What is Levenshtein distance?

The Levenshtein distance between two strings is the minimum number of single-character edits (insertions, deletions, or substitutions) required to change one string into the other. It was developed by Vladimir Levenshtein in 1965.

How is the similarity percentage calculated?

Similarity % = (maxLength - distance) / maxLength × 100. If two 7-character strings have a distance of 3, similarity is (7-3)/7 = 57.1%.

Is the comparison case-sensitive?

By default yes. Toggle the case-sensitive option off to compare strings in a case-insensitive manner (both are lowercased before comparison).

What is the distance matrix?

The matrix shows the dynamic programming table used to compute the edit distance. Each cell [i][j] represents the minimum edits to transform the first i characters of String A into the first j characters of String B. The bottom-right cell is the final distance.

String Similarity (Levenshtein)

Compare two strings and see Levenshtein edit distance, similarity score, and step-by-step operations.

How it works

1
Enter two strings

Type or paste the two strings you want to compare.
2
Review the results

See the edit distance, similarity percentage, and color-coded edit operations.
3
Explore the matrix

For short strings, expand the distance matrix to see the full dynamic programming table.

Common use cases

Classic example

kitten → sitting
Identical strings

hello → hello

About This Tool

Enter two strings and instantly see the Levenshtein edit distance -- the minimum number of single-character insertions, deletions, and substitutions needed to transform one string into the other. The tool also shows a percentage similarity score, a color-coded list of edit operations, and (for short strings) the full dynamic programming matrix.

The similarity percentage is calculated as: (maxLength - distance) / maxLength × 100. A distance of 0 means the strings are identical (100% similar). Higher distances indicate more differences.

Use cases include: fuzzy string matching for search suggestions, typo detection in form validation, comparing DNA/protein sequences, evaluating OCR accuracy, measuring how similar two text snippets are, and testing data deduplication logic.

The algorithm runs in O(n×m) time where n and m are the string lengths. For very long strings (1000+ characters), computation may take a moment. All processing is client-side.

More examples

Examples

Classic example

Input

kitten → sitting

Output

Distance: 3, Similarity: 57.1%, Ops: Substitute k→s, Keep i, Keep t, Keep t, Substitute e→i, Keep n, Insert g

Identical strings

Input

hello → hello

Output

Distance: 0, Similarity: 100%

Frequently Asked Questions

What is Levenshtein distance?: The Levenshtein distance between two strings is the minimum number of single-character edits (insertions, deletions, or substitutions) required to change one string into the other. It was developed by Vladimir Levenshtein in 1965.
How is the similarity percentage calculated?: Similarity % = (maxLength - distance) / maxLength × 100. If two 7-character strings have a distance of 3, similarity is (7-3)/7 = 57.1%.
Is the comparison case-sensitive?: By default yes. Toggle the case-sensitive option off to compare strings in a case-insensitive manner (both are lowercased before comparison).
What is the distance matrix?: The matrix shows the dynamic programming table used to compute the edit distance. Each cell [i][j] represents the minimum edits to transform the first i characters of String A into the first j characters of String B. The bottom-right cell is the final distance.

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