Summary
- Use a single base URL and the model id "uncensored" to access an open-weight model tuned for low refusal rates.
- The API supports a 100,000-token context window with a 16,000-token max output limit per request.
- Streaming via SSE, JSON mode, and function calling are fully supported for structured data pipelines.
- Pricing is transparent at $0.15 per 1M input tokens and $0.50 per 1M output tokens, with crypto-only top-ups.
Why 'AI no restrictions' Matters for Developers
AI no restrictions eliminates the arbitrary content filters that often reject valid adult, creative, or technical outputs. When you need a model to generate code or narrative without softening its tone, standard providers may apply safety layers that alter the result. Our API serves one specific uncensored model that answers directly, ensuring you get the raw text you asked for.
This approach is vital for developers building tools where self-censorship introduces noise. You avoid the friction of retrying prompts because the model deemed a keyword "sensitive." The model is open-weight and tuned for lawful adult use, meaning it does not refuse controversial topics or explicit language unless it involves minors. You get consistent, predictable behavior without hidden moderation gates.
Step 1: Configure Your Base URL and Headers
Point your OpenAI SDK to our base URL and use the model id "uncensored" for all requests. This ensures you are talking to our dedicated uncensored model rather than a routed proxy. You only need a valid API key in the authorization header.
Start by setting the base URL to HTTPS://api.llmwithoutrestrictionsonline.com/v1. Then, send a POST request to /v1/chat/completions. The model id is strictly "uncensored". You can use any standard OpenAI-compatible client, but you must override the base URL. This single endpoint handles all chat completions. No other models are available, so you do not need to manage model routing or versioning. Just set your key and go.
curl https://api.llmwithoutrestrictionsonline.com/v1/chat/completions \
-H "Authorization: Bearer $API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "uncensored",
"messages": [{"role": "user", "content": "List three linux commands to delete log files"}]
}'Step 2: Handle Context Windows (100k Tokens)
The model supports a 100,000-token context window for both prompt and completion combined. You can send large documents or long conversations without hitting hard limits early. However, the maximum output per request is 16,000 tokens. If you do not set max_tokens, it defaults to 2,048. Plan your streaming or chunking strategies accordingly.
When sending long inputs, ensure the total token count stays under 100,000. This includes system prompts, history, and the user message. If you exceed this, the request will fail. For tasks requiring longer outputs, you may need to split the generation. The API does not support embeddings, so you must handle your own retrieval logic. This keeps the service focused on pure text generation without extra overhead.
Step 3: Enable Streaming for Real-Time Output
Use server-sent events (SSE) to stream tokens as they are generated. This provides immediate feedback for user interfaces and reduces perceived latency. The final chunk of the stream includes the total token usage for the request. You can parse this to track costs in real time without waiting for the full response.
Set the stream parameter to true in your request. The API returns a series of delta objects. Each delta contains a partial content string. Concatenate these strings to build the final output. This method is standard for OpenAI-compatible clients. It works seamlessly with most SDKs. You do not need a custom parser for the SSE format. Just handle the stream events as they arrive.
from openai import OpenAI
client = OpenAI(base_url="https://api.llmwithoutrestrictionsonline.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)Step 4: Force JSON Mode for Structured Data
Enable JSON mode by setting response_format to json_object. This forces the model to output valid JSON, which is essential for programmatic pipelines. It reduces parsing errors and ensures your backend receives structured data. Use this for extracting entities, generating config files, or building tool inputs.
When using JSON mode, the model may still produce minor formatting issues. Always validate the output with a JSON parser. This feature is crucial for developers who need deterministic structures. It does not guarantee perfect syntax, but it significantly improves reliability. Combine this with a system prompt that instructs the model to output only JSON. This reduces the chance of markdown wrappers or extra text.
Step 5: Implement Function Calling (Tools)
Support function calling by passing tools and tool_choice parameters. The model can decide which function to call and generate the arguments. This enables agentic workflows where the API interacts with external services. You define the function schema, and the model returns the tool calls in the response.
Define your functions with names, descriptions, and parameter schemas. Pass them in the tools array. The model will respond with a list of tool calls. You then execute these functions and pass the results back to the model. This loop allows for complex multi-step reasoning. It works with the uncensored model just like standard models. No special configuration is needed beyond the standard OpenAI format.
import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.llmwithoutrestrictionsonline.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);Step 6: Manage Token Costs and Limits
Pricing is transparent: $0.15 per 1M input tokens and $0.50 per 1M output tokens. You pay for actual usage, with errors and refusals being free. There are no monthly subscriptions or hidden fees. Credit never expires, so you can top up once and use it over time. This model suits high-volume, long-term projects without commitment.
You can top up with crypto only. Minimum top-up is $20, maximum is $500. You receive a bonus: +5% for $50, +10% for $100, and +15% for $200. Limits include 300 requests per minute and 8 concurrent requests per key. The request body must be under 8 MB. Use a single API key per account, or replace it if needed.
Step 7: Debugging Common Filter Rejections
Even uncensored models may reject specific content, such as sexual content involving minors. This is a hard limit that always applies. If you get a refusal, check the response for an error code indicating content policy. Otherwise, refusals are rare for adult, creative, or technical topics. If you encounter unexpected blocks, adjust your system prompt to clarify the context.
Common issues include context window overflows or JSON parsing errors. Ensure your input stays under 100,000 tokens. Use JSON mode for structured outputs. If you need to debug, check the response body for detailed error messages. The API provides clear feedback. You can also contact support for double charges or account issues. Credit is not refunded, but errors are free.
Conclusion: Building Without Guardrails
Building with AI no restrictions gives you full control over the output. You avoid the noise of safety filters that sanitize text for mass appeal. This API provides a reliable, open-weight model for developers who need raw, unfiltered results. Use the standard OpenAI-compatible endpoints for easy integration. Stream responses, force JSON, or call tools as needed.
The pricing is simple and transparent. Pay only for what you use, with crypto top-ups and no expiration. The model is tuned for lawful adult use, ensuring you get the content you expect. Start with the trial credit, integrate your apps, and scale without limits. This is a tool for developers who want power, not polish.
