Run Qwen 3.8 27B Uncensored Thinking without a GPU.

Uncensored by Alibaba, on UncensoredLLM as qwen3.8-27b-uncensored-thinking.

Qwen 3.8 27B Uncensored Thinking is an uncensored model from Alibaba. It costs $2.50 per million input tokens and $7.50 per million output tokens, prepaid, with a 1,048,576-token context window. Call it at https://api.uncensoredllm.ai/v1/chat/completions with model "qwen3.8-27b-uncensored-thinking" and an UncensoredLLM key, from any OpenAI SDK or client that takes a base URL.

Chat with Qwen 3.8 27B Uncensored Thinking in the browser at Unrestrictable, no key.

Get a key

Price per million tokens

Input
$2.50
Cached input
$0.55
Output
$7.50
Daily check
Steady since Nov 4
As of
2026-11-09

First request

curl
1curl https://api.uncensoredllm.ai/v1/chat/completions \
2 -H "Authorization: Bearer sk-ullm-live-…" \
3 -H "Content-Type: application/json" \
4 -d '{"model": "qwen3.8-27b-uncensored-thinking", "messages": [{"role": "user", "content": "Hello"}]}'

Base URL https://api.uncensoredllm.ai/v1. The key comes from the console; prices settle per token.

Qwen 3.8 27B Uncensored Thinking settings for SillyTavern and Janitor AI

SillyTavern API
Chat Completion
Source
Custom (OpenAI-compatible)
Custom endpoint
https://api.uncensoredllm.ai/v1
Model
qwen3.8-27b-uncensored-thinking
Context Size
1,048,576 tokens
Janitor AI proxy URL
https://api.uncensoredllm.ai/v1/chat/completions
Key prefix
sk-ullm-

Qwen 3.8 27B Uncensored Thinking specs

Kind
Uncensored
Developer
Alibaba
Context window
1,048,576 tokens
Wire formats
openai

Get a key

  1. Create a key in the console. Credit is prepaid from $10, with no subscription.
  2. Set OPENAI_BASE_URL to https://api.uncensoredllm.ai/v1 and send your sk-ullm- key as the bearer token.
  3. Send qwen3.8-27b-uncensored-thinking as the model. The same key calls every model on UncensoredLLM.

What Qwen 3.8 27B Uncensored Thinking is

Developer
Alibaba
Weights
not published for this build
Context window here
1,048,576 tokens

From the live catalog.

From the SDK

openai.py
1from openai import OpenAI
2
3client = OpenAI(base_url="https://api.uncensoredllm.ai/v1", api_key="sk-ullm-live-…")
4
5completion = client.chat.completions.create(
6 model="qwen3.8-27b-uncensored-thinking",
7 messages=[{"role": "user", "content": "Hello"}],
8)
9print(completion.choices[0].message.content)
openai.ts
1import OpenAI from 'openai';
2
3const client = new OpenAI({ baseURL: 'https://api.uncensoredllm.ai/v1', apiKey: 'sk-ullm-live-…' });
4
5const completion = await client.chat.completions.create({
6 model: 'qwen3.8-27b-uncensored-thinking',
7 messages: [{ role: 'user', content: 'Hello' }],
8});
9console.log(completion.choices[0].message.content);

Guides: SillyTavern, Janitor AI, SDKs.

What Qwen 3.8 27B Uncensored Thinking answers

Qwen 3.8 27B Uncensored Thinking is an uncensored build: it answers without the safety refusals of the stock model.

Every request is routed to the build’s live route and settled to the token at the prices above. The daily real-model check sends a fixed prompt to every model and records whether the answer still matches; this model’s verdict today is “Steady since Nov 4”.

Use it from the OpenAI SDK, from SillyTavern or Janitor, or from a coding agent. Keys and top-ups live in the console.

Also try

What Qwen 3.8 27B Uncensored Thinking costs, worked

A chat turn of 2,000 input and 500 output tokens at $2.50 input and $7.50 output per million tokens is $0.0088; 1,000 turns are $8.75. A long chat session of 100 replies of 300 tokens costs $1.225 at 4,000 tokens of context and $4.225 at 16,000: every message resends the context, so size it to what the chat needs. All from prepaid credit, no subscription.

