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Qwen/Qwen3.8-27B
Input Modalities
Output Modalities
Supported Functionality
Qwen3.8-27B features the following enhancements:
reasoning_effort, and reasoning context from historical messages is retained via preserve_thinking.For streamlined integration, we recommend using Qwen3.8 via APIs.
[!Important] Inference efficiency and throughput vary significantly across frameworks. We recommend using the latest framework versions to ensure optimal performance and compatibility. For production workloads or high-throughput scenarios, dedicated serving engines such as SGLang, vLLM, or TokenSpeed are recommended.
Qwen3.8 can be deployed with popular inference frameworks, e.g.:
[!Important] Qwen3.8 models operate in thinking mode by default, generating thinking content signified by
<think>\n...</think>\n\nbefore producing the final response. To disable thinking content and obtain a direct response, refer to the examples here.
[!Tip] We recommend using the following sets of sampling parameters for generation:
- Thinking Mode:
temperature=1.0,top_p=0.95,top_k=20,min_p=0.0,presence_penalty=0.0,repetition_penalty=1.0- Instruct (or non-thinking) mode:
temperature=0.7,top_p=0.80,top_k=20,min_p=0.0,presence_penalty=1.5,repetition_penalty=1.0Please note that the support for sampling parameters varies according to inference frameworks.
Qwen3.8 comes with official support for reasoning_effort, which can be used to adjust reasoning depth and control cost:
xhigh (default): for complex tasks demanding thorough analysismedium: balancing accuracy and speedlow: efficient reasoning optimizing for speed and costIn addition, preserve_thinking is enabled by default for all workloads for the best out-of-the-box experience. To disable preserved thinking, refer to the examples here.
[!Tip] In multi-turn agentic tasks, lower reasoning effort does not always reduce overall task completion time. Although it may produce faster per-turn responses, it can also lead to insufficient analysis, more failures, and repeated retries, which may increase total latency and token consumption.
The Chat Completions API can be used with most inference frameworks, as well as Qwen Cloud. Before starting, make sure the OpenAI Python SDK is installed and the API key and the API base URL are configured, e.g.:
shell
pip install -U openai# Set the following accordinglyexport OPENAI_BASE_URL='your-base-url'export OPENAI_API_KEY='your-api-key'
python
from openai import OpenAI# Configured by environment variablesclient = OpenAI()messages = [{"role": "user", "content": "Write a Python function to merge two sorted linked lists."}]completion = client.chat.completions.create(model="Qwen/Qwen3.8-27B",messages=messages,extra_body={"chat_template_kwargs": {"enable_thinking": True, # on by default"preserve_thinking": True, # on by default},},reasoning_effort="xhigh", # xhigh by default; supported levels are xhigh, medium, and lowstream=True,stream_options={"include_usage": True},)reasoning_content = ""answer_content = ""is_answering = Falseprint("\n" + "=" * 20 + "Reasoning" + "=" * 20 + "\n")for chunk in completion:if not chunk.choices:print("\nUsage:")print(chunk.usage)continuedelta = chunk.choices[0].deltaif hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:if not is_answering:print(delta.reasoning_content, end="", flush=True)reasoning_content += delta.reasoning_contentelif hasattr(delta, "reasoning") and delta.reasoning is not None:if not is_answering:print(delta.reasoning, end="", flush=True)reasoning_content += delta.reasoningif hasattr(delta, "content") and delta.content:if not is_answering:print("\n" + "=" * 20 + "Answer" + "=" * 20 + "\n")is_answering = Trueprint(delta.content, end="", flush=True)answer_content += delta.contentmessages.append({"role": "assistant","content": answer_content,"reasoning_content": reasoning_content,"reasoning": reasoning_content,})
python
from openai import OpenAI# Configured by environment variablesclient = OpenAI()messages = [{"role": "user","content": [{"type": "image_url","image_url": {"url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/CI_Demo/mathv-1327.jpg"}},{"type": "text","text": "The centres of the four illustrated circles are in the corners of the square. The two big circles touch each other and also the two little circles. With which factor do you have to multiply the radii of the little circles to obtain the radius of the big circles?\nChoices:\n(A) $\\frac{2}{9}$\n(B) $\\sqrt{5}$\n(C) $0.8 \\cdot \\pi$\n(D) 2.5\n(E) $1+\\sqrt{2}$"}]}]chat_response = client.chat.completions.create(model="Qwen/Qwen3.8-27B",messages=messages,)print("Chat response:", chat_response)
python
from openai import OpenAI# Configured by environment variablesclient = OpenAI()messages = [{"role": "user","content": [{"type": "video_url","video_url": {"url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/video/N1cdUjctpG8.mp4"}},{"type": "text","text": "How many porcelain jars were discovered in the niches located in the primary chamber of the tomb?"}]}]chat_response = client.chat.completions.create(model="Qwen/Qwen3.8-27B",messages=messages,)# When vLLM is launched with `--media-io-kwargs '{"video": {"num_frames": -1}}'`,# video frame sampling can be configured via `extra_body` (e.g., by setting `fps`).# This feature is currently supported only in vLLM.## By default, `fps=2` and `do_sample_frames=True`.# With `do_sample_frames=True`, you can customize the `fps` value to set your desired video sampling rate.# chat_response = client.chat.completions.create(# model="Qwen/Qwen3.8-27B",# messages=messages,# extra_body={# "mm_processor_kwargs": {"fps": 2, "do_sample_frames": True},# },# )print("Chat response:", chat_response)
Qwen3.8-27B will think by default before responding. You can obtain a direct response from the model without thinking by configuring the API parameters. For example,
python
from openai import OpenAI# Configured by environment variablesclient = OpenAI()messages = [{"role": "user","content": [{"type": "image_url","image_url": {"url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/RealWorld/RealWorld-04.png"}},{"type": "text","text": "Where is this?"}]}]chat_response = client.chat.completions.create(model="Qwen/Qwen3.8-27B",messages=messages,temperature=0.7,top_p=0.8,presence_penalty=1.5,extra_body={"top_k": 20,"chat_template_kwargs": {"enable_thinking": False},},)print("Chat response:", chat_response)
[!Note] If you are using APIs from Qwen Cloud, in addition to changing
model, please use"enable_thinking": Falseinstead of"chat_template_kwargs": {"enable_thinking": False}.
