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Text Similarity – Cosine (Word-Count Vectors)
x402
Text Similarity – Cosine (Word-Count Vectors) is a paid API for AI agents from text-similarity.x402tookit.com, paid per call via x402, $0.02/call, status unknown (last checked 2026-10-21).
Computes cosine similarity between two texts using word-count (bag-of-words) vectors, returning the similarity score, dot product, vector norms, and shared term count.
Price / call$0.02
Success—
StatusUnknown
How this service works
Cosine similarity of the word-count vectors of two texts, with shared terms and vector norms.
Output
Returns a JSON object containing the cosine similarity score (0–1), dot product of the two word-count vectors, L2 norms of each vector (normA, normB), number of shared terms, distinct token counts for each text, token counts, a human-readable summary and verdict, the ignoreCase setting used, and optional degraded/disclaimer flags.
Example use cases
Duplicate support ticket detection
“Check if these two support tickets are essentially saying the same thing — Text A: 'My app keeps crashing every time I open it on iOS 17' and Text B: 'The application crashes on launch on my iPhone running iOS 17' — give me a cosine similarity score so I can decide if they should be merged.”
Plagiarism check on student submissions
“I need to compare two student essay excerpts for similarity. Text A is 'Climate change is driven by greenhouse gas emissions from human activities such as burning fossil fuels.' and Text B is 'Human activities, particularly the burning of fossil fuels, are the primary cause of greenhouse gas emissions and climate change.' Can you get the cosine similarity between them?”
Product description deduplication
“Tell me how similar these two product descriptions are so I can decide if they're duplicates: Text A is 'Wireless Bluetooth headphones with 30-hour battery life and active noise cancellation' and Text B is 'Noise-cancelling Bluetooth headphones offering 30 hours of wireless battery life' — use case-insensitive comparison.”
Technical details
Endpoint
GEThttps://text-similarity.x402tookit.com/cosine-tokens?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, interpretable word-frequency-based similarity score — especially for duplicate detection, near-match filtering, or any task where term overlap matters more than semantic meaning. It is faster and cheaper than embedding-based similarity and produces transparent intermediate values (norms, dot product, shared terms) useful for debugging or ranking. Prefer it over Dice/Jaccard endpoints when you want a continuous similarity metric that accounts for term frequency rather than just presence/absence.
Side effects
- Missing required query parameters 'a' or 'b' returns an error
- Texts exceeding 2000 characters may be rejected or truncated
- Empty strings produce a zero-norm vector making cosine undefined (likely returns null or 0)
- Payment failure (x402) prevents the call from completing
- Very short single-word inputs may yield low-signal results
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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