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Model facts & API quick start
Qwen3.6-27B (Qwen/Qwen3.6-27B-TEE) is served on Chutes inside a Trusted Execution Environment (TEE). Call it through the OpenAI-compatible API at https://llm.chutes.ai/v1 with a Chutes API key and "model": "Qwen/Qwen3.6-27B-TEE".
Qwen3.6-27B at a glance
- Model ID
Qwen/Qwen3.6-27B-TEE- Developer
- Qwen
- Deployed by
- chutes
- Type
- LLM
- Input price
- $0.25 per 1M tokens
- Output price
- $2.00 per 1M tokens
- Cached input price
- $0.025 per 1M tokens
- Context length
- 262,144 tokens
- Modalities
- text, image in → text out
- Confidential compute (TEE)
- Yes, runs in a Trusted Execution Environment
- Precision
- FP8 (as served on Chutes)
- Open weights
Qwen/Qwen3.6-27B-FP8- Parameters
- 27B (27.78B safetensors)
- Architecture
- Dense hybrid-attention vision-language model: 64 layers in 16 blocks of 3x (Gated DeltaNet + FFN) followed by 1x (Gated Attention + FFN), with a vision encoder
- License
- Apache-2.0
- Released
- 2026-04
- Context
- 262,144 native, extensible up to 1,010,000 tokens (model card)
- Linear attention
- Gated DeltaNet: 48 V heads, 16 QK heads, head dim 128
- Full attention
- Gated Attention: 24 Q heads, 4 KV heads, head dim 256
- Vocabulary
- 248,320 (padded)
- Thinking
- Thinking mode by default (<think> blocks); instruct mode available; new thinking-preservation option for multi-turn agents
- Serving on Chutes
- TEE, FP8 weights, DFlash speculative decoding
API quick start
- Base URL
https://llm.chutes.ai/v1- model
Qwen/Qwen3.6-27B-TEE- Endpoint
POST /v1/chat/completions- Auth header
Authorization: Bearer $CHUTES_API_KEY
OpenAI-compatible: point any OpenAI SDK at the base URL above and pass your Chutes API key as the API key.
curl -X POST "https://llm.chutes.ai/v1/chat/completions" \
-H "Authorization: Bearer $CHUTES_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen/Qwen3.6-27B-TEE",
"messages": [
{
"role": "user",
"content": "Hello!"
}
],
"stream": true,
"max_tokens": 1024,
"temperature": 0.7
}'Qwen3.6-27B FAQ
How large is the context window?
262,144 tokens natively, per both config.json and the model card. The card states it is extensible up to 1,010,000 tokens with appropriate serving configuration; treat the native 262K as the dependable planning number.
Is Qwen3.6-27B multimodal?
Yes. It is an image-text-to-text model with a vision encoder, and the model card demonstrates both image and video input through OpenAI-compatible messages. Output is text only.
Does it emit thinking tokens?
By default, yes: responses begin with a <think>...</think> block before the final answer. The card documents an instruct (non-thinking) mode for direct responses, and a new thinking-preservation option that carries reasoning context across turns in agent workflows.
What sampling parameters should I use?
Per the model card: temperature 1.0, top_p 0.95, top_k 20, min_p 0 for general thinking-mode tasks; drop temperature to 0.6 for precise coding work such as WebDev; and temperature 0.7, top_p 0.80, top_k 20 with presence_penalty 1.5 for instruct mode.
How do I call it from the OpenAI SDK?
Set base_url to https://llm.chutes.ai/v1, use your Chutes API key, and pass model Qwen/Qwen3.6-27B-TEE. Chat completions with streaming work as with any OpenAI-compatible endpoint.
What does TEE mean for this chute?
The model runs inside a Trusted Execution Environment: attested confidential-compute hardware, so prompts and outputs are processed in protected memory. It is a serving property; the underlying weights are the Qwen3.6-27B FP8 build.
Can I use it commercially?
Yes. The upstream repository is Apache-2.0 licensed, which allows commercial use, modification, and redistribution. Endpoint usage is billed through your Chutes account, separately from the model license.
How good is it at coding, really?
The model card reports 77.2 on SWE-bench Verified, 53.5 on SWE-bench Pro, 71.3 on SWE-bench Multilingual, and 59.3 on Terminal-Bench 2.0. The release explicitly targets agentic coding, frontend workflows, and repository-level reasoning.