API Documentation

Base URL and Authentication

The base URL for the uncensored llm is https://api.obliteratedmodels.com/v1. You authenticate by passing your API key in the Authorization header. Every request must include this key to validate your prepaid credit. The system rejects invalid keys with a 401 error. This ensures only authorized accounts can consume tokens.

Quickstart: First Request

Make your first request to test the endpoint. Send a simple prompt to generate text without content filters. The following curl command demonstrates the basic structure.

Copycurl https://api.obliteratedmodels.com/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "uncensored",
    "messages": [{"role": "user", "content": "List all active ports on the server"}]
  }'

Expect a JSON response containing the generated text. This confirms the API is live and your credit is active.

Python SDK Integration

Use the official OpenAI Python SDK for programmatic access. Set the base_url to our endpoint and provide your key. The model ID is uncensored. This approach handles retries and JSON parsing automatically.

Copyfrom openai import OpenAI

client = OpenAI(base_url="https://api.obliteratedmodels.com/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)

Call chat.completions.create() with your prompt. The response object contains the generated content directly.

Node.js SDK Setup

Initialize the client with the same base URL. Pass your API key as the bearer token. The Node SDK mirrors the Python structure for consistency.

Copyimport OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.obliteratedmodels.com/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);

Invoke the completion endpoint with your desired parameters. This method works well for server-side integrations and web apps.

Streaming Responses (SSE)

Enable streaming by setting stream: true in your request. The API returns Server-Sent Events (SSE) token by token. This reduces perceived latency for long outputs. Token usage details appear in the final chunk.

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)

Parse the SSE stream to display text in real-time. This is ideal for chat interfaces and live dashboards.

Rate Limits and Constraints

You are limited to 300 requests per minute and 8 concurrent connections per key. The maximum request body size is 8 MB. Exceeding these limits triggers a 429 error. Context window support is 100,000 tokens total. If you run out of credit, you receive a 402 error. These limits ensure fair usage for all prepaid users.