Best LLM API/API Documentation for uncensored model
API Documentation for uncensored model
Send your first uncensored request to our OpenAI-compatible API in seconds. This guide covers authentication, SDK usage, streaming, and limits for the uncensored LLM API.
Authentication and Base URL
Start by sending requests to https://api.bestllmapiprovider.com/v1 with your API key. The base URL replaces the standard OpenAI endpoint in your client configuration. Pass the key in the Authorization: Bearer <key> header for every call. This works with the official OpenAI SDKs and any OpenAI-compatible client.
Required Headers
Content-Type: application/jsonAuthorization: Bearer YOUR_API_KEY
Keep your key secure. If you lose it, generate a new one from the dashboard. The old key stops working immediately.
curl https://api.bestllmapiprovider.com/v1/chat/completions \
-H "Authorization: Bearer $API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "uncensored",
"messages": [{"role": "user", "content": "Calculate the total cost for 50k tokens"}]
}'
Quickstart: Send your first uncensored request
Make a POST request to /v1/chat/completions to get text output. The model ID is always uncensored. Send your prompt in the messages array. The API returns a direct response without content filters for lawful adult use.
Example Payload
model:"uncensored"messages:[{"role": "user", "content": "Explain quantum entanglement."}]
This endpoint handles text generation only. It does not support images or audio. Use the parameters below to control the output style.
from openai import OpenAI
client = OpenAI(base_url="https://api.bestllmapiprovider.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)
Parameters: Temperature, Top-P, and Seed
Control creativity with temperature and top_p. Set temperature to 0.7 for balanced output or 1.5 for more variation. Use top_p to limit the probability mass of sampled tokens. Set a seed integer for reproducible results. These parameters help you tune the model for specific tasks.
Key Parameters
temperature: 0.0 to 2.0top_p: 0.1 to 1.0seed: any integer
Combine these with max_tokens to control length. The model respects the context window limits strictly.
import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.bestllmapiprovider.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 responses and token counting
Enable streaming by setting stream: true in your request. The API sends Server-Sent Events (SSE) for each generated token. This reduces perceived latency for long responses. Token usage is reported in the final chunk.
Stream Handling
- Read chunks as they arrive
- Parse the
deltafield - Check
usagein the last chunk
Streaming works with all parameters. It does not affect the per-token pricing. You pay for actual tokens generated.
stream = 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)
Function Calling and JSON Mode
Use tools for function calling or response_format for JSON output. Set response_format to {"type": "json_object"} for strict JSON. The model returns structured data suitable for parsing. This is useful for building agents or data extraction pipelines.
Configuration
tools: array of function definitionsresponse_format:{"type": "json_object"}
Ensure your prompt instructions match the schema. The model may still require validation. Always handle potential parsing errors.
JSON Mode Example
Set response_format to enforce strict JSON output. This reduces the need for post-processing.
Limits, Errors, and Context Window
The context window is 100,000 tokens total. Max output is 16,000 tokens per request. You can send 300 requests per minute per key. Eight concurrent requests are allowed simultaneously. Errors return standard HTTP codes.
Common Errors
- 401: Invalid or missing API key
- 402: Insufficient credit
- 429: Rate limit exceeded
Refusals due to content limits do not charge credit. Errors do not charge credit either. Your prepaid credit never expires.
Request Limits
Max request body size is 8 MB. Exceeding this returns a 413 error. Manage your token usage carefully.
Frequently asked questions
How do I pay for the uncensored LLM API?
You top up with crypto only (USDT on TRC20 or USDC on Base). Any whole amount from $20 to $500 is accepted. Credit is charged by real token usage at $0.15 per 1M input tokens and $0.50 per 1M output tokens.
Is the uncensored model used for training?
No, your prompts are not used for training. The model is an open-weight model run on our own servers. It is tuned to answer without content refusals for lawful adult use.
What happens if I get a 402 error?
A 402 error means you have no credit left. You need to top up your account with crypto to continue using the API. Errors like 401 or 429 do not charge credit.
Get your API key
Register, retrieve your key, update the endpoint. That is all.