Text Similarity — This tool uses a local sentence-embedding model loaded into your browser on first use. It maps each text to a vector; the similarity score is the cosine similarity between the two vectors.
Runs entirely on your device. The model is downloaded once to your browser cache, then runs offline. Nothing is sent to a server.
Was Text Similarity useful?
What is text similarity?
This tool uses a local sentence-embedding model loaded into your browser on first use. It maps each text to a vector; the similarity score is the cosine similarity between the two vectors.
Unlike a string-similarity check (which only sees literal characters), this tool sees meaning. "I love this product" and "This is amazing" score 0.7+; "I love this product" and "I hate everything" score 0.1 or lower. Inference runs in your browser; the model weights are cached after the first download.
How to use it
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Paste both texts
One per side. They can be any length — sentence, paragraph, or short article.
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Click Compare
The first run downloads the model. After that it is cached and runs in a fraction of a second.
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Read the verdict
A score from 0 to 1 plus a label: near-duplicate (≥0.95), similar (≥0.7), related (≥0.4), or unrelated (<0.4).
Examples
Two paraphrases
Same idea in different words.
A: "I love this product, it works great." / B: "This item is fantastic and functions perfectly."
Result: 0.78 — similar. The two sentences say the same thing in different words.
Two unrelated topics
Different subjects entirely.
A: "I love this product, it works great." / B: "The train arrives at noon."
Result: 0.04 — unrelated. The two sentences have no shared meaning.
Frequently asked questions
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Last reviewed: 2026-08-27