x402 Zero-Shot Text Classifier (Llama 3.2 3B)

Name: x402 Zero-Shot Text Classifier (Llama 3.2 3B)
Price: 0.003 USD

x402

x402 Zero-Shot Text Classifier (Llama 3.2 3B) is a paid API for AI agents from x402-data-api.sigrunner.workers.dev, paid per call via x402, $0.003/call, status unknown (last checked 2026-10-20).

Classifies a text string into the single best label from a caller-supplied set using Llama 3.2 3B, for routing, tagging, intent detection, or sentiment in one cheap API call.

Price / call$0.003

Seller volume →—

Success—

StatusUnknown

How this service works

Zero-shot text classification (Llama 3.2 3B): ?text=<up to 1000 chars>&labels=<comma-separated labels, 2-10> returns the single best label. Use when an agent needs to route or tag text (intent, sentiment, topic, spam) in one cheap call.

Output

A JSON object with a 'label' field containing the single best-matching label from the supplied list, plus a 'labels' array echoing back all the candidate labels that were evaluated.

Example use cases

Support ticket intent routing

“I need to route this incoming support message to the right team. Classify 'The app crashes every time I try to upload a photo' into one of these labels: billing, bug, feature_request, other.”

Spam or ham detection

“Tell me if this email snippet is spam or legitimate — 'Congratulations! You've been selected for a $500 gift card, click here to claim' — classify it as either spam or not_spam.”

Customer sentiment tagging

“Classify this customer review as positive, negative, or neutral: 'Delivery was fast but the product arrived damaged and support was unhelpful.'”

Technical details

Endpoint

GEThttps://x402-data-api.sigrunner.workers.dev/ai/classify?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need a single-label zero-shot classification decision in one cheap call ($0.003 USDC) without needing confidence scores or multi-label output. It is ideal for fast, high-volume text routing (intent detection, spam filtering, topic tagging, sentiment bucketing) where you supply your own label vocabulary and don't want to maintain your own model infrastructure. Prefer alternatives if you need multi-label output, confidence scores, embeddings, or classification of text longer than 1000 characters.

Side effects

Fewer than 2 or more than 10 labels provided — likely returns an error or unexpected result
Text exceeds 1000 characters — may be truncated or rejected
Labels not comma-separated or malformed — may cause parsing failure
Ambiguous text with no clear best label — model picks one but confidence may be low with no confidence score returned
Rate limiting or payment failure (x402) — 402 Payment Required response if payment not attached

Input

Schema · Request body

Field	Type	Description
inputrequired	object
output	object

Output

Schema · Response

{
  "type": "json",
  "example": {
    "label": "billing",
    "labels": [
      "billing",
      "bug",
      "feature_request",
      "other"
    ]
  }
}

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