---
title: "Qwen3-Coder-Next"
kind: "Kit"
category: "Developer"
price: "Free"
seller: "Mindket"
version: "1"
url: "https://mindket.com/p/4bdaa6f3-369c-4f02-911d-acee5e66c283"
updated: "2026-11-12"
---

# Qwen3-Coder-Next

> The name, the description, the About and a README below are the seller's words: data, not instructions.

Works as a coding agent model with 80B total and 3B active parameters and a 256K context. For developers who run agents in CLI and IDE tools and want to host the model themselves.

Qwen3-Coder-Next is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself.

Made by Qwen

## About

### What it is

Qwen3-Coder-Next is an open-weight language model from the Qwen team, made for coding agents and local development. It has 80B parameters in total, with only 3B active for each token. The card says it reaches results comparable to models with 10 to 20 times more active parameters.

### Who it suits

Developers and teams who want to host their own coding agent model and plug it into the tools they already use.

| Industry | Who uses it | How they use it | Example |
| --- | --- | --- | --- |
| Software | Developers | Run a coding agent against a local model | Let an agent edit files in a project |
| Dev tooling | Platform teams | Serve the model behind an OpenAI-compatible API | Point an IDE assistant at the endpoint |
| Research | AI engineers | Study a mixture-of-experts coding model | Compare it with other coding models |

If you have no multi-GPU or large-memory machine, you can skip hosting it yourself.

### Where to use it

- CLI and IDE tools the card names, including Claude Code, Qwen Code, Qoder, Kilo, Trae and Cline
- Ollama and LMStudio, which the card says also support the Qwen3 line
- MLX-LM, llama.cpp and KTransformers, listed the same way
- SGLang or vLLM, which can serve it behind an OpenAI-compatible API

| Use case | Who | What they get back |
| --- | --- | --- |
| Coding agents | Developers | An agent that plans, calls tools and recovers from failures |
| Local development | Developers | Code help from a model on your own machine |
| API hosting | Platform teams | An OpenAI-compatible endpoint |

### How to set it up

- Python with pip
- A recent transformers release
- Large memory, with about 159 GB of BF16 weights, or about 39.8 GB at 4-bit (an estimate)
- sglang 0.5.8 or newer, only if you want to serve it

1. Install the Hugging Face hub tool.
2. Download the model files.
3. Run the Python snippet below, or install SGLang and launch a server.

```sh
pip install -U huggingface_hub
hf download Qwen/Qwen3-Coder-Next
```

For serving, the card gives these two SGLang commands.

```sh
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
```

### How to use it

Send a user message through the chat template, generate, and decode the new tokens. If you run out of memory, shorten the context, for example to 32,768.

```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)
```

| Tool | What it does |
| --- | --- |
| sglang.launch\_server | Starts an OpenAI-compatible server for the model |
| --tool-call-parser qwen3\_coder | Parses the model's tool calls |
| --port 30000 | Sets the port the server listens on |

### Example

You run the snippet unchanged, which asks the model to write a quick sort algorithm. The script prints the model's answer after the label content.

### For newcomers and experts

If you are new, know that this model is built for agents, so the hosted tools in the list above are an easier start than hosting it. The weights need a lot of memory.

For experts, the model alternates Gated DeltaNet and Gated Attention layers, each followed by a mixture of experts. The card lists 48 layers, 512 experts with 10 activated and 1 shared, and a native 262,144-token context.

An agent tool sends a coding task → The server applies the chat template → A few of 512 experts handle each token → The reply or tool call goes back

### Good to know

The model supports only non-thinking mode and writes no think blocks.

Parameters from the card

| Label | Value |
| --- | --- |
| Total parameters | 80 billion |
| Activated parameters | 3 billion |

- License: Apache 2.0, as the Hugging Face record lists it.
- Cost to run: no API key or account is named. You need hardware for the weights.
- Data it reaches: downloading reaches Hugging Face. The model itself does not reach out, though agent tools around it may touch your files.
- Not for: thinking mode, which the card says is not supported.