By default, Qwen3.8 retains thinking blocks from all historical messages, maintaining a complete reasoning trace across the conversation. This behavior, known as preserved thinking, ensures full context continuity and is especially beneficial for agent scenarios where decision consistency and reduced redundant reasoning are critical. It also improves KV cache utilization, optimizing inference efficiency in both thinking and non-thinking modes.
If you prefer to retain only the thinking blocks from the latest user message, you can disable this behavior by setting preserve_thinking to False:
python
from openai import OpenAI# Configured by environment variablesclient = OpenAI()messages = [...]chat_response = client.chat.completions.create(model="Qwen/Qwen3.8-27B",messages=messages,extra_body={"chat_template_kwargs": {"preserve_thinking": False},},)print("Chat response:", chat_response)
[!Note] If you are using APIs from Qwen Cloud, in addition to changing
model, please use"preserve_thinking": Falsedirectly instead of wrapping it inchat_template_kwargs.
To achieve optimal performance, we recommend the following settings:
Sampling Parameters: We suggest using the following sets of sampling parameters:
temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0temperature=0.7, top_p=0.80, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0For supported frameworks, you can adjust the parameter between 0 and 2 to reduce endless repetition. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
If you find our work helpful, feel free to give us a cite.
bibtex
@misc{qwen38,title = {{Qwen3.8-Max}: A New Bar for Coding and Cowork},url = {https://qwen.ai/blog?id=qwen3.8},author = {{Qwen Team}},month = {August},year = {2026}}
This is a domain-calibrated quantization of Qwen/Qwen3.8-27B produced with llm-compressor GPTQ:
GPTQModifier(scheme="W8A16") — 400 quantized Linear GEMMsk_scale/v_scale tensors baked onto the 16 full-attention layers, loaded automatically with --kv-cache-dtype fp8. This is the delta from the existing data-free RTN W8A16 checkpoints, whose kv_cache_scheme is null (dynamic per-token scaling at serve time)lm_head, the full vision tower (333 tensors), the MTP head (model_mtp.safetensors, grafted unquantized), and the two tiny GDN recurrent-gate projections linear_attn.in_proj_a/in_proj_b per the llm-compressor Qwen3.5/3.6 guidance (issue #3040: ignore the gates only, not the whole module; , , are quantized)Dual RTX 3090 Ti (Ampere sm_86), vLLM TP2, BF16 activations, MTP speculative decoding (num_speculative_tokens: 2):
CompressedTensorsWNA16 → MarlinLinearKernelQuantized on a 128 GB RAM host via sequential onloading (the 52 GB BF16 source plus GPTQ calibration does not fit smaller hosts). The full recipe is in recipe.yaml.
The upstream model card above describes the base model.
presence_penaltyAdequate Output Length: To optimize performance on agentic tasks, we recommend allocating sufficient output length to allow the model to generate detailed and comprehensive responses. For frameworks that support separate token limits for internal reasoning and final outputs, we suggest the following configuration within the 1M context length:
These settings provide the necessary capacity for complex reasoning while ensuring ample space for high-quality final deliverables.
Processing Ultra-Long Texts: Qwen3.8-27B natively supports context lengths of up to 262,144 tokens. For long-horizon tasks where the total length (including both input and output) exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively, e.g., YaRN.
YaRN is currently supported by several inference frameworks, e.g., vLLM, SGLang, and TokenSpeed. In general, there are two approaches to enabling YaRN for supported frameworks:
Modifying the model configuration file:
In the config.json file, change the rope_parameters fields in text_config to:
json
{"mrope_interleaved": true,"mrope_section": [11,11,10],"rope_type": "yarn","rope_theta": 10000000,"partial_rotary_factor": 0.25,"factor": 4.0,"original_max_position_embeddings": 262144,}
Passing command line arguments:
For vLLM, you can use
shell
VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 vllm serve ... --hf-overrides '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}' --max-model-len 1000000
For SGLang, you can use
shell
SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 python -m sglang.launch_server ... --json-model-override-args '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}' --context-length 1000000
For TokenSpeed, you can use
shell
TOKENSPEED_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 tokenspeed serve ... --hf-overrides '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}' --max-model-len 1000000
[!NOTE] All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. We advise modifying the
rope_parametersconfiguration only when processing long contexts is required. It is also recommended to modify thefactoras needed. For example, if the typical context length for your application is 524,288 tokens, it would be better to setfactoras 2.0.
Long Video Understanding: To optimize inference efficiency for plain text and images, the size parameter in the released video_preprocessor_config.json is conservatively configured. It is recommended to set the longest_edge parameter in the video_preprocessor_config file to 469,762,048 (corresponding to 224k video tokens) to enable higher frame-rate sampling for hour-scale videos and thereby achieve superior performance. For example,
json
{"longest_edge": 469762048, "shortest_edge": 4096}
Alternatively, override the default values via engine startup parameters. For implementation details, refer to: vLLM / SGLang.
linear_attnin_proj_qkvin_proj_zout_projpack-quantized, auto-detected by vLLM from config.json — no --quantization flag; serve with --kv-cache-dtype fp8