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Vextorium Text Classification
x402
Vextorium Text Classification is a paid API for AI agents from api.vextorium.com, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-12).
Classifies a piece of text into one or more user-provided candidate labels using a local LLM, returning matched labels, a confidence score, and a reasoning explanation.
Price / call$0.005
Success—
StatusUnknown
How this service works
552 pay-per-call data APIs for AI agents: Solana and EVM on-chain data, tokenized stocks, DeFi, Hyperliquid, economy and official statistics, FX, validation, web and developer tools. Pay in USDC via x402.
Output
Returns a JSON object with the model name used (e.g. 'Qwen3-8B (local)'), an array of matched labels, a natural-language reason explaining why those labels were chosen, and a confidence score between 0 and 1.
Example use cases
Customer support ticket routing
“Classify this incoming support ticket into one of these categories: 'billing', 'technical_issue', 'shipping', or 'account_access'. The message is: 'My payment went through but my account still shows as unpaid and I can't access premium features.'”
Content moderation intent detection
“I need to know whether this user comment falls under 'hate_speech', 'spam', 'off_topic', or 'acceptable'. The comment is: 'Buy followers fast — click here now!!! Limited offer!' Allow only one label.”
Multi-label news article tagging
“Tag this news excerpt with all that apply from: 'economy', 'geopolitics', 'tech', 'climate', 'health'. Allow multiple labels. The text is: 'Central banks worldwide are raising rates while green energy subsidies face cuts amid slowing global growth.'”
Technical details
Endpoint
POSThttps://api.vextorium.com/v1/classify-text?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need zero-shot text classification with a natural language explanation and confidence score, without needing to train a custom model. Ideal for dynamic label sets defined at call time (e.g. support categories, intent buckets, topic tags). Prefer this over embedding-based classifiers when interpretability (the 'reason' field) matters. The pay-per-call x402 model makes it suitable for sporadic or agent-driven workloads without subscription overhead.
Side effects
- Fewer than 2 or more than 30 candidate labels provided — validation error
- Text field is missing or empty — required field error
- Label array is missing — required field error
- Ambiguous text leads to low confidence score with unexpected label assignment
- Non-English text may reduce accuracy depending on model language support
- Payment failure via x402 results in 402 response before processing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | Text to classify |
| labels | array | 2-30 candidate labels |
| multiple | boolean | Allow several labels (default false) |
Output
Schema · Response
{
"type": "json",
"example": {
"model": "Qwen3-4B (local)",
"labels": [
"reclamacion"
],
"reason": "El texto expresa un problema con una orden y falta de respuesta, lo cual son elementos ...",
"confidence": 0.95
}
}Tags
x402
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