NoFilter AI: The Uncensored LLM API for Code-First Integration

Why Uncensored Matters for Developers

Standard LLMs are fine-tuned to be helpful, harmless, and honest, which often means they refuse lawful adult content, controversial opinions, or creative edge cases. NoFilter AI provides a single, uncensored endpoint optimized for developers who need their models to answer questions without unnecessary moralizing or content filters. This is not a chat interface; it is a raw API for direct integration. You control the context and the output, ensuring your application behaves exactly as you define it without hidden guardrails.

Our model is an open-weight model run on our own servers, tuned specifically to avoid refusals for lawful adult use. It is not GPT, Claude, Gemini, Grok, DeepSeek, Qwen, Llama, or any other vendor's model. When you need a text-in, text-out API that respects your application's logic over a generic safety profile, this is the tool for you.

OpenAI-Compatible Quick Start

Integration is straightforward because we follow the OpenAI chat-completions standard. You only need two endpoints: POST /v1/chat/completions for generation and GET /v1/models to verify availability. There are no embeddings, no image generation, no audio, and no video. Just text. This simplicity means you can drop our SDK into your existing codebase and start generating content immediately.

• Base URL: https://api.nofilterai.cc/v1
• Model ID: "uncensored"
• SDKs: Works with the official OpenAI SDKs and any OpenAI-compatible client.

Copycurl https://api.nofilterai.cc/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "uncensored",
    "messages": [{"role": "user", "content": "Write a blunt product review of a cheap VPN."}]
  }'

The model accepts standard parameters like temperature, top_p, stop, and seed, giving you full control over the output style and randomness.

Streaming, JSON, and Function Calling

We support modern LLM features that are essential for robust application development. Streaming is enabled via Server-Sent Events (SSE), with token usage details provided in the last chunk. This allows your frontend to render text in real-time without waiting for the full response.

For structured data, use the response_format parameter set to json_object to enforce JSON output. Function calling (tools) is fully supported, allowing your LLM to interact with your backend services. You can specify tool choices and define schemas just as you would with other major providers. Note that we do not offer fine-tuning or model routing; this is a single, optimized uncensored model.

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)

Simple Token Pricing

No subscriptions, no monthly fees, and no hidden tiers. You pay only for the tokens you actually use. Errors and refusals are free, so you only spend when you get value. This transparent pricing model ensures you can predict costs accurately based on your token consumption.

• Input Tokens: $0.25 per 1M tokens
• Output Tokens: $1.00 per 1M tokens

Credit is prepaid and never expires. If you make a mistake, such as a double charge, contact Support for a fix; credit is not refunded but remains available for future use. This pay-as-you-go model is ideal for developers who want to scale costs directly with usage without committing to a monthly budget.