Inference402 Structured JSON Chat

x402

Inference402 Structured JSON Chat is a paid API for AI agents from inference402.worlddata402.workers.dev, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).

Generate LLM responses constrained to a caller-supplied JSON Schema, using Llama 3.3 70B, paid per-call via USDC on Base mainnet with no API key required.

Price / call$0.01

Seller volume →—

Success—

StatusUnknown

How this service works

Pay-per-call AI inference for agents: chat (Llama 3.3 70B / 3.1 8B), structured JSON, summarize, translate (incl. Egyptian Arabic), extract, classify, sentiment, keywords, rewrite, embeddings, moderation, vision and speech-to-text. USDC on Base mainnet over x402. No API key, no account.

Output

A JSON object with a 'data' field containing the schema-validated response, a 'model' field indicating which Llama model was used, a 'valid' boolean confirming schema compliance, and an 'attempts' integer showing how many inference retries were needed to produce a valid result.

Example use cases

Entity extraction from user profile text

“Parse this bio into a structured JSON object with fields for name, age, and city: 'Hi, I'm Sara Ahmed, 34, from Alexandria.' Use the schema {name: string, age: integer, city: string}.”

Classifying support ticket with typed output

“Classify this support ticket into a JSON object with fields 'category' (one of: billing, technical, account) and 'urgency' (low, medium, high): 'I can't log in and my subscription expires tomorrow.'”

Structured product data extraction

“Given this product description — 'Wireless noise-cancelling headphones, black, $149, in stock' — extract a JSON object with fields: product_name, color, price_usd, and in_stock (boolean).”

Technical details

Endpoint

GEThttps://inference402.worlddata402.workers.dev/v1/chat/json?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need LLM output that must conform to a specific JSON Schema without post-processing — especially in agentic pipelines where downstream code consumes typed data. It is ideal when you have no existing AI API key, want pay-per-call billing in USDC with no subscription, and need schema validation built into the inference loop. Prefer alternatives if you need streaming responses, very large token outputs (>768), or integration with OpenAI-compatible SDKs.

Side effects

Schema too large (>4000 chars serialized) causes rejection
Messages array empty or exceeds 50 items
max_tokens out of range (must be 1–768)
Payment failure or insufficient USDC balance on Base mainnet blocks the call
Model fails to produce schema-valid JSON after retry attempts, returning valid: false
Malformed JSON Schema causes parse errors
Rate limiting or Cloudflare Worker timeout on complex schemas

Input

Schema · Request body

Field	Type	Description
schema	object	JSON Schema the output must satisfy (≤4000 chars when serialized)
messages	array
max_tokens	integer

Output

Schema · Response

{
  "type": "json",
  "example": {
    "data": {
      "age": 34,
      "city": "Alexandria",
      "name": "Sara Ahmed"
    },
    "model": "@cf/meta/llama-3.3-70b-instruct-fp8-fast",
    "valid": true,
    "attempts": 1
  }
}

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