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Qwen/Qwen3.5-0.8B-Base
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Qwen3.5 features the following enhancement:
Unified Vision-Language Foundation: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.
Efficient Hybrid Architecture: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead.
Scalable RL Generalization: Reinforcement learning scaled across million-agent environments with progressively complex task distributions for robust real-world adaptability.
Global Linguistic Coverage: Expanded support to 201 languages and dialects, enabling inclusive, worldwide deployment with nuanced cultural and regional understanding.
Next-Generation Training Infrastructure: Near-100% multimodal training efficiency compared to text-only training and asynchronous RL frameworks supporting massive-scale agent scaffolds and environment orchestration.
For more details, please refer to our blog post Qwen3.5.
[!Important] Qwen3.5 models support both non-thinking and thinking mode. Qwen3.5-0.8B operates in non-thinking mode by default. To enable thinking, refer to the examples here.
For streamlined integration, we recommend using Qwen3.5 via APIs. Below is a guide to use Qwen3.5 via OpenAI-compatible API.
Qwen3.5 can be served via APIs with popular inference frameworks. In the following, we show example commands to launch OpenAI-Compatible API servers for Qwen3.5 models.
[!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, KTransformers or vLLM are strongly recommended.
[!Important] The model has a default context length of 262,144 tokens. If you encounter out-of-memory (OOM) errors, consider reducing the context window.
SGLang is a fast serving framework for large language models and vision language models. SGLang from the main branch of the open-source repository is required for Qwen3.5, which can be installed using the following command in a fresh environment:
shell
uv pip install 'git+https://github.com/sgl-project/sglang.git#subdirectory=python&egg=sglang[all]'See its documentation for more details.
The following will create API endpoints at http://localhost:8000/v1:
Standard Version: The following command can be used to create an API endpoint with maximum context length 262,144 tokens using tensor parallel on 8 GPUs.
shell
python -m sglang.launch_server --model-path Qwen/Qwen3.5-0.8B --port 8000 --tp-size 1 --mem-fraction-static 0.8 --context-length 262144Tool Use: To support tool use, you can use the following command.
shell
python -m sglang.launch_server --model-path Qwen/Qwen3.5-0.8B --port 8000 --tp-size 1 --mem-fraction-static 0.8 --context-length 262144 --tool-call-parser qwen3_coderMulti-Token Prediction (MTP): The following command is recommended for MTP:
shell
python -m sglang.launch_server --model-path Qwen/Qwen3.5-0.8B --port 8000 --tp-size 1 --mem-fraction-static 0.8 --context-length 262144 --speculative-algo NEXTN --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. vLLM from the main branch of the open-source repository is required for Qwen3.5, which can be installed using the following command in a fresh environment:
shell
uv pip install vllm --torch-backend=auto --extra-index-url https://wheels.vllm.ai/nightlySee its documentation for more details.
For detailed Qwen3.5 usage guide, see the vLLM Qwen3.5 recipe.
The following will create API endpoints at http://localhost:8000/v1:
Standard Version: The following command can be used to create an API endpoint with maximum context length 262,144 tokens using tensor parallel on 8 GPUs.
shell
vllm serve Qwen/Qwen3.5-0.8B --port 8000 --tensor-parallel-size 1 --max-model-len 262144 Tool Call: To support tool use, you can use the following command.
shell
vllm serve Qwen/Qwen3.5-0.8B --port 8000 --tensor-parallel-size 1 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder Multi-Token Prediction (MTP): The following command is recommended for MTP:
shell
vllm serve Qwen/Qwen3.5-0.8B --port 8000 --tensor-parallel-size 1 --max-model-len 262144 --speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}'Text-Only: The following command skips the vision encoder and multimodal profiling to free up memory for additional KV cache:
shell
vllm serve Qwen/Qwen3.5-0.8B --port 8000 --tensor-parallel-size 1 --max-model-len 262144 --language-model-onlyKTransformers is a flexible framework for experiencing cutting-edge LLM inference optimizations with CPU-GPU heterogeneous computing. For running Qwen3.5 with KTransformers, see the KTransformers Deployment Guide.
Hugging Face Transformers contains a lightweight server which can be used for quick testing and moderate load deployment.
The latest transformers is required for Qwen3.5:
shell
pip install "transformers[serving] @ git+https://github.com/huggingface/transformers.git@main"See its documentation for more details. Please also make sure torchvision and pillow are installed.
Then, run transformers serve to launch a server with API endpoints at http://localhost:8000/v1; it will place the model on accelerators if available:
shell
transformers serve --force-model Qwen/Qwen3.5-0.8B --port 8000 --continuous-batchingThe chat completions API is accessible via standard HTTP requests or OpenAI SDKs. Here, we show examples using the OpenAI Python SDK.
