We use cookies.This website uses essential cookies to operate core features. With your consent, we also use analytics cookies to understand traffic and improve the service. For more details, see our .
We use cookies.This website uses essential cookies to operate core features. With your consent, we also use analytics cookies to understand traffic and improve the service. For more details, see our .
Was this tool helpful to use?
Your feedback helps us make it better
Calculate the information entropy of text or data sequences to evaluate uncertainty and randomness.
Entropy is computed from character frequency and shown in bit/char
Enter text to see the result
Overview
Understand what the tool solves, how it works, and the boundaries of its data.
This calculator summarizes the frequency distribution of the text you enter. It counts each Unicode code point, estimates the probability of each distinct code point from its share of the input, and applies Shannon entropy. The result describes the observed sample distribution; it does not model the process that produced the text.
For a code point with observed frequency nᵢ in an input of N code points, its estimated probability is pᵢ = nᵢ/N. The calculation is H = −Σ pᵢ log₂(pᵢ). A code point that occurs more often contributes more of the distribution, while a perfectly even distribution across distinct code points gives a higher value than a concentrated one.
Entropy per character is displayed to six decimal places and is more precisely read as bits per code point. Total information multiplies the unrounded per-code-point result by the code-point count, then displays bits to three decimal places. This aggregate is a frequency-based estimate for the entered sample, not a file size or guaranteed encoding length. The other outputs are the number of distinct code points and the input length in code points.
Guide
Follow the workflow and verify inputs and outputs with practical examples.
Paste text, a code fragment, or another text sequence into the input field. The result updates as the input changes.
Read the per-code-point entropy, aggregate bit estimate, distinct code-point count, and total code-point count. The entropy value and aggregate can be copied from their result controls.
When comparing samples, apply the same preparation to each one. The calculation preserves case and includes spaces, line breaks, punctuation, and other entered code points; it does not normalize or clean the text.
For example, AAAA contains one distinct code point, so its empirical entropy is 0 bits per code point and the aggregate is 0 bits. In ABAB, the two code points each have probability one half, giving 1 bit per code point and 4 total bits.
Use cases
See how the tool fits into real work and everyday tasks.
Use a repeated sequence, then an evenly alternating sequence, to see how changing observed frequencies changes entropy while the method stays the same.
For exploratory analysis, compare samples using consistent choices about case, whitespace, punctuation, and line breaks. Treat the value as one descriptive statistic alongside the sample itself.
Use multilingual text or emoji to show that the reported length is based on code points, not necessarily the number of visible glyphs a reader perceives.
Q&A
Find concise answers to common questions and confusing cases.
The tool iterates Unicode code points. A displayed glyph can be formed from multiple code points, such as a base letter plus a combining mark, so the counted length may differ from a reader’s visual count. See the Unicode Consortium glossary for the code point definition.
Yes. Uppercase and lowercase letters are counted separately, and the tool does not convert case. Spaces, punctuation, and line breaks also participate in the frequency distribution.
No. This calculation describes frequencies in the one string supplied. It does not estimate how a person or password generator chooses secrets, model an attacker’s guesses, or assess account protections. NIST explains why estimating entropy for user-chosen passwords is challenging.
A short input still has a valid empirical frequency calculation, but it may be a poor representation of a larger source or process. The tool does not infer unseen symbols or correct for sampling effects.
Notes
Review scope, result limitations, and important precautions before use.
Do not treat this result as password-strength advice, a security test, a randomness certification, byte entropy, or a compression guarantee. It uses the entered sample’s code point frequencies only; it does not analyze sequence order, a source model, the encoding’s bytes, or an attacker’s strategy. Password security requires an appropriate generation and authentication context, and the value here cannot establish it.
Unicode code points are not the same as visible characters or grapheme clusters. If a comparison depends on normalization, case folding, or removing whitespace, do that consistently before entering each sample and document the choice.
Related
Discover related tools, collections, and available API capabilities.