GPT-6 Luna API

Use model: "gpt-6-luna" to call GPT-6 Luna. The examples below send a synchronous request using OpenAI Chat Completions.

These are integration examples, not recorded model outputs. Keep the key in your server environment as BEATAPI_API_KEY; replace <BEATAPI_API_KEY> in the cURL example with your own key. Do not put the key in browser code.

Send a request

POST https://api.beatapi.io/v1/chat/completions

Install openai in your server project.

import OpenAI from "openai";
const apiKey = process.env.BEATAPI_API_KEY;
if (!apiKey) throw new Error("Set BEATAPI_API_KEY in your server environment.");
const client = new OpenAI({
  apiKey,
  baseURL: "https://api.beatapi.io/v1",
});
const completion = await client.chat.completions.create({
  model: "gpt-6-luna",
  messages: [{ role: "user", content: "Classify the following support ticket using exactly one label: account_access, billing, or product_defect. Return the label and a brief reason. Ticket: I reset my password, but the new password still does not let me sign in." }],
});
const choice = completion.choices[0];
if (!choice || choice.finish_reason !== "stop") {
  throw new Error(`Handle the finish reason before accepting the result: ${choice?.finish_reason}`);
}
const text = choice.message.content;
if (typeof text !== "string" || !text.trim()) {
  throw new Error("The response contains no text answer.");
}
console.log(text);
console.log({ id: completion.id, model: completion.model, usage: completion.usage });

cURL

curl --request POST \
  --url https://api.beatapi.io/v1/chat/completions \
  --header 'Authorization: Bearer <BEATAPI_API_KEY>' \
  --header 'Content-Type: application/json' \
  --data '{
  "model": "gpt-6-luna",
  "messages": [
    {
      "role": "user",
      "content": "Classify the following support ticket using exactly one label: account_access, billing, or product_defect. Return the label and a brief reason. Ticket: I reset my password, but the new password still does not let me sign in."
    }
  ]
}'

Read and validate the result

Read the answer from choices[0].message.content, then inspect the actual finish_reason and usage. The example accepts stop; a truncated answer or a tool request needs separate application handling. Validate the returned label against your allowed labels before using it to route a ticket.

An HTTP 200 alone does not prove that the answer satisfies your task. Validate the content in your application. These text requests return directly; do not poll /v1/tasks for them.

Check GET /v1/models for the model IDs available to your account. This minimal example does not claim support for additional reasoning, sampling, cache-control or hosted-tool settings.