidable words in any piece of ordinary English prose — that's expected and not meaningful on its own. The more useful signal usually comes from looking a bit further down the list, at the first content words (nouns, verbs, adjectives) that appear, since an unexpectedly high count there is what typically points to real repetition worth addressing in a revision pass.
Text Statistics gives a full statistical breakdown of any text. These related tools cover other text analysis and formatting needs.