Uncensored LLM API: The Alternative to Standard Grok Models

Why Uncensored Matters for Developers

Standard models often refuse lawful adult content or controversial topics based on predefined safety layers. For developers building niche applications, these refusals break workflows. Our grok nsfw API serves an open-weight model tuned to answer without these arbitrary limits. It does not refuse based on topic alone, allowing you to handle content policy in your own layer or accept raw output.

This approach gives you full control. You get the model's raw judgment without hidden filters. The model is not GPT, Claude, or the official Grok from xAI. It is a distinct open-weight model running on our infrastructure, optimized for high capacity and low refusal rates.

OpenAI-Compatible Integration

Our API follows the OpenAI chat-completions standard. You can use the official OpenAI SDKs or any compatible client like LangChain or LlamaIndex. Simply update the base URL to https://api.groknsfw.cc/v1 and provide your API key. The model id is "uncensored".

Copyfrom openai import OpenAI

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

This compatibility means you don't need to rewrite your logic. Streaming, function calling, and JSON mode work exactly as you expect. If you need embeddings or image generation, this API does not provide them. It is strictly text-in, text-out.

High Context Window & Streaming

Process large documents with a 64,000 token context window. This covers both prompt and completion. You can request up to 16,000 output tokens per request, which is significantly higher than many standard free tiers. Streaming is supported via Server-Sent Events (SSE), allowing real-time token delivery.

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)

This is critical for applications requiring low latency. Token usage details are included in the final chunk. The model supports parameters like temperature, top_p, and stop sequences for precise control over generation.

Who This Is NOT For

If you need a dedicated chat interface, look elsewhere. This is a raw API for developers who manage their own UI. If you require image generation, audio, or video, our service does not support those formats. We do not offer SLAs, SOC2 certifications, or dedicated support channels. If you need model routing between multiple vendors, this is a single-model endpoint. We also do not offer a free unlimited tier; you must top up credit to use the service. If you prefer paying with credit cards or PayPal, our crypto-only model may not suit you.