Qwen 3.8 27B Uncensored

Run long-form chat, code analysis, and document work without managing GPUs. imageat exposes the 27B model through one OpenAI-compatible endpoint, with optional thinking mode and usage-based credits.

Create an API key View quickstart

262K

context

27B

parameters

1–3 credits

per request

OpenAI

compatible

Live playground

Try Qwen in your browser

Run a real completion using your imageat account. We reserve 3 credits, then charge 1–3 credits from the completed run's actual processing cost and automatically return the rest.

imageat playground—Qwen 3.8 27B Uncensored

Thinking

Prompt

Advanced settings

TemperatureMax tokens

Loading…

Final cost is based on actual runtime.

Output

Your response will appear here

Enter a prompt and run the model.

Choose the depth

Turn thinking on for difficult code and planning, or off when latency and concise answers matter more.

Prototype before integrating

Tune the request in the live imageat playground, then move the same settings into your backend.

Use your existing stack

Send role-based messages to an OpenAI-compatible endpoint with the imageat key and credits you already use.

QUICKSTART

Your first completion

1Create an iat_live_ key in Projects.
2Keep the key in server-side environment variables.
3Send messages to the chat completions endpoint.

cURL

curl https://api.imageat.com/v1/chat/completions \
  -H "Authorization: Bearer iat_live_YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen/qwen3.8-27b-uncensored",
    "messages": [
      {"role": "system", "content": "You are a precise coding assistant."},
      {"role": "user", "content": "Explain this function and improve it."}
    ],
    "enable_thinking": true,
    "temperature": 0.7,
    "max_tokens": 1000
  }'

Supported controls

enable_thinking

temperature

top_p

top_k

max_tokens

min_tokens

repetition_penalty

length_penalty

stop

seed

quantization

do_sample

user

session_id

Safety is still your responsibility

“Uncensored” means refusal behavior is reduced, not that every output is safe or correct. Add moderation, logging, abuse controls, and legal review before exposing it to end users.

Usage-based pricing

Qwen API pricing in imageat credits

Three credits are temporarily reserved when a request starts. After completion, imageat charges only the tier reached by the provider's actual processing cost and returns the unused reservation automatically.

Final charge	Provider cost	Typical fit
1 credit	Up to $0.025	Short, direct responses
2 credits	$0.025–$0.050	Medium completions and light reasoning
3 credits	Above $0.050	Long or thinking-heavy requests

Examples describe common patterns, not fixed execution times. The final charge is calculated from the completed request.

Model guide

Qwen 3.8 27B Uncensored overview

This imageat deployment packages Qwen 3.8 27B Uncensored as a hosted chat API: no model download, inference server, or GPU capacity planning is required. Applications send familiar system, user, and assistant messages, while imageat handles execution, credit settlement, and the final OpenAI-style response.

The model is useful when a request needs more room than a typical short-context assistant—large specifications, multi-file code excerpts, research notes, or extended conversations. Thinking mode is a per-request choice, so one integration can support both quick chat and more deliberate analysis.

What you can build

Add long-context assistance to an imageat workflow without hosting a separate LLM service
Review large product specifications, logs, or code excerpts submitted by your backend
Generate structured creative briefs before sending work into image and video pipelines
Build support agents that preserve role-based conversation context across several turns
Turn research notes into implementation plans, checklists, documentation, or reports
Prototype prompts in the playground and reuse the selected parameters through the API

Outputs

The model returns a single assistant response as UTF-8 text in an OpenAI-compatible chat completion response. When thinking mode is enabled, the response may include a dedicated reasoning block before the final answer.

choices[0].message.content

Read the generated assistant text from this field.

Model inputs

Control the response style, length, repeatability, and conversation identity through the imageat Chat Completions API.

messages

Your main instructions, questions, and conversation history as role-based text messages.

system message

An optional message that sets the assistant’s role, tone, and behavioral rules.

enable_thinking

Enables deeper thinking-style output or disables it for shorter, direct responses.

user & session_id

Optional identifiers used to keep separate users and conversation sessions.

temperature, top_p & top_k

Randomness and probability-sampling controls for generation.

repetition_penalty

Reduces repeated phrases and generation loops.

length_penalty

Nudges the model toward shorter or longer completions.

min_tokens & max_tokens

Sets optional minimum and maximum output-token limits.

stop

A string or array of phrases that stops generation at the first match.

seed

An optional integer seed for more repeatable sampled outputs.

quantization

Trades a small amount of quality for more efficient inference when enabled.

do_sample

Switches between randomized sampling and more deterministic generation.

