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.

Model Overview

• 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

We advise you to use the latest version of transformers.

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)

For deployment, you can use the latest sglang or vllm to create an OpenAI-compatible API endpoint.

sglang>=v0.5.8 is required for Qwen3-Coder-Next, which can be installed using:


pip install 'sglang[all]>=v0.5.8'

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.


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>=0.15.0 is required for Qwen3-Coder-Next, which can be installed using:


pip install 'vllm>=0.15.0'

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.


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.

# 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])

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}
}