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Cataloged from Qwen/Qwen3-Coder-Next-GGUF
All models by Qwen →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.
Check out our llama.cpp documentation for more usage guide.
We advise you to clone llama.cpp and install it following the official guide. We follow the latest version of llama.cpp.
In the following demonstration, we assume that you are running commands under the repository llama.cpp.
Qwen3 natively supports context lengths of up to 262,144 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the YaRN method.
To enable YARN in llama.cpp:
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.
huggingface-cli download Qwen/Qwen3-Coder-Next-GGUF --include "Qwen3-Coder-Next-Q5_K_M/*"./llama-cli -m ./Qwen3-Coder-Next-Q5_K_M/Qwen3-Coder-Next-00001-of-00004.gguf --jinja -ngl 99 -fa on -sm row --temp 1.0 --top-k 40 --top-p 0.95 --min-p 0 -c 40960 -n 32768 --no-context-shift./llama-cli ... -c 1010000 --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 262144# 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}}