Recommended settings

Use these presets as a starting point based on the official Qwen 3.8 sampling guidance.

Reasoning and coding

Enable thinking, then use temperature: 1.0, top_p: 0.95, top_k: 20, and repetition_penalty: 1.0.

Direct answers and chat

Disable thinking, then use temperature: 0.7, top_p: 0.80, top_k: 20, and repetition_penalty: 1.0.

Limitations

Reduced refusal behavior is not a guarantee that every prompt will be answered or that every answer is appropriate.
Messages and generated output share the 262,144-token context budget; reserve enough space for the completion.
Large context capacity does not guarantee perfect retrieval—important instructions should be explicit and well structured.
Thinking mode can materially increase runtime, output length, and the final imageat credit tier.
Sampling and quantization settings can change factual consistency, formatting, and reproducibility.
The imageat endpoint documented here accepts text messages; it does not expose the checkpoint's possible vision inputs.

Safety and compliance

This model may be more willing to answer sensitive requests than strongly aligned chat models. You must still follow imageat platform rules and all applicable laws. Do not use it for instructions that enable wrongdoing, violence, self-harm, or illegal access.

For user-facing products, add your own moderation, logging, rate limits, abuse prevention, and human review for high-risk use cases.

Ready to build with Qwen?

Your existing imageat credits and API keys work here.

Open API keys

curl https://api.imageat.com/v1/chat/completions \ -H "Authorization: Bearer iat_live_YOUR_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "qwen/qwen3.8-27b-uncensored", "messages": [ {"role": "system", "content": "You are a precise coding assistant."}, {"role": "user", "content": "Explain this function and improve it."} ], "enable_thinking": true, "temperature": 0.7, "max_tokens": 1000 }'

Final charge

Provider cost

1 credit

Up to $0.025

2 credits

$0.025–$0.050

3 credits

Above $0.050

Qwen 3.8 27B Uncensored overview

What you can build

Add long-context assistance to an imageat workflow without hosting a separate LLM service

Review large product specifications, logs, or code excerpts submitted by your backend

Generate structured creative briefs before sending work into image and video pipelines

Build support agents that preserve role-based conversation context across several turns

Turn research notes into implementation plans, checklists, documentation, or reports

Prototype prompts in the playground and reuse the selected parameters through the API

Model inputs

Control the response style, length, repeatability, and conversation identity through the imageat Chat Completions API.

messages

Your main instructions, questions, and conversation history as role-based text messages.

system message

An optional message that sets the assistant’s role, tone, and behavioral rules.

enable_thinking

Enables deeper thinking-style output or disables it for shorter, direct responses.

user & session_id

Optional identifiers used to keep separate users and conversation sessions.

temperature, top_p & top_k

Randomness and probability-sampling controls for generation.

repetition_penalty

Reduces repeated phrases and generation loops.

length_penalty

Nudges the model toward shorter or longer completions.

min_tokens & max_tokens

Sets optional minimum and maximum output-token limits.

stop

A string or array of phrases that stops generation at the first match.

seed

An optional integer seed for more repeatable sampled outputs.

quantization

Trades a small amount of quality for more efficient inference when enabled.

do_sample

Switches between randomized sampling and more deterministic generation.

Recommended settings

Use these presets as a starting point based on the official Qwen 3.8 sampling guidance.

Reasoning and coding

Enable thinking, then use temperature: 1.0, top_p: 0.95, top_k: 20, and repetition_penalty: 1.0.

Direct answers and chat

Disable thinking, then use temperature: 0.7, top_p: 0.80, top_k: 20, and repetition_penalty: 1.0.

Limitations

Reduced refusal behavior is not a guarantee that every prompt will be answered or that every answer is appropriate.

Messages and generated output share the 262,144-token context budget; reserve enough space for the completion.

Large context capacity does not guarantee perfect retrieval—important instructions should be explicit and well structured.

Thinking mode can materially increase runtime, output length, and the final imageat credit tier.

Sampling and quantization settings can change factual consistency, formatting, and reproducibility.

The imageat endpoint documented here accepts text messages; it does not expose the checkpoint's possible vision inputs.

Safety and compliance

For user-facing products, add your own moderation, logging, rate limits, abuse prevention, and human review for high-risk use cases.