Text Similarity Checker — Free, Browser-Only
Compare two texts for similarity. Cosine, Jaccard, Levenshtein metrics. No upload, no signup.
About Text Similarity Checker
Text similarity measures how close two pieces of text are. Different metrics capture different aspects: Levenshtein (edit distance — number of single-char changes to transform one into the other), Jaccard (word-set overlap — independent of order), Cosine (vector-space angle — accounts for term frequency). Useful for plagiarism / duplicate detection, near-duplicate finding in documents, and similarity-based search. The ZTools Text Similarity Checker runs all three metrics in the browser, displays the score (0-1) for each, and highlights the matching / different parts.
Use cases
- Detect near-duplicate articles. Two scraped articles — are they the same content with minor edits? Cosine similarity > 0.9 suggests yes.
- Compare draft revisions. How much did the revision change? Levenshtein gives a precise edit distance.
- Plagiarism check at the paragraph level. Paragraph A vs paragraph B — Jaccard score reveals significant word overlap.
- Find similar customer-support tickets. New ticket has cosine similarity > 0.7 with an old resolved one — surface the resolution.
How it works
- Paste text A and text B. Two text areas. Any size up to several MB.
- Compute metrics. Levenshtein (edit distance), normalized to 0-1; Jaccard (intersection / union of word sets); Cosine (TF vectors of term frequencies).
- Display. Each metric with score + interpretation. Highlighted diff showing matching tokens.
Examples
Input: "hello world" vs "hello world!"
Output: Levenshtein 0.92 (1 edit). Jaccard 1.0 (same word set). Cosine 0.99.
Input: Two paraphrased paragraphs
Output: Levenshtein low (~0.4 — many edits). Jaccard moderate (~0.6 — some shared words). Cosine moderate (~0.7 — similar topic).
Input: Identical text
Output: All metrics = 1.0.
Frequently asked questions
Which metric for plagiarism?
Cosine — captures topic + word frequency. Levenshtein flags too many false negatives on paraphrased content.
Will it detect AI-generated text?
No — these are similarity metrics between two texts, not AI-detection. Use a dedicated AI detector for that (and they're unreliable).
Maximum text size?
Levenshtein is O(n*m) — slows down for million-char texts. Jaccard / cosine are O(n+m) — handle large inputs.
Privacy?
All client-side.
Pro tips
- For short text, Levenshtein is most informative.
- For long documents, cosine + Jaccard scale better; ignore Levenshtein.
- For semantic similarity (paraphrases), embedding-based tools (sentence-transformers) beat simple word-overlap metrics.
- For plagiarism detection at scale, dedicated services (Turnitin, Grammarly) compare against vast corpora — this tool is for pairwise comparison only.
Reviewed by Ahsan Mahmood · Last updated 2026-05-06 · Part of ZTools.
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