Qwen/Qwen3.6-35B-A3B · Hugging Face

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Qwen3.6-35B-A3B

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Image-Text-to-Text Transformers Safetensors qwen3_5_moe conversational Eval Results

License: apache-2.0

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Instructions to use Qwen/Qwen3.6-35B-A3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

How to use Qwen/Qwen3.6-35B-A3B with Transformers:

Use a pipeline as a high-level helper

from transformers import pipeline

pipe = pipeline("image-text-to-text", model="Qwen/Qwen3.6-35B-A3B") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)

Load model directly

from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Qwen/Qwen3.6-35B-A3B") model = AutoModelForMultimodalLM.from_pretrained("Qwen/Qwen3.6-35B-A3B") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

How to use Qwen/Qwen3.6-35B-A3B with vLLM:

Install from pip and serve model

Install vLLM from pip:

pip install vllm

Start the vLLM server:

vllm serve "Qwen/Qwen3.6-35B-A3B"

Call the server using curl (OpenAI-compatible API):

curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Qwen/Qwen3.6-35B-A3B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'

Use Docker

docker model run hf.co/Qwen/Qwen3.6-35B-A3B

How to use Qwen/Qwen3.6-35B-A3B with SGLang:

Install from pip and serve model

Install SGLang from pip:

pip install sglang

Start the SGLang server:

python3 -m sglang.launch_server \ --model-path "Qwen/Qwen3.6-35B-A3B" \ --host 0.0.0.0 \ --port 30000

Call the server using curl (OpenAI-compatible API):

curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Qwen/Qwen3.6-35B-A3B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'

Use Docker images

docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Qwen/Qwen3.6-35B-A3B" \ --host 0.0.0.0 \ --port 30000

Call the server using curl (OpenAI-compatible API):

curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Qwen/Qwen3.6-35B-A3B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'

How to use Qwen/Qwen3.6-35B-A3B with Docker Model Runner:

docker model run hf.co/Qwen/Qwen3.6-35B-A3B

Qwen3.6-35B-A3B

This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format.

These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc.

Following the February release of the Qwen3.5 series, we're pleased to share the first open-weight variant of Qwen3.6. Built on direct feedback from the community, Qwen3.6 prioritizes stability and real-world utility, offering developers a more intuitive, responsive, and genuinely productive coding experience.

Qwen3.6 Highlights

This release delivers substantial upgrades, particularly in

Benchmark Results

For more details, please refer to our blog post Qwen3.6-35B-A3B.

Model Overview

Benchmark Results

Language

Qwen3.5-27B| Gemma4-31B| Qwen3.5-35BA3B| Gemma4-26BA4B| Qwen3.6-35BA3B

Coding Agent SWE-bench Verified | 75.0 | 52.0 | 70.0 | 17.4 | 73.4 SWE-bench Multilingual | 69.3 | 51.7 | 60.3 | 17.3 | 67.2 SWE-bench Pro | 51.2 | 35.7 | 44.6 | 13.8 | 49.5 Terminal-Bench 2.0 | 41.6 | 42.9 | 40.5 | 34.2 | 51.5 Claw-Eval Avg | 64.3 | 48.5 | 65.4 | 58.8 | 68.7 Claw-Eval Pass^3 | 46.2 | 25.0 | 51.0 | 28.0 | 50.0 SkillsBench Avg5 | 27.2 | 23.6 | 4.4 | 12.3 | 28.7 QwenClawBench | 52.2 | 41.7 | 47.7 | 38.7 | 52.6 NL2Repo | 27.3 | 15.5 | 20.5 | 11.6 | 29.4 QwenWebBench | 1068 | 1197 | 978 | 1178 | 1397 General Agent TAU3-Bench | 68.4 | 67.5 | 68.9 | 59.0 | 67.2 VITA-Bench | 41.8 | 43.0 | 29.1 | 36.9 | 35.6 DeepPlanning | 22.6 | 24.0 | 22.8 | 16.2 | 25.9 Tool Decathlon | 31.5 | 21.2 | 28.7 | 12.0 | 26.9 MCPMark | 36.3 | 18.1 | 27.0 | 14.2 | 37.0 MCP-Atlas | 68.4 | 57.2 | 62.4 | 50.0 | 62.8 WideSearch | 66.4 | 35.2 | 59.1 | 38.3 | 60.1 Knowledge MMLU-Pro | 86.1 | 85.2 | 85.3 | 82.6 | 85.2 MMLU-Redux | 93.2 | 93.7 | 93.3 | 92.7 | 93.3 SuperGPQA | 65.6 | 65.7 | 63.4 | 61.4 | 64.7 C-Eval | 90.5 | 82.6 | 90.2 | 82.5 | 90.0 STEM & Reasoning GPQA | 85.5 | 84.3 | 84.2 | 82.3 | 86.0 HLE | 24.3 | 19.5 | 22.4 | 8.7 | 21.4 LiveCodeBench v6 | 80.7 | 80.0 | 74.6 | 77.1 | 80.4 HMMT Feb 25 | 92.0 | 88.7 | 89.0 | 91.7 | 90.7 HMMT Nov 25 | 89.8 | 87.5 | 89.2 | 87.5 | 89.1 HMMT Feb 26 | 84.3 | 77.2 | 78.7 | 79.0 | 83.6 IMOAnswerBench | 79.9 | 74.5 | 76.8 | 74.3 | 78.9 AIME26 | 92.6 | 89.2 | 91.0 | 88.3 | 92.7

