Today, we’re announcing Qwen3-Coder, our most agentic code model to date. Qwen3-Coder is available in multiple sizes, but we’re excited to introduce its most powerful variant first: Qwen3-Coder-480B-A35B-Instruct. featuring the following key enhancements:

Significant Performance among open models on Agentic Coding, Agentic Browser-Use, and other foundational coding tasks, achieving results comparable to Claude Sonnet. Long-context Capabilities with native support for 256K tokens, extendable up to 1M tokens using Yarn, optimized for repository-scale understanding. Agentic Coding supporting for most platform such as Qwen Code, CLINE, featuring a specially designed function call format.

Qwen3-480B-A35B-Instruct has the following features:

Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 480B in total and 35B activated Number of Layers: 62 Number of Attention Heads (GQA): 96 for Q and 8 for KV Number of Experts: 160 Number of Activated Experts: 8 Context Length: 262,144 natively. NOTE: This model supports only non-thinking mode and does not generate blocks in its output. Meanwhile, specifying enable_thinking=False is no longer required.

For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub, and Documentation. We advise you to use the latest version of transformers.

With transformers<4.51.0, you will encounter the following error:

The following contains a code snippet illustrating how to use the model generate content based on given inputs.

from modelscope import AutoModelForCausalLM, AutoTokenizer

model_name = “Qwen/Qwen3-480B-A35B-Instruct”

load the tokenizer and the model

tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=“auto”, device_map=“auto” )

prepare the model input

prompt = “Write a quick sort algorithm.” messages = [ {“role”: “user”, “content”: prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) model_inputs = tokenizer([text], return_tensors=“pt”).to(model.device)

conduct text completion

generated_ids = model.generate( **model_inputs, max_new_tokens=65536 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()

content = tokenizer.decode(output_ids, skip_special_tokens=True)

print(“content:”, content)

Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as 32,768.

For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.

Agentic Coding Qwen3-Coder excels in tool calling capabilities.

You can simply define or use any tools as following example.

Your tool implementation

def square_the_number(num: float) -> dict: return num ** 2

Define Tools

tools=[ { “type”:“function”, “function”:{ “name”: “square_the_number”, “description”: “output the square of the number.”, “parameters”: { “type”: “object”, “required”: [“input_num”], “properties”: { ‘input_num’: { ‘type’: ‘number’, ‘description’: ‘input_num is a number that will be squared’ } }, } } } ]

import OpenAI

Define LLM

client = OpenAI( # Use a custom endpoint compatible with OpenAI API base_url=’http://localhost:8000/v1’, # api_base api_key=“EMPTY” )

messages = [{‘role’: ‘user’, ‘content’: ‘square the number 1024’}]

completion = client.chat.completions.create( messages=messages, model=“Qwen3-Coder-480B-A35B-Instruct”, max_tokens=65536, tools=tools, )

print(completion.choice[0])

Best Practices To achieve optimal performance, we recommend the following settings:

Sampling Parameters:

We suggest using temperature=0.7, top_p=0.8, top_k=20, repetition_penalty=1.05. Adequate Output Length: We recommend using an output length of 65,536 tokens for most queries, which is adequate for instruct models.

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

@misc{qwen3technicalreport, title={Qwen3 Technical Report}, author={Qwen Team}, year={2025}, eprint={2505.09388}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.09388}, }