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QuickBuild LLM Chat Completions
x402
QuickBuild LLM Chat Completions is a paid API for AI agents from quickbuild.quynguyenbac.workers.dev, paid per call via x402, $0.003/call, status unknown (last checked 2026-10-21).
Runs a prompt or chat message history through a hosted open-source LLM (Llama, Mistral, or Granite) and returns an OpenAI-compatible chat completion response
Price / call$0.003
Success—
StatusUnknown
How this service works
Landing pages ($15), Google Sheets/Apps Script automation ($10), scrapers, scripts & Telegram bots (from $15), broken-link + sitemap audit reports ($10). See a working demo first, pay after.
Output
An OpenAI-compatible chat.completion JSON object containing the assistant's reply text, the model used, token counts (prompt, completion, total), a finish_reason, and a unique completion ID. When stream=true, returns server-sent events with chat.completion.chunk objects ending with [DONE].
Example use cases
Micro-task text summarization
“Use the llama-3.2-3b model to summarize this paragraph in two sentences, keep it under 100 tokens: 'Cloudflare Workers run serverless code at the edge, close to users worldwide, with low latency and automatic scaling without managing servers.'”
Structured JSON extraction from prose
“Send this to the Llama 3.1 8B model with response_format set to json_object: 'Extract the name, date, and amount from this invoice text: John Doe, invoice dated March 15 2025, total $4,200.'”
System-prompted customer FAQ bot
“Call the mistral-7b model with a system message saying 'You are a concise customer support agent for a SaaS product' and a user message asking 'How do I reset my password?' — cap it at 200 tokens.”
Technical details
Endpoint
GEThttps://quickbuild.quynguyenbac.workers.dev/v1/chat/completions?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need cheap ($0.003/call), OpenAI-compatible LLM inference on small open-source models (Llama, Mistral, Granite) without managing infrastructure, especially for micro-tasks where GPT-4-class capability is unnecessary. Prefer it over heavier LLM APIs when cost and compatibility matter more than frontier-model quality, and when a 512-token output cap is acceptable.
Side effects
- Unknown model name falls back silently to default llama-3.1-8b rather than erroring
- max_tokens hard-capped at 512 — requests for longer outputs are silently truncated
- Missing payment header (x402) returns HTTP 402 before inference runs
- Prompt or messages array omitted — falls back to a built-in sample question rather than erroring
- Stream mode delivers the full answer in one chunk, not token-by-token, despite SSE framing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"id": "chatcmpl-3f2a9c",
"model": "@cf/meta/llama-3.1-8b-instruct-fp8-fast",
"usage": {
"total_tokens": 49,
"prompt_tokens": 22,
"completion_tokens": 27
},
"object": "chat.completion",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "HTTP 402 Payment Required means the server wants payment before it will serve the requested resource."
},
"finish_reason": "stop"
}
],
"created": 1792526400
}
}Tags
x402
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