API Documentation

Base URL and Authentication

Start by pointing your SDK to our hosted endpoint. Use the base URL https://api.chatgptsenzafiltri.com/v1 and pass your API key in the Authorization header. This keeps your client code identical to standard OpenAI implementations while routing requests to our uncensored model.

Copycurl https://api.chatgptsenzafiltri.com/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "uncensored",
    "messages": [{"role": "user", "content": "What is the capital of France"}]
  }'

Sign up for a key via Google or email. No phone number is required, and the key appears immediately. You only need one active key per account.

First Request

Send a POST request to /v1/chat/completions with the model ID uncensored. This endpoint handles text input and returns unrestricted text output without content refusals for lawful adult use. You can adjust parameters like temperature and top_p to control creativity.

Copyfrom openai import OpenAI

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

The model runs on our own servers. It is not GPT, Claude, or any other vendor model. It simply answers without the typical moral guardrails found in major vendor APIs.

Node SDK Integration

Use the official Node SDK by configuring the base URL. This ensures compatibility with existing tooling and libraries. The API supports standard parameters including stop, seed, presence_penalty, and frequency_penalty.

Copyimport OpenAI from "openai";

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

Errors and refusals are free, so you only pay for real token usage. The prepaid credit never expires, allowing you to top up whenever needed.

Streaming Responses via SSE

Enable streaming by setting the stream parameter to true. You receive Server-Sent Events (SSE) for real-time token delivery. The final chunk includes the total token usage for the request.

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)

This is ideal for chat interfaces where latency matters. The context window supports 100,000 tokens for prompt and completion combined, ensuring long conversations remain intact.

JSON Mode and Function Calling

Use response_format: {"type": "json_object"} for strict JSON output. Function calling (tools) is fully supported, allowing you to integrate the uncensored LLM into automated workflows. Tool choice and tool parameters work exactly as they do in the OpenAI API.

This feature set makes it a viable drop-in replacement for production applications requiring structured data or tool execution without vendor constraints.

Rate Limits and Constraints

You are limited to 300 requests per minute per key and 8 concurrent requests. The request body must not exceed 8 MB. These limits prevent abuse while ensuring stable performance for most developers.

Errors like 401 indicate an invalid key, 402 means no credit, and 429 signals rate limit exhaustion. The context window is 100,000 tokens total. Max output is 16,000 tokens per request, or 2,048 if you do not set max_tokens.