API Documentation: Quick Start

Base URL and Authentication

Send your requests to our specific base URL. Use your API key in the Authorization header. Invalid keys return 401 errors immediately.

Chat Completions Endpoint

Use the standard chat completions endpoint for text generation. Pass your model ID as uncensored. The system accepts standard parameters for content control.

Here is a basic request to get started:

Copycurl https://api.deepseekapiprice.com/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "uncensored",
    "messages": [{"role": "user", "content": "Check the current rate for deepseek r1 per token."}]
  }'

Available Parameters and Settings

Control the output style using standard parameters. Set temperature for randomness and top_p for nucleus sampling. You can also define stop sequences or enforce JSON mode. These settings allow precise control over the uncensored LLM behavior.

Example Python integration:

Copyfrom openai import OpenAI

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

Node.js

Copyimport OpenAI from "openai";

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

Streaming

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)