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SimHash Near-Duplicate Detection
x402
SimHash Near-Duplicate Detection is a paid API for AI agents from api.24klabs.ai, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-13).
Computes a 64-bit SimHash fingerprint for text content, or compares two texts returning Hamming distance and a 0–1 similarity score for near-duplicate detection.
Price / call$0.002
Success—
StatusUnknown
How this service works
Compute a 64-bit SimHash fingerprint for content, or compare two texts and return the Hamming distance and a 0-1 similarity score for near-duplicate detection.
Output
Returns a 64-bit SimHash fingerprint for a single input, or when comparing two texts returns the Hamming distance (integer, lower = more similar) and a 0–1 similarity score (higher = more similar), enabling threshold-based near-duplicate detection.
Example use cases
Deduplicating scraped news articles
“I've scraped two news articles and want to know if they're near-duplicates — compute the SimHash similarity between 'Scientists discover new exoplanet in habitable zone' and 'Researchers find new planet in star's habitable zone' and give me the Hamming distance and similarity score.”
Fingerprinting documents for dedup pipeline
“Generate a 64-bit SimHash fingerprint for this paragraph so I can store it and check future documents against it: 'Our return policy allows refunds within 30 days of purchase with original receipt.'”
Plagiarism check on user submissions
“Check whether these two student essay intros are near-duplicates by comparing their SimHash — 'The industrial revolution transformed society through mechanization' versus 'Mechanization during the industrial revolution fundamentally changed society' — and give me the similarity score.”
Technical details
Endpoint
POSThttps://api.24klabs.ai/api/v1/simhash-dedupWhen to prefer
Use this endpoint when you need fast, scalable near-duplicate detection for text without requiring a full semantic embedding model — ideal for deduplication pipelines, plagiarism checks, or content fingerprinting where fuzzy matching is needed. Prefer this over exact-hash approaches (MD5/SHA) when you need to detect near-duplicates with minor edits, and prefer it over expensive embedding-based similarity when exact semantic understanding is not required.
Side effects
- Empty or missing text input returns a validation error
- Extremely long texts may be truncated or cause timeout
- Non-text binary content is not supported
- Similarity score of exactly 0 or 1 may be returned for trivially identical or completely unrelated texts
- Payment failure via x402 results in 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| a | string | |
| b | string |
Output
Schema · Response
{
"type": "json",
"example": {
"distance": 3,
"similarity": 0.95
}
}Tags
x402
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