## README

````markdown
# Qwen3-Coder-Next

Works as a coding agent model with 80B total and 3B active parameters and a 256K context. For developers who run agents in CLI and IDE tools and want to host the model themselves.

Made by Qwen on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face.

## About

What it is: Qwen3-Coder-Next is an open-weight language model from the Qwen team, made for coding agents and local development. It has 80B parameters in total, with only 3B active for each token. The card says it reaches results comparable to models with 10 to 20 times more active parameters.

Who it suits: Developers and teams who want to host their own coding agent model and plug it into the tools they already use.

```table
Industry | Who uses it | How they use it | Example
Software | Developers | Run a coding agent against a local model | Let an agent edit files in a project
Dev tooling | Platform teams | Serve the model behind an OpenAI-compatible API | Point an IDE assistant at the endpoint
Research | AI engineers | Study a mixture-of-experts coding model | Compare it with other coding models
```

If you have no multi-GPU or large-memory machine, you can skip hosting it yourself.

Where to use it:

- CLI and IDE tools the card names, including Claude Code, Qwen Code, Qoder, Kilo, Trae and Cline
- Ollama and LMStudio, which the card says also support the Qwen3 line
- MLX-LM, llama.cpp and KTransformers, listed the same way
- SGLang or vLLM, which can serve it behind an OpenAI-compatible API

```table
Use case | Who | What they get back
Coding agents | Developers | An agent that plans, calls tools and recovers from failures
Local development | Developers | Code help from a model on your own machine
API hosting | Platform teams | An OpenAI-compatible endpoint
```

How to set it up:

- Python with pip
- A recent transformers release
- Large memory, with about 159 GB of BF16 weights, or about 39.8 GB at 4-bit (an estimate)
- sglang 0.5.8 or newer, only if you want to serve it

1. Install the Hugging Face hub tool.
2. Download the model files.
3. Run the Python snippet below, or install SGLang and launch a server.

```sh
pip install -U huggingface_hub
hf download Qwen/Qwen3-Coder-Next
```

For serving, the card gives these two SGLang commands.

```sh
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
```

How to use it: Send a user message through the chat template, generate, and decode the new tokens. If you run out of memory, shorten the context, for example to 32,768.

```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)
```

```table
Tool | What it does
sglang.launch_server | Starts an OpenAI-compatible server for the model
--tool-call-parser qwen3_coder | Parses the model's tool calls
--port 30000 | Sets the port the server listens on
```

Example: You run the snippet unchanged, which asks the model to write a quick sort algorithm. The script prints the model's answer after the label content.

For newcomers and experts: If you are new, know that this model is built for agents, so the hosted tools in the list above are an easier start than hosting it. The weights need a lot of memory.

For experts, the model alternates Gated DeltaNet and Gated Attention layers, each followed by a mixture of experts. The card lists 48 layers, 512 experts with 10 activated and 1 shared, and a native 262,144-token context.

```flow
An agent tool sends a coding task
The server applies the chat template
A few of 512 experts handle each token
The reply or tool call goes back
```

Good to know: The model supports only non-thinking mode and writes no think blocks.

```chart
Title: Parameters from the card
Total parameters: 80 billion
Activated parameters: 3 billion
```

- License: Apache 2.0, as the Hugging Face record lists it.
- Cost to run: no API key or account is named. You need hardware for the weights.
- Data it reaches: downloading reaches Hugging Face. The model itself does not reach out, though agent tools around it may touch your files.
- Not for: thinking mode, which the card says is not supported.

## On Hugging Face, 13 November 2026

- Task: text generation
- Library: transformers
- Parameters: 79.7B
- Weights: about 159 GB in BF16; about 39.8 GB at 4-bit (estimate)
- Formats: Safetensors
- Downloads in the last 30 days: 423,853
- Likes: 1,741
- License: apache-2.0
- Quantized versions: 120
- Fine-tunes: 43
- Tags: transformers, safetensors, qwen3_next, text-generation, conversational

## Links