# Qwen3-Coder-480B-A35B-Instruct ## Highlights 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.  ## Model Overview **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](https://qwenlm.github.io/blog/qwen3-coder/), [GitHub](https://github.com/QwenLM/Qwen3-Coder), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Quickstart We advise you to use the latest version of \`transformers\`. With \`transformers<4.51.0\`, you will encounter the following error: \`\`\` KeyError: 'qwen3_moe' \`\`\` The following contains a code snippet illustrating how to use the model generate content based on given inputs. \`\`\`python from transformers 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. \`\`\`python # 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: 1. **Sampling Parameters**: - We suggest using \`temperature=0.7\`, \`top_p=0.8\`, \`top_k=20\`, \`repetition_penalty=1.05\`. 2. **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\}, \} \`\`\`