Normalized edit distance
ClientLevenshtein distance divided by max length.
About Normalized edit distance
Levenshtein distance divided by max length. Processing runs in your browser—Toolcore does not need your paste for the core operation on this page.
Deterministic string metrics only; rephrased text may score differently than human judgment.
How to use
Paste or type in the main field, then read the output below. Use Load example when available, or open the page with ?q= / ?qb= so tickets and agents share the same input.
Nearby workflows on Toolcore
- Levenshtein distance — Compare two strings for edit distance and similarity score—local Levenshtein calculator. Useful when a second similarity metric helps decide deduplication.
- Jaccard similarity — Word-token Jaccard index between two texts—optional case folding, in your browser. Useful when a second similarity metric helps decide deduplication.
- Simple character ratio — Character overlap ratio between two strings. Useful when a second similarity metric helps decide deduplication.
- Token sort ratio — Fuzzy ratio after sorting word tokens (FuzzyWuzzy-style). Useful when a second similarity metric helps decide deduplication.
Common use cases
- Normalized edit distance for quick local checks without uploading data.
- Copy results into tickets, docs, or classroom notes.
Common mistakes to avoid
Unexpected input shape
See the intro and how-to notes for accepted formats.
FAQ
Is processing local?
Yes—this runs entirely in your browser.
Agent prefill?
Use q or qb for the main text field when supported.
More tools
Related utilities you can open in another tab—mostly client-side.
Levenshtein distance
ClientCompare two strings for edit distance and similarity score—local Levenshtein calculator.
Jaccard similarity
ClientWord-token Jaccard index between two texts—optional case folding, in your browser.
Simple character ratio
ClientCharacter overlap ratio between two strings.
Token sort ratio
ClientFuzzy ratio after sorting word tokens (FuzzyWuzzy-style).