How to use Text Similarity Checker
Compare two texts side-by-side and measure how similar they are using cosine and Jaccard similarity algorithms — all computed in your browser with no text ever uploaded to a server. Useful for detecting duplicate content, checking paraphrase overlap, comparing contract revisions, and identifying near-duplicate submissions. Results include a percentage similarity score for each algorithm, a list of shared terms, and vocabulary unique to each passage. Ideal for writers, editors, researchers, and SEO professionals.
- Paste the first text into the left input field.
- Paste the second text into the right input field.
- Click "Compare" to run both cosine and Jaccard similarity calculations.
- Review the similarity percentage and the breakdown of shared versus unique words.
- Use the highlighted diff view to spot paraphrased sentences or substituted phrasing.
Your data never leaves your device — 100% private processing.
Cosine similarity explained
Cosine similarity measures the angle between two text vectors in a high-dimensional word-frequency space. Each document is represented as a vector of term frequencies, and the cosine of the angle between them — (A·B) divided by (||A|| × ||B||) — gives a value between 0 (no word overlap) and 1 (identical word distribution). Because cosine similarity is length-agnostic, it works well even when one text is much longer than the other, making it the preferred metric for content duplication detection and document comparison.
| Metric | Formula | Length sensitivity | Best for |
|---|---|---|---|
| Cosine similarity | (A·B) / (||A|| × ||B||) | Low (length-agnostic) | Documents of unequal length |
| Jaccard similarity | |A ∩ B| / |A ∪ B| | High | Strict vocabulary overlap checks |
Jaccard similarity and when to use it
Jaccard similarity divides the number of unique words shared by both texts by the total number of unique words across both texts: |A ∩ B| / |A ∪ B|. A score of 1.0 means both texts use exactly the same vocabulary; 0 means no words in common. Jaccard is more sensitive to vocabulary differences than cosine similarity but less sensitive to term frequency. Use Jaccard for strict vocabulary-overlap checks and cosine when comparing documents of significantly different lengths.
Glossary
- Cosine similarity
- A metric measuring the cosine of the angle between two document term-frequency vectors, ranging from 0 (unrelated) to 1 (identical distribution), regardless of document length.
- Jaccard similarity
- The ratio of the intersection size to the union size of two word sets, measuring strict vocabulary overlap between 0 (no shared words) and 1 (identical vocabulary).
- Term frequency (TF)
- The count of how many times a word appears in a document, used as the basis for building vector representations of text for similarity comparison.
- Tokenization
- The process of splitting a text into individual words or tokens, typically lowercased and stripped of punctuation, before similarity algorithms are applied.
- Near-duplicate
- A document that shares most of its content with another — usually the result of paraphrasing, synonym substitution, or minor edits — detected by high similarity scores.
Related reading
Frequently Asked Questions
Why use Text Similarity Checker?
- Run both cosine and Jaccard similarity algorithms simultaneously on two text samples
- Identify shared vocabulary and unique terms in each passage without any server upload
- Length-agnostic cosine metric compares documents accurately even when sizes differ greatly
- Side-by-side highlighted diff exposes paraphrased sentences and synonym substitutions
Common use cases
- Check whether a freelance submission is too similar to a previously published article
- Verify that a paraphrased literature-review paragraph differs enough from the original source
- Compare two contract revisions to quantify how much phrasing changed between versions
- Identify near-duplicate product descriptions in a catalogue before running an SEO audit
- Evaluate whether two student-submitted essays share excessive vocabulary overlap
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