BYTETOOLS

Text Entropy Calculator

Calculate Shannon entropy in bits per character, word or byte. Shows maximum entropy, redundancy and a full symbol probability table.

Entropy

Paste some text to measure its Shannon entropy — the average number of bits needed to encode one character once you know how often each one appears.

What is the Text Entropy Calculator?

Shannon entropy measures how unpredictable a piece of text is. It answers the question: on average, how many bits do you need to encode one symbol once you know how often each symbol appears?

  • Shannon entropy H = −Σ p·log₂p in bits per symbol
  • Four bases: characters, case-insensitive characters, words or UTF-8 bytes
  • Reports maximum entropy log₂(n), redundancy and total information in bits
  • Sorted probability table with counts, probabilities and per-symbol bits
  • Handles single-symbol input honestly instead of dividing by zero
  • Counts characters by Unicode code point, so emoji are one symbol

How to use the Text Entropy Calculator

  1. 1

    Paste your text, password, token or sequence into the box.

  2. 2

    Choose whether to measure entropy over characters, case-insensitive characters, words or UTF-8 bytes.

  3. 3

    Read the entropy in bits per symbol, the maximum entropy and the redundancy.

  4. 4

    Scan the probability table to see which symbols dominate the text.

  5. 5

    Copy the summary if you need the numbers elsewhere.

About the Text Entropy Calculator

Shannon entropy measures how unpredictable a piece of text is. It answers the question: on average, how many bits do you need to encode one symbol once you know how often each symbol appears? The formula is H = −Σ p·log₂p, summed over every distinct symbol, and the answer is in bits.

This calculator computes it over characters, case-insensitive characters, words or raw UTF-8 bytes, and reports the maximum possible entropy log₂(n) for the number of distinct symbols found, the redundancy 1 − H/Hmax that tells you how predictable the text is, and the total information content in bits and bytes. A sorted table shows every symbol with its count, its probability and its individual information value.

It is used to estimate password strength, compare compressibility, analyse ciphertext and study language statistics. Everything is calculated in your browser, so passwords and tokens you paste are never uploaded — although a real password strength estimate should also account for dictionary words and common patterns, which entropy alone does not measure.

Frequently asked questions

What is Shannon entropy in simple terms?

It is the average number of bits needed to encode one symbol of your text, given how often each symbol appears. Text where every symbol is equally likely has maximum entropy; text that repeats one symbol over and over has an entropy of zero.

How do I calculate the entropy of a password?

Paste it in and read the bits-per-character figure, then multiply by the length to get total bits — the tool shows that as “total information”. Be careful though: this measures only symbol frequency, so a long dictionary word can score well while still being trivially guessable.

What does redundancy mean here?

Redundancy is 1 − H/Hmax, the share of the text that is predictable given the symbols it uses. English prose typically shows high redundancy, while compressed or encrypted data shows almost none.

Why is the redundancy shown as a dash?

Because every symbol in the text is the same. With only one distinct symbol the maximum entropy log₂(1) is zero, so dividing by it has no meaning — the tool shows a dash instead of an invented number.

Is my text or password sent to a server?

No. The calculation runs entirely in your browser with JavaScript, so anything you paste stays on your device and the page keeps working offline.

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