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Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.
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:
Qwen3-Coder-Next has the following features:
NOTE: This model supports only non-thinking mode and does not generate <think></think> 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.
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
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.
For deployment, you can use the latest sglang or vllm to create an OpenAI-compatible API endpoint.
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:
See its documentation 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.
[!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 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:
See its documentation 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.
[!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.
Qwen3-Coder-Next excels in tool calling capabilities.
You can simply define or use any tools as following example.
To achieve optimal performance, we recommend the following sampling parameters: temperature=1.0, top_p=0.95, top_k=40.
If you find our work helpful, feel free to give us a cite.
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen3-Coder-Next"
# load the tokenizer and the modeltokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto")
# prepare the model inputprompt = "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 completiongenerated_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)pip install 'sglang[all]>=v0.5.8'python -m sglang.launch_server --model Qwen/Qwen3-Coder-Next --port 30000 --tp-size 2 --tool-call-parser qwen3_coder```pip install 'vllm>=0.15.0'vllm serve Qwen/Qwen3-Coder-Next --port 8000 --tensor-parallel-size 2 --enable-auto-tool-choice --tool-call-parser qwen3_coder# Your tool implementationdef square_the_number(num: float) -> dict: return num ** 2
# Define Toolstools=[ { "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 LLMclient = 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])@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}}