Free text entropy / predictability calculator. Computes Shannon entropy at the character and word level, n-gram entropy, and a compression-ratio estimate. Compare your text against typical human and AI-generated ranges.
Use when you suspect a text is repetitive but can't spot why. Word entropy catches phrase-level repetition; character entropy catches alphabet-level oddities.
Lexical diversity measures unique-word *count*; entropy measures unique-word *distribution*. Two texts can have identical TTR but very different entropy.
Shannon entropy is computed as H = −Σ p(x) · log₂ p(x) over the chosen symbol set (characters, words, or word bigrams).
Predictability is 1 − H/Hₘₐₓ — higher means more repetitive and easier to compress.
Compare modes: AI-generated text often shows lower word entropy and higher word-bigram repetition than human writing of similar length.
A measurement of unpredictability. Higher entropy means each symbol (word or character) carries more information; lower means the text is more repetitive.
~9–10 bits per word for typical prose; ~4 bits per character. Highly templated text falls below.
Different views surface different patterns. Word-bigram entropy catches phrase-level repetition; character entropy catches alphabet quirks.
ان پٹ
The cat sat on the mat. The cat sat on the mat. The cat sat on the mat.
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Word entropy: 2.32 bits — Very predictable (predictability 51%).
Only 6 unique words across 18 — the same six repeated three times. Maximum possible entropy is log₂(6) ≈ 2.58 bits, so 2.32 is close to maximum-given-vocab but predictability vs uniform is low.
% content words vs. function words.
analysisVocabulary variety — type‑token ratio.
analysisTotal + per‑word syllable counts.
analysisHighlight passive sentences in your writing.