Uncensored buildsContextIn / out per 1M1,000 chat turns
GLM-5.3 Flash Uncensored—$0.1053 / $0.3685$0.39
GLM-5.3 Uncensored—$0.282 / $0.846$0.99
Dolphin Mistral 24B Venice Edition33K$0.50 / $2.25$2.13
Deepseek V4.1 Flash Uncensored1000K$0.5412 / $2.5882$2.38
Gemma 4 26B-A4B Uncensored33K$0.60 / $2.80$2.60
Amoral Gemma3 27B v233K$1.05 / $1.05$2.63
Gemma 4 31B K1 v5262K$1.071 / $1.071$2.68
DeepSeek V4 Pro Uncensored—$0.97 / $1.94$2.91
DeepSeek V4.1 Flash 0910 Uncensored524K$0.825 / $3.30$3.30
DeepSeek V4.1 Flash 0910 Uncensored Thinking524K$0.825 / $3.30$3.30
DeepSeek V4.1 Flash 1017 Uncensored1049K$0.825 / $3.30$3.30
DeepSeek V4.1 Flash 1017 Uncensored Thinking1049K$0.825 / $3.30$3.30
DeepSeek V4.1 Flash Uncensored Thinking1049K$0.825 / $3.30$3.30
Qwen 3.8 27B Uncensored Fast524K$2.50 / $7.50$8.75
Qwen 3.8 27B Uncensored Thinking1049K$2.50 / $7.50$8.75
Inkling Uncensored262K$2.13 / $12.1403$10.33

Live from the catalog on 2026-11-09, ordered by the cost of 1,000 turns of 2,000 input and 500 output tokens. Every model.

Qwen 3.8 27B Uncensored Thinking in SillyTavern and Janitor AI: what to set

The three fields are the base URL, the key and the model id, as in the settings above. What differs by build is the reply length, the reasoning switch and the context size, and those decide both whether a reply arrives whole and what each message costs.

  • Context Size up to 1,048,576 tokens, the window the catalog lists for qwen3.8-27b-uncensored-thinking. Every message resends the context, so set it to what the chat needs rather than the maximum.
  • No jailbreak preamble is needed and none should be sent: a preamble is resent and billed on every message, and this build answers without one.

A request that fails before the model answers is not billed. A refusal written as ordinary text is billed like any answer, which is the reason to send this build rather than a stock model with a preamble.

What Qwen 3.8 27B Uncensored Thinking costs by client

SessionWhat is sentAt the live price
SillyTavern or Janitor AI, one evening100 replies of 300 tokens on 4,000 tokens of context$1.225
The same evening on a long chat100 replies of 300 tokens on 16,000 tokens of context$4.225
A month of short turns1,000 turns of 2,000 input and 500 output tokens$8.75
One long document, summarised once100,000 input and 1,000 output tokens in one request$0.2575

Input and output tokens at the price above, read from the catalog on 2026-11-09; cached input is not counted. The balance is prepaid and each request settles at these rates.

Qwen 3.8 27B Uncensored Thinking questions

Qwen 3.8 27B Uncensored Thinking is an uncensored build from Alibaba, with a 1,048,576-token context window. On UncensoredLLM it answers as qwen3.8-27b-uncensored-thinking.

No. Credit is prepaid from $10 and billed per token: input $2.50 and output $7.50 per million tokens, cached input $0.55.

About $0.0063 for 1,000 tokens in and 500 tokens out, at $2.50 in and $7.50 out per million tokens.

Up to 1,048,576 tokens, the context window the catalog lists for Qwen 3.8 27B Uncensored Thinking.

Yes. Send qwen3.8-27b-uncensored-thinking as the model through the OpenAI SDK, or any client that takes a custom OpenAI base URL, with base URL https://api.uncensoredllm.ai/v1.

In SillyTavern, set API to Chat Completion, Source to Custom (OpenAI-compatible) and the custom endpoint to https://api.uncensoredllm.ai/v1, paste your key, then pick qwen3.8-27b-uncensored-thinking from the list it loads. In Janitor AI, choose Proxy, set the URL to https://api.uncensoredllm.ai/v1/chat/completions, paste your key and type qwen3.8-27b-uncensored-thinking as the model. See the docs.

Yes. The daily real-model check sends every model the same fixed prompt and records whether the answer still matches. Qwen 3.8 27B Uncensored Thinking’s latest result, from Nov 9: steady since Nov 4. See the daily check.

No base model, parameter count or training notes are published for Qwen 3.8 27B Uncensored Thinking. The catalog lists a 1,048,576-token context window.

$0.875 for 100 chat turns of 2,000 input and 500 output tokens each, at $2.50 input and $7.50 output per million tokens, from prepaid credit with no subscription.

Yes, with the settings above: Chat Completion with a Custom (OpenAI-compatible) source and https://api.uncensoredllm.ai/v1 as the endpoint in SillyTavern; the proxy URL https://api.uncensoredllm.ai/v1/chat/completions and qwen3.8-27b-uncensored-thinking as the model in Janitor AI. See the SillyTavern guide.

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