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Text Similarity Checker

AI Runs in browser

Compare two texts with AI semantic similarity — detects duplicate content and plagiarism with a 0–100% match score.

Last updated 01 Apr 2026

Compare two texts using AI embeddings to measure semantic similarity. Returns a score from 0 (completely different meaning) to 1 (identical meaning) using MiniLM-L6-v2 sentence transformers. Unlike keyword matching, it understands meaning — two paraphrased sentences score highly even with different words. Runs in your browser.

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~21.9 MB download

Enter two texts above to compare their semantic similarity
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How to use

  1. 1

    Enter the first text

    Type or paste your first text in the Text A field. Works with single sentences or multi-paragraph passages.

  2. 2

    Enter the second text

    Type or paste the text you want to compare in the Text B field.

  3. 3

    Click Compare

    Press Compare to download the AI model (first use only, ~23 MB) and calculate the semantic similarity score.

  4. 4

    Read the similarity score

    The score ranges from 0.00 (completely unrelated) to 1.00 (identical meaning), with an interpretation label such as Very Similar, Moderately Similar, or Unrelated.

Frequently asked questions

What is semantic similarity?
Semantic similarity measures how closely two texts match in meaning, not just in exact wording. Two texts can be highly similar even if they use completely different words — for example, 'The cat sat on the mat' and 'A feline rested on a rug' would score very highly.
What does a score of 1.0 mean?
A score of 1.0 means the texts have identical or nearly identical meaning. A score of 0.0 means they are completely unrelated in meaning. In practice, most pairs of related texts score between 0.5 and 0.9.
How is this different from a plagiarism checker?
Traditional plagiarism checkers look for exact or near-exact word matches. This tool measures semantic meaning — it can detect paraphrased plagiarism where the wording is changed but the meaning is copied. However, it does not compare against a database of existing documents.
Is my text sent to a server?
No. All processing happens locally in your browser. The AI model runs on your device after a one-time download — your text is never transmitted to any server.
What AI model is used?
This tool uses all-MiniLM-L6-v2, a lightweight sentence embedding model that converts text into 384-dimensional vectors and compares them using cosine similarity. It is one of the most widely used sentence transformers in NLP research.
Can I compare texts in different languages?
The MiniLM model is primarily trained on English text. For cross-language comparison, results may be less accurate. For best results, compare texts in the same language.
What is cosine similarity?
Cosine similarity measures the angle between two vectors in high-dimensional space. A cosine of 1.0 means the vectors point in exactly the same direction (identical meaning); 0.0 means they are perpendicular (unrelated meaning).
Can it detect AI-generated paraphrasing?
It can detect semantic overlap between any two texts, including paraphrased content. However, sophisticated AI rewrites that preserve structure while altering surface form may score lower than simple paraphrases.
Does it work on mobile?
Yes, on modern mobile browsers that support WebAssembly. The 23 MB model download applies on first use — we recommend Wi-Fi for the initial load.

Semantic text similarity goes beyond keyword matching. Instead of comparing

exact words, this tool uses a neural network to understand the meaning behind

your text. Two sentences that say the same thing with completely different words

will score highly; two sentences that share words but mean different things

will score low.

Powered by the all-MiniLM-L6-v2 model — a compact sentence transformer that

converts text into 384-dimensional embedding vectors. The cosine similarity

between these vectors gives a score from 0 to 1 that captures how closely two

texts match in meaning.

Common use cases include: plagiarism detection in academic submissions, duplicate

content identification for SEO (detecting near-duplicate pages that dilute rankings),

semantic search evaluation, checking whether a paraphrase accurately preserves

meaning, and NLP research and benchmarking.

All processing runs locally in your browser after a one-time ~23 MB model download.

Your text is never sent to any server. The model caches so subsequent visits load

instantly.

Who is this for? Students checking paraphrase accuracy, SEO managers identifying

near-duplicate content, developers evaluating semantic search pipelines, and

researchers working in NLP and content analysis.

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