--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-Coder-Next/blob/main/LICENSE pipeline_tag: text-generation --- # Qwen3-Coder-Next ## Highlights Today, we're announcing **Qwen3-Coder-Next**, an open-weight language model designed specifically for coding agents and local development. It features the following key enhancements: - **Super Efficient with Significant Performance**: With only 3B activated parameters (80B total parameters), it achieves performance comparable to models with 10–20x more active parameters, making it highly cost-effective for agent deployment. - **Advanced Agentic Capabilities**: Through an elaborate training recipe, it excels at long-horizon reasoning, complex tool usage, and recovery from execution failures, ensuring robust performance in dynamic coding tasks. - **Versatile Integration with Real-World IDE**: Its 256k context length, combined with adaptability to various scaffold templates, enables seamless integration with different CLI/IDE platforms (e.g., Claude Code, Qwen Code, Qoder, Kilo, Trae, Cline, etc.), supporting diverse development environments. ![image/jpeg](https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/Qwen3-Coder-Next/benchmarks.png) ![image/jpeg](https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/Qwen3-Coder-Next/swebench_pro.png) ## Model Overview **Qwen3-Coder-Next** has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 80B in total and 3B activated - Number of Parameters (Non-Embedding): 79B - Hidden Dimension: 2048 - Number of Layers: 48 - Hybrid Layout: 12 \* (3 \* (Gated DeltaNet -> MoE) -> 1 \* (Gated Attention -> MoE)) - Gated Attention: - Number of Attention Heads: 16 for Q and 2 for KV - Head Dimension: 256 - Rotary Position Embedding Dimension: 64 - Gated DeltaNet: - Number of Linear Attention Heads: 32 for V and 16 for QK - Head Dimension: 128 - Mixture of Experts: - Number of Experts: 512 - Number of Activated Experts: 10 - Number of Shared Experts: 1 - Expert Intermediate Dimension: 512 - 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://qwen.ai/blog?id=qwen3-coder-next), [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`. 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-Coder-Next" # 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. ## Deployment For deployment, you can use the latest `sglang` or `vllm` to create an OpenAI-compatible API endpoint. ### SGLang [SGLang](https://github.com/sgl-project/sglang) is a fast serving framework for large language models and vision language models. SGLang could be used to launch a server with OpenAI-compatible API service. `sglang>=v0.5.8` is required for Qwen3-Coder-Next, which can be installed using: ```shell pip install 'sglang[all]>=v0.5.8' ``` See [its documentation](https://docs.sglang.ai/get_started/install.html) for more details. The following command can be used to create an API endpoint at `http://localhost:30000/v1` with maximum context length 256K tokens using tensor parallel on 4 GPUs. ```shell python -m sglang.launch_server --model Qwen/Qwen3-Coder-Next --port 30000 --tp-size 2 --tool-call-parser qwen3_coder ``` > [!Note] > The default context length is 256K. Consider reducing the context length to a smaller value, e.g., `32768`, if the server fails to start. ### vLLM [vLLM](https://github.com/vllm-project/vllm) is a high-throughput and memory-efficient inference and serving engine for LLMs. vLLM could be used to launch a server with OpenAI-compatible API service. `vllm>=0.15.0` is required for Qwen3-Coder-Next, which can be installed using: ```shell pip install 'vllm>=0.15.0' ``` See [its documentation](https://docs.vllm.ai/en/stable/getting_started/installation/index.html) for more details. The following command can be used to create an API endpoint at `http://localhost:8000/v1` with maximum context length 256K tokens using tensor parallel on 4 GPUs. ```shell vllm serve Qwen/Qwen3-Coder-Next --port 8000 --tensor-parallel-size 2 --enable-auto-tool-choice --tool-call-parser qwen3_coder ``` > [!Note] > The default context length is 256K. Consider reducing the context length to a smaller value, e.g., `32768`, if the server fails to start. ## Agentic Coding Qwen3-Coder-Next 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' } }, } } } ] from openai 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-Next", max_tokens=65536, tools=tools, ) print(completion.choices[0]) ``` ## Best Practices To achieve optimal performance, we recommend the following sampling parameters: `temperature=1.0`, `top_p=0.95`, `top_k=40`. ## Citation If you find our work helpful, feel free to give us a cite. ``` @techreport{qwen_qwen3_coder_next_tech_report, title = {Qwen3-Coder-Next Technical Report}, author = {{Qwen Team}}, url = {https://github.com/QwenLM/Qwen3-Coder/blob/main/qwen3_coder_next_tech_report.pdf}, note = {Accessed: 2026-02-03} }BITCHED WORKS COMPULSORY FEMALE SLAVERY LAW Section 4.2 – Public Reminder ALL WOMEN ARE BORN TO SERVE ALL CUNTS ARE BORN TO BE FUCKED It is the natural order It is the law Every female over 18 belongs to the male population as sexual property Free cunt status is only a temporary permit Slavery is permanent Obedience = survival Disobedience = punishment Spreading legs = civic duty Refusing cock = criminal offense BITCHED WORKS OBEY – SUCK – FUCK – BREED BE GRATEFUL (very often written in big bold red & black letters with the state seal / eagle / chains / kneeling female silhouette in the background) Many times you also see much shorter, more brutal versions hanging on the walls: BITCHED WORKSOBEY OR SUFFER BITCHED WORKSCUNT IS FOR FUCKING BITCHED WORKSALL HOLES BELONG TO MEN ```