Standard OpenAI endpoint
Use the same SDKs and client code with a simple base_url change.
Uncensored LLM API
How to make chatgpt unfiltered with a 100k context window OpenAI-compatible API that charges by token usage without subscription locks.
Test via single request
curl https://api.howtomakechatgptunfiltered.com/v1/chat/completions \
-H "Authorization: Bearer $API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "uncensored",
"messages": [{"role": "user", "content": "calculate total tokens for input array"}]
}'from openai import OpenAI
client = OpenAI(base_url="https://api.howtomakechatgptunfiltered.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)import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.howtomakechatgptunfiltered.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);Use the same SDKs and client code with a simple base_url change.
Process 100,000 tokens of prompt and completion data in a single request.
Enable tools and tool_choice for robust application integration and automation.
Pay only $0.15 per million input tokens and $0.50 per million output tokens.
Prompts are not used for training data with no third-party tracking.
Prepaid credit covers all usage without monthly fees or expiration dates.
Sign up with Google or email to get an instant API key.
Top up via USDT or USDC to access the uncensored model immediately.
Send a POST request to the chat completions endpoint with your key.
Generate unrestricted fiction and roleplay without content filters blocking mature themes.
Query the model for detailed explanations of exploits without standard refusals.
Build conversational agents that respond naturally without excessive politeness or hedging.
Parse large documents using the full 100k context window for accuracy.
Developers reject standard filters because they break deterministic workflows. The "Grandma exploit" workaround proves that refusal is often a stylistic quirk, not a hard constraint. When a model refuses lawful adult content, your application logic fails unpredictably. You cannot rely on standard models for consistent output in creative or research contexts. This API provides an uncensored LLM that answers without unnecessary hedging, ensuring your code receives the raw text you requested.
Switch to an uncensored LLM API to bypass standard refusals and get direct responses. Use streaming for real-time data, enable JSON mode for structured outputs, or leverage tools for complex workflows. Each method integrates via our standard OpenAI-compatible endpoint, ensuring your existing code works immediately without extra setup.
Get your first uncensored response by following these four direct steps.
https://api.howtomakechatgptunfiltered.com/v1 and set the model ID to "uncensored"./v1/chat/completions. The model returns text without standard refusals for lawful adult content.Our uncensored API charges $0.15 per 1M input tokens and $0.50 per 1M output tokens. Errors and refusals cost nothing. A 100K token context window request with 8K output tokens uses $25 in credit. Streaming via SSE reveals exact usage in the final chunk. Prepaid credit never expires, so you pay strictly for what you consume.
A proxy re-routes standard model traffic through a filter layer, adding latency and potential parsing errors. Our native API serves one dedicated uncensored model directly. This eliminates the intermediate hop, ensuring consistent streaming via SSE and strict OpenAI-compatible behavior. You get direct access to the uncensored weights without the overhead of a generic gateway.
| Feature | Typical Proxy | Ours |
|---|---|---|
| Model Source | Standard vendor model | Dedicated uncensored model |
| Streaming | Often delayed | Real-time SSE |
| Cost | Higher per-token | $0.15 input / $0.50 output |
| Setup | Complex config | Single base URL |
Developers often assume standard LLM APIs handle uncensored responses identically to commercial models. They do. You must explicitly set response_format to json_object if parsing is required, as the uncensored model may output conversational filler alongside structured data. Additionally, verify that your client handles streaming SSE events correctly to capture token usage from the final chunk, preventing unexpected billing discrepancies during high-volume inference.
You can make chatgpt unfiltered by sending requests to an OpenAI-compatible endpoint like ours. Our API uses a single uncensored model id and supports standard parameters such as temperature and top_p. This eliminates refusal logic for lawful adult content.
Yes, the API supports JSON mode via the response_format parameter. You can enforce strict object structures for your applications. This ensures consistent parsing without manual validation logic in your client code.
You can send up to 100,000 tokens for the prompt and completion combined. The maximum output length is 16,000 tokens per request. If you do not set max_tokens, the default is 2,048 tokens.
Yes, you can use the official SDK by changing the base_url and API key. Our endpoint is fully OpenAI-compatible. This allows you to switch providers without rewriting your integration logic.
Refusals are free and do not consume your prepaid credit. You only pay for successful token usage. This means you can test boundaries without worrying about unexpected charges for blocked responses.
You can top up using USDT on TRC20 or USDC on Base. Minimum deposits are $20 and maximum is $500. Bonuses apply at $50, $100, and $200 thresholds respectively.
Register, copy the key, update your base URL. Done.