String Similarity Checker
Compare two strings with four established similarity metrics at once: Levenshtein edit distance, Jaro-Winkler, Sorensen-Dice bigrams and longest common subsequence. Useful for deduplication and fuzzy matching thresholds.
Last reviewed by the Radiatus Cloud team
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There is no single measure of similar
Fuzzy matching goes wrong when a team picks one algorithm, tunes a threshold against a handful of examples, and then discovers the metric disagrees with human judgement on a different shape of data. Edit distance treats "Jon Smith" and "John Smith" as one character apart, which is close, but it treats "Smith, John" and "John Smith" as very distant even though a person would call them the same name. Choosing a metric is really choosing what kind of difference you want to forgive.
What each metric forgives
Levenshtein counts single character insertions, deletions and substitutions, so it is strongest for typos and OCR noise. Jaro-Winkler weights agreement near the start of the string, which makes it the traditional choice for personal names and short identifiers. Sorensen-Dice compares sets of adjacent character pairs, so it ignores word order entirely and handles reordered fields well. Longest common subsequence rewards preserved ordering while allowing arbitrary insertions, which suits comparing versions of a sentence.
Picking a threshold
Run your real hard cases through all four here before you write the rule. Pairs you consider matches should cluster above your cutoff under the metric you plan to ship, and pairs you consider distinct should cluster below it. If the two clusters overlap under every metric, the answer is usually normalisation first, such as case folding, accent stripping and token sorting, rather than a cleverer distance.
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Frequently Asked Questions
What is the difference between distance and similarity?
Distance counts operations and grows with difference, so 0 means identical. Similarity is normalised to a 0 to 1 scale where 1 means identical. This tool shows both, since libraries differ in which they return.
Which metric should I use for names?
Jaro-Winkler is the traditional choice because it gives extra weight to a shared prefix, which matches how personal names vary. For names given in different field orders, Sorensen-Dice usually behaves better because it ignores ordering.
Does case or whitespace affect the score?
Yes, all four metrics are literal by default. The normalise option lower cases, collapses runs of whitespace and trims, which is what most production pipelines do before comparing.
Is Levenshtein the same as Damerau-Levenshtein?
No. Damerau-Levenshtein additionally treats a transposition of two adjacent characters as a single operation, so "recieve" against "receive" costs one rather than two. This tool reports classic Levenshtein.
Are long strings safe to compare?
Levenshtein and LCS are quadratic, so very long inputs get slow. Inputs are capped at a few thousand characters each, which is far beyond the field lengths these metrics are normally used on.
Privacy & Security
Everything runs in your browser; nothing is uploaded.
How to Use
Paste two strings and press Compare. Each metric is scored from 0 to 1 with the raw distance alongside.
Disclaimer: This tool is provided "as is" without warranty of any kind. Results are for educational and utility purposes.
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