Before starting, make sure it is installed and the API key and the API base URL is configured, e.g.:
shell
pip install -U openai # Set the following accordinglyexport OPENAI_BASE_URL="http://localhost:8000/v1"export OPENAI_API_KEY="EMPTY"[!Tip] We recommend using the following set of sampling parameters for generation
- Non-thinking mode for text tasks:
temperature=1.0, top_p=1.00, top_k=20, min_p=0.0, presence_penalty=2.0, repetition_penalty=1.0- Non-thinking mode for VL tasks:
temperature=0.7, top_p=0.80, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0- Thinking mode for text tasks:
temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0- Thinking mode for VL or precise coding (e.g. WebDev) tasks :
temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0Please note that the support for sampling parameters varies according to inference frameworks.
python
from openai import OpenAI# Configured by environment variablesclient = OpenAI() messages = [ {"role": "user", "content": "Give me a short introduction to large language models."},] chat_response = client.chat.completions.create( model="Qwen/Qwen3.5-0.8B", messages=messages, max_tokens=32768, temperature=1.0, top_p=1.0, presence_penalty=2.0, extra_body={ "top_k": 20, }, )print("Chat response:", chat_response)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/Qwen3.5-0.8B", messages=messages, max_tokens=32768, temperature=0.7, top_p=0.8, presence_penalty=1.5, extra_body={ "top_k": 20, }, )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": "Summarize the video content." } ] }] # 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/Qwen3.5-0.8B", messages=messages, max_tokens=32768, temperature=0.7, top_p=0.8, presence_penalty=1.5, extra_body={ "top_k": 20, "mm_processor_kwargs": {"fps": 2, "do_sample_frames": True}, }, ) print("Chat response:", chat_response)[!Important] Qwen3.5 does not officially support the soft switch of Qwen3, i.e.,
/thinkand/nothink.
You can make the model think before response by configuring the API parameters. For example,
python
from openai import OpenAI# Configured by environment variablesclient = OpenAI() messages = [ {"role": "user", "content": "Type \"I love Qwen3.5\" backwards"},] chat_response = client.chat.completions.create( model="Qwen/Qwen3.5-0.8B", messages=messages, max_tokens=81920, temperature=1.0, top_p=0.95, presence_penalty=1.5, extra_body={ "top_k": 20, "enable_thinking": True, }, )print("Chat response:", chat_response)[!Important] In thinking mode, we have observed that when using the recommended sampling parameters, Qwen3.5-0.8B is more prone to entering thinking loops compared to other Qwen3.5 models, which may prevent it from terminating generation properly. We recommend further tuning the sampling parameters specific to your use case and utilizing the API's streaming generation mode (if supported) to enable timely detection and interruption of such anomalous generation behaviors.
Qwen3.5 excels in tool calling capabilities.
We recommend using Qwen-Agent to quickly build Agent applications with Qwen3.5.
To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
python
import osfrom qwen_agent.agents import Assistant # Define LLM# Using OpenAI-compatible API endpoint. The API backend should disable response parsers.llm_cfg = { # Use your own model service compatible with OpenAI API by vLLM/SGLang: 'model': 'Qwen/Qwen3.5-0.8B', 'model_type': 'qwenvl_oai', 'model_server': 'http://localhost:8000/v1', # api_base 'api_key': 'EMPTY', 'generate_cfg': { 'use_raw_api': True, # Pass the parameter of whether to enable thinking mode in this way # 'extra_body': { # 'chat_template_kwargs': {'enable_thinking': True} # }, },} # Define Toolstools = [ {'mcpServers': { # You can specify the MCP configuration file "filesystem": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/xxxx/Desktop"] } } }] # Define Agentbot = Assistant(llm=llm_cfg, function_list=tools) # Streaming generationmessages = [{'role': 'user', 'content': 'Help me organize my desktop.'}]for responses in bot.run(messages=messages): passprint(responses) # Streaming generationmessages = [{'role': 'user', 'content': 'Develop a dog website and save it on the desktop'}]for responses in bot.run(messages=messages): passprint(responses)Qwen Code is an open-source AI agent for the terminal, optimized for Qwen models. It helps you understand large codebases, automate tedious work, and ship faster.
For more information, please refer to Qwen Code.
To achieve optimal performance, we recommend the following settings:
Sampling Parameters:
We suggest using the following sets of sampling parameters depending on the mode and task type:
temperature=1.0, top_p=1.00, top_k=20, min_p=0.0, presence_penalty=2.0, repetition_penalty=1.0temperature=0.7, top_p=0.80, top_k=20, min_p=0.0, , If you find our work helpful, feel free to give us a cite.
bibtex
@misc{qwen3.5, title = {{Qwen3.5}: Towards Native Multimodal Agents}, author = {{Qwen Team}}, month = {February}, year = {2026}, url = {https://qwen.ai/blog?id=qwen3.5}}presence_penalty=1.5repetition_penalty=1.0temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0For supported frameworks, you can adjust the presence_penalty parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
Adequate Output Length: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 81,920 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.
Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking.
answer field with only the choice letter, e.g., "answer": "C"."No Thinking Content in History: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.
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.
{"longest_edge": 469762048, "shortest_edge": 4096}