Vision Language

Qwen3.5-27B| Claude-Sonnet-4.5| Gemma4-31B| Gemma4-26BA4B| Qwen3.5-35B-A3B| Qwen3.6-35B-A3B

STEM and Puzzle MMMU | 82.3 | 79.6 | 80.4 | 78.4 | 81.4 | 81.7 MMMU-Pro | 75.0 | 68.4 | 76.9 | 73.8 | 75.1 | 75.3 Mathvista(mini) | 87.8 | 79.8 | 79.3 | 79.4 | 86.2 | 86.4 ZEROBench_sub | 36.2 | 26.3 | 26.0 | 26.3 | 34.1 | 34.4 General VQA RealWorldQA | 83.7 | 70.3 | 72.3 | 72.2 | 84.1 | 85.3 MMBenchEN-DEV-v1.1 | 92.6 | 88.3 | 90.9 | 89.0 | 91.5 | 92.8 SimpleVQA | 56.0 | 57.6 | 52.9 | 52.2 | 58.3 | 58.9 HallusionBench | 70.0 | 59.9 | 67.4 | 66.1 | 67.9 | 69.8 Text Recognition and Document Understanding OmniDocBench1.5 | 88.9 | 85.8 | 80.1 | 74.4 | 89.3 | 89.9 CharXiv(RQ) | 79.5 | 67.2 | 67.9 | 69.0 | 77.5 | 78.0 CC-OCR | 81.0 | 68.1 | 75.7 | 74.5 | 80.7 | 81.9 AI2D_TEST | 92.9 | 87.0 | 89.0 | 88.3 | 92.6 | 92.7 Spatial Intelligence RefCOCO(avg) | 90.9 | -- | -- | -- | 89.2 | 92.0 ODInW13 | 41.1 | -- | -- | -- | 42.6 | 50.8 EmbSpatialBench | 84.5 | 71.8 | -- | -- | 83.1 | 84.3 RefSpatialBench | 67.7 | -- | -- | -- | 63.5 | 64.3 Video Understanding VideoMME(w sub.) | 87.0 | 81.1 | -- | -- | 86.6 | 86.6 VideoMME(w/o sub.) | 82.8 | 75.3 | -- | -- | 82.5 | 82.5 VideoMMMU | 82.3 | 77.6 | 81.6 | 76.0 | 80.4 | 83.7 MLVU | 85.9 | 72.8 | -- | -- | 85.6 | 86.2 MVBench | 74.6 | -- | -- | -- | 74.8 | 74.6 LVBench | 73.6 | -- | -- | -- | 71.4 | 71.4

Quickstart

For streamlined integration, we recommend using Qwen3.6 via APIs. Below is a guide to use Qwen3.6 via OpenAI-compatible API.

Serving Qwen3.6

Qwen3.6 can be served via APIs with popular inference frameworks. In the following, we show example commands to launch OpenAI-Compatible API servers for Qwen3.6 models.

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.

The model has a default context length of 262,144 tokens. If you encounter out-of-memory (OOM) errors, consider reducing the context window. However, because Qwen3.6 leverages extended context for complex tasks, we advise maintaining a context length of at least 128K tokens to preserve thinking capabilities.

SGLang

SGLang is a fast serving framework for large language models and vision language models. sglang>=0.5.10 is recommended for Qwen3.6, which can be installed using the following command in a fresh environment:

uv pip install sglang[all]

See its documentation for more details.

The following will create API endpoints at http://localhost:8000/v1:

python -m sglang.launch_server --model-path Qwen/Qwen3.6-35B-A3B --port 8000 --tp-size 8 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3

python -m sglang.launch_server --model-path Qwen/Qwen3.6-35B-A3B --port 8000 --tp-size 8 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3 --tool-call-parser qwen3_coder

python -m sglang.launch_server --model-path Qwen/Qwen3.6-35B-A3B --port 8000 --tp-size 8 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3 --speculative-algo NEXTN --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4

For detailed deployment guide, see the SGLang Qwen3.5 Cookbook.

vLLM

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. vllm>=0.19.0 is recommended for Qwen3.6, which can be installed using the following command in a fresh environment:

uv pip install vllm --torch-backend=auto

See its documentation for more details.

The following will create API endpoints at http://localhost:8000/v1:

vllm serve Qwen/Qwen3.6-35B-A3B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3

vllm serve Qwen/Qwen3.6-35B-A3B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder

vllm serve Qwen/Qwen3.6-35B-A3B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}'

vllm serve Qwen/Qwen3.6-35B-A3B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --language-model-only

For detailed deployment guide, see the vLLM Qwen3.5 Recipe.

KTransformers

KTransformers is a flexible framework for experiencing cutting-edge LLM inference optimizations with CPU-GPU heterogeneous computing. For running Qwen3.6 with KTransformers, see the KTransformers Deployment Guide.

Hugging Face Transformers

Hugging Face Transformers contains a lightweight server which can be used for quick testing and moderate load deployment. The latest transformers is required for Qwen3.6:

pip install "transformers[serving]"

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:

transformers serve Qwen/Qwen3.6-35B-A3B --port 8000 --continuous-batching

Using Qwen3.6 via the Chat Completions API

The 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.:

pip install -U openai

Set the following accordingly

export OPENAI_BASE_URL="http://localhost:8000/v1" export OPENAI_API_KEY="EMPTY"

We recommend using the following set of sampling parameters for generation

Please note that the support for sampling parameters varies according to inference frameworks.

Qwen3.6 models operate in thinking mode by default, generating thinking content signified by <think>\n...</think>\n\n before producing the final responses. To disable thinking content and obtain direct response, refer to the examples here.

Text-Only Input

from openai import OpenAI

Configured by environment variables

client = OpenAI()

messages = [ {"role": "user", "content": "Type \"I love Qwen3.6\" backwards"}, ]

chat_response = client.chat.completions.create( model="Qwen/Qwen3.6-35B-A3B", messages=messages, max_tokens=81920, temperature=1.0, top_p=0.95, presence_penalty=1.5, extra_body={ "top_k": 20, }, ) print("Chat response:", chat_response)

Image Input

from openai import OpenAI

Configured by environment variables

client = 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/Qwen3.6-35B-A3B", messages=messages, max_tokens=81920, temperature=1.0, top_p=0.95, presence_penalty=1.5, extra_body={ "top_k": 20, }, ) print("Chat response:", chat_response)

Video Input

from openai import OpenAI

Configured by environment variables

client = 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?" } ] } ]

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/Qwen3.6-35B-A3B", messages=messages, max_tokens=81920, temperature=1.0, top_p=0.95, presence_penalty=1.5, extra_body={ "top_k": 20, "mm_processor_kwargs": {"fps": 2, "do_sample_frames": True}, }, )

print("Chat response:", chat_response)

Instruct (or Non-Thinking) Mode

Qwen3.6 does not officially support the soft switch of Qwen3, i.e., /think and /nothink.

Qwen3.6 will think by default before response. You can obtain direct response from the model without thinking by configuring the API parameters. For example,

from openai import OpenAI

Configured by environment variables

client = OpenAI()

messages = [ { "role": "user", "content": [ { "type": "image_url", "image_url": { "url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.6/demo/RealWorld/RealWorld-04.png" } }, { "type": "text", "text": "Where is this?" } ] } ]

chat_response = client.chat.completions.create( model="Qwen/Qwen3.6-35B-A3B", messages=messages, max_tokens=32768, 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)

If you are using APIs from Alibaba Cloud Model Studio, in addition to changing model, please use "enable_thinking": False instead of "chat_template_kwargs": {"enable_thinking": False}.

Preserve Thinking

By default, only the thinking blocks generated in handling the latest user message is retained, resulting in a pattern commonly as interleaved thinking. Qwen3.6 has been additionally trained to preserve and leverage thinking traces from historical messages. You can enable this behavior by setting the preserve_thinking option:

from openai import OpenAI

Configured by environment variables

client = OpenAI()

messages = [...]

chat_response = client.chat.completions.create( model="Qwen/Qwen3.6-35B-A3B", messages=messages, max_tokens=32768, temperature=0.7, top_p=0.8, presence_penalty=1.5, extra_body={ "top_k": 20, "chat_template_kwargs": {"preserve_thinking": True}, }, ) print("Chat response:", chat_response)

If you are using APIs from Alibaba Cloud Model Studio, in addition to changing model, please use "preserve_thinking": True instead of "chat_template_kwargs": {"preserve_thinking": False}.

This capability is particularly beneficial for agent scenarios, where maintaining full reasoning context can enhance decision consistency and, in many cases, reduce overall token consumption by minimizing redundant reasoning. Additionally, it can improve KV cache utilization, optimizing inference efficiency in both thinking and non-thinking modes.

Agentic Usage

Qwen3.6 excels in tool calling capabilities.

Qwen-Agent

We recommend using Qwen-Agent to quickly build Agent applications with Qwen3.6.

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.

import os from qwen_agent.agents import Assistant

Define LLM

Using Alibaba Cloud Model Studio

llm_cfg = {

Use the OpenAI-compatible model service provided by DashScope:

'model': 'Qwen3.6-35B-A3B', 'model_type': 'qwenvl_oai', 'model_server': 'https://dashscope.aliyuncs.com/compatible-mode/v1', 'api_key': os.getenv('DASHSCOPE_API_KEY'),

'generate_cfg': { 'use_raw_api': True,

When using Dash Scope OAI API, pass the parameter of whether to enable thinking mode in this way

'extra_body': { 'enable_thinking': True, 'preserve_thinking': True, }, }, }

Using OpenAI-compatible API endpoint.

functionality of the deployment frameworks and let Qwen-Agent automate the related operations.

llm_cfg = {

# Use your own model service compatible with OpenAI API by vLLM/SGLang:

'model': 'Qwen/Qwen3.6-35B-A3B',

'model_type': 'qwenvl_oai',

'model_server': 'http://localhost:8000/v1', # api_base

'api_key': 'EMPTY',

'generate_cfg': {

'use_raw_api': True,

# When using vLLM/SGLang OAI API, pass the parameter of whether to enable thinking mode in this way

'extra_body': {

'chat_template_kwargs':

},

},

}

Define Tools

tools = [ {'mcpServers': { # You can specify the MCP configuration file "filesystem": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/xxxx/Desktop"] } } } ]

Define Agent

bot = Assistant(llm=llm_cfg, function_list=tools)

Streaming generation

messages = [{'role': 'user', 'content': 'Help me organize my desktop.'}] for responses in bot.run(messages=messages): pass print(responses)

Streaming generation

messages = [{'role': 'user', 'content': 'Develop a dog website and save it on the desktop'}] for responses in bot.run(messages=messages): pass print(responses)

Qwen Code

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.

Processing Ultra-Long Texts

Qwen3.6 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., transformers, vllm, ktransformers and sglang. In general, there are two approaches to enabling YaRN for supported frameworks:

{ "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, }

For vllm, you can use

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 1010000

For sglang and ktransformers, you can use

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 1010000

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_parameters configuration only when processing long contexts is required. It is also recommended to modify the factor as needed. For example, if the typical context length for your application is 524,288 tokens, it would be better to set factor as 2.0.

Best Practices

To achieve optimal performance, we recommend the following settings:

  1. Sampling Parameters :

  2. We suggest using the following sets of sampling parameters depending on the mode and task type:

  3. Thinking mode for general tasks : temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
  4. Thinking mode for precise coding tasks (e.g., WebDev) : temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0
  5. 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=1.0
  6. For 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.

  7. 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.

  8. Standardize Output Format : We recommend using prompts to standardize model outputs when benchmarking.

  9. Math Problems : Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.

  10. Multiple-Choice Questions : Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the answer field with only the choice letter, e.g., "answer": "C"."
  11. 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,

{"longest_edge": 469762048, "shortest_edge": 4096}

Alternatively, override the default values via engine startup parameters. For implementation details, refer to: vLLM / SGLang.

Citation

If you find our work helpful, feel free to give us a cite.

@misc{qwen36_35b_a3b, title = {{Qwen3.6-35B-A3B}: Agentic Coding Power, Now Open to All}, url = {https://qwen.ai/blog?id=qwen3.6-35b-a3b}, author = {{Qwen Team}}, month = {April}, year = {2026} }

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🔧 curtburk/vLLM_on_NVIDIA_GB10 🧪 KyleHessling1/qwopus36-35b-a3b-eval 🧠 artificialguybr/Qwen3.6-35B-A3B-zero 🤖 Yash030/claude-code-proxy 📜 yuukicammy/kuzushiji-ocr 🚀 ashirato/surrogate-1-shard1 🚀 surrogate1/surrogate-1-shard2 🧠 sinanmut/Qwen3.6-35B-A3B-zero + 34 Spaces + 31 Spaces

Collection including Qwen/Qwen3.6-35B-A3B

Qwen3.6 Collection 4 items • Updated Apr 22 • 399

Evaluation results

86

49.5

85.2

73.4

51.5 *

44.1 *

90.7 *

58.3 *

Expand 15 evaluations