LLM inference

LLM inference proxy - send an OpenAI-format chat/completions request and get a response from GPT-4o-mini. Supports vision (up to 2 image URLs, low detail) and structured output (response_format: json_object or json_schema). No API key needed; pay per call via x402. Input capped at 16k chars, output at 4096 tokens.

Input

Example output

{
  "model": "gpt-4o-mini",
  "provider": "openai",
  "usage": {
    "prompt_tokens": 12,
    "completion_tokens": 8,
    "total_tokens": 20
  },
  "choices": [
    {
      "message": {
        "role": "assistant",
        "content": "Hello! How can I help you today?"
      },
      "finish_reason": "stop"
    }
  ]
}

Try it - see the 402 challenge (free)

curl -i -X POST https://agent402.tools/api/llm \
  -H "Content-Type: application/json" \
  -d '{"model":"gpt-4o-mini","messages":[{"role":"user","content":"Say hello in one sentence."}],"max_tokens":64}'

The response is HTTP 402 Payment Required with exact payment requirements. Any x402 v2 client pays automatically and retries:

Paid call (JavaScript agent)

import { wrapFetchWithPayment } from "@x402/fetch";
import { x402Client } from "@x402/core/client";
import { registerExactEvmScheme } from "@x402/evm/exact/client";
import { privateKeyToAccount } from "viem/accounts";

const client = new x402Client();
registerExactEvmScheme(client, { signer: privateKeyToAccount(KEY) });
const payFetch = wrapFetchWithPayment(fetch, client);

const res = await payFetch("https://agent402.tools/api/llm", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    "model": "gpt-4o-mini",
    "messages": [
      {
        "role": "user",
        "content": "Say hello in one sentence."
      }
    ],
    "max_tokens": 64
  }),
});

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