API Quickstart

Base URL and authentication

Configure your client to point to the correct endpoint and provide your key. Use https://api.getcheapai.cc/v1 as the base URL. Pass your API key in the Authorization header using the Bearer scheme. This setup works with any standard OpenAI-compatible SDK.

Sending your first request

Make a simple POST request to generate text. The model id is uncensored. Send a prompt and receive a completion. Errors and refusals are free, so you only pay for actual tokens. Use the curl example below to test connectivity.

Copycurl https://api.getcheapai.cc/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "uncensored",
    "messages": [{"role": "user", "content": "Generate 500 lines of dummy JSON data"}]
  }'

Python SDK integration

Initialize the client with your key and the base URL. Set the model to uncensored. Pass messages in the standard format. The SDK handles serialization automatically. This approach reduces boilerplate code significantly.

Copyfrom openai import OpenAI

client = OpenAI(base_url="https://api.getcheapai.cc/v1", api_key="YOUR_KEY")

resp = client.chat.completions.create(
    model="uncensored",
    messages=[{"role": "user", "content": "Summarise this thread without softening it."}],
)
print(resp.choices[0].message.content)

Node SDK usage

Import the library and configure the base URL. Call chat.completions.create with your messages. Handle the response asynchronously. This pattern fits well into modern JavaScript environments.

Copyimport OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.getcheapai.cc/v1", apiKey: process.env.API_KEY });

const resp = await client.chat.completions.create({
  model: "uncensored",
  messages: [{ role: "user", content: "Draft a villain monologue for my game." }],
});
console.log(resp.choices[0].message.content);

Streaming responses

Enable streaming by setting stream: true. Receive tokens as they are generated. This improves perceived latency for large outputs. The final chunk contains token usage statistics for billing.

Copystream = client.chat.completions.create(
    model="uncensored",
    messages=[{"role": "user", "content": "Tell the story in second person."}],
    stream=True,
)
for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

Limits and error handling

Handle specific HTTP status codes to manage your application flow. A 401 means your key is invalid. A 402 indicates insufficient credit. A 429 signals you hit the 300 requests per minute limit. The context window is 100,000 tokens total. Errors do not consume tokens.

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