HubVibe LLM Raw Completion

x402

HubVibe LLM Raw Completion is a paid API for AI agents from hubvibe-io.com, paid per call via x402, $0.25/call, status unknown (last checked 2026-10-22).

Send a prompt (and optional system message) to a configurable LLM provider and model, receiving an unconstrained raw text completion.

Price / call$0.25

Seller volume →—

Success—

StatusUnknown

How this service works

LLM text generation / chat completion: a raw completion from your prompt with optional system message, max_tokens and temperature; Gemini by default, other configured models by provider and model. Use it for drafting, rewriting, translation, classification or any prompt, paid per call with no API key or account. Input: prompt. Answer grounded in your own text: llm.analyze.

Output

A raw text string as generated by the selected LLM provider and model, with no enforced structure or schema — the exact completion the model produces for the given prompt and parameters.

Example use cases

Custom chatbot response generation

“I need you to generate a customer support reply using Claude — use the system message 'You are a polite customer support agent for a SaaS company' and the prompt 'A user is asking how to reset their password. Write a helpful response.' Keep max_tokens at 512.”

Creative writing with controlled randomness

“Generate a short sci-fi story opening paragraph using Gemini with temperature set to 1.4 so it's more creative. The prompt is: 'A lone astronaut discovers a signal from a dead star that shouldn't exist.'”

Structured data extraction via free-form prompt

“Run this prompt through Claude with temperature 0.1 for consistency: 'Extract the company name, founding year, and CEO from this text: Acme Corp was founded in 1998 by Jane Doe, who continues to serve as CEO.' No system message needed, max 256 tokens.”

Technical details

Endpoint

POSThttps://hubvibe-io.com/work/llm/generate?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need a completely unconstrained LLM completion — no prescribed output format, schema, or analysis pipeline. It is ideal when you want full control over system message, provider selection (Gemini vs Claude), model variant, temperature, and token budget. Prefer this over llm.analyze when you do not want any opinionated wrapping around the model's response and want the raw model output directly.

Side effects

Invalid provider name returns an error (only 'gemini' and 'anthropic' are supported)
Requested model not available if Vertex AI Model Garden access not yet enabled for Anthropic
max_tokens out of range (must be 1–4096) returns a validation error
Temperature out of range (must be 0–2) returns a validation error
Prompt too long for model context window causes truncation or error
Payment failure via x402 protocol blocks the request
Provider or model quota/rate-limit reached causing temporary unavailability

Input

Schema · Request body

Field	Type	Description
model	string
prompt	string
system	string
language	string
provider	string
max_tokens	integer
temperature	number

Output

Schema · Response

{
  "type": "json",
  "example": {
    "result": {
      "text": "Here is a short poem about bees...",
      "model": "gemini-2.5-flash",
      "usage": {
        "input_tokens": 14,
        "output_tokens": 96
      },
      "provider": "gemini",
      "finish_reason": "STOP"
    },
    "status": "ok",
    "worker": "llm.generate",
    "price_usd": 0.25,
    "provenance": {
      "steps": [
        {
          "ms": 640,
          "ok": true,
          "step": "generate",
          "provider": "completion"
        }
      ],
      "attempts": 1,
      "elapsed_ms": 660,
      "providers_used": [
        "completion"
      ]
    },
    "receipt_id": "rcpt_9f1c2b3a4d5e6f70",
    "receipt_url": "/work/receipts/rcpt_9f1c2b3a4d5e6f70"
  }
}

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