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Text Similarity Checker
Measure how similar two texts are as a percentage.
Only in the first
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In both
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Only in the second
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Quick Tool Overview
The Text Similarity Checker reports four established similarity measures at once — Levenshtein, Jaccard, cosine and Dice — because each answers a different question, and where they disagree the disagreement is itself the useful finding.
- What it does
- Scores how similar two texts are, four different ways
- You give it
- Two texts or documents
- You get back
- Four percentage scores plus the shared and unique content
- Best for
- Fuzzy matching, deduplication and comparing drafts
About the Text Similarity Checker
"How similar are these?" needs a number, and the honest answer is that it depends which kind of similarity you mean. Two texts can share every word in a different order, or differ by one character in every word. Those are opposite results under different measures, and a single percentage hides that.
So this reports four: Levenshtein similarity, which counts the single-character edits needed to turn one into the other; Jaccard similarity, which compares the sets of words they use; cosine similarity over word frequencies, which is what search engines use; and Dice coefficient over character pairs, which is good at catching near-matching names. Each measures something real, and where they disagree the disagreement is the useful information.
Key features of the Text Similarity Checker
Four real algorithms
Levenshtein for near-identical strings, Jaccard and cosine for documents, Dice for fuzzy name matching.
Explains what each measures
So you can pick the score that fits your question rather than trusting a single number blindly.
Shared and unique words listed
The vocabulary each text has to itself, alongside what they hold in common.
Interpretation guidance
What a 90 per cent means versus a 40 per cent, so the figure translates into a decision.
Handles any length
From two product names to two full articles, with the expensive edit-distance calculation capped so long texts stay responsive.
How to use the Text Similarity Checker
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1
Paste both texts
The two versions or documents you want compared.
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2
Read the four scores
Levenshtein, Jaccard, cosine and Dice each measure similarity differently and are shown together.
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3
Check the shared content
The words both texts use and the words unique to each are listed below the scores.
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4
Interpret the spread
Where the four scores disagree, the reason is explained — that is usually the most useful finding.
Frequently asked questions
It depends on the question. Levenshtein for near-identical strings such as names or SKUs. Jaccard or cosine for documents, where word choice matters more than order. Dice for fuzzy name matching. The page explains what each one is measuring.
Above 90 per cent generally means the texts are near-identical with minor edits. Between 60 and 90 means substantial shared content. Below 40 usually means they only share common words such as "the" and "and", which every English text does.
It can tell you that two texts you already have are similar, which is a useful check. It cannot search the web for sources, so it is not a plagiarism detector — it is a comparison between the two documents you paste in.
Because they measure different things. Two texts with the same words in a shuffled order score high on Jaccard and low on Levenshtein. That disagreement tells you the content matches but the structure does not, which is genuinely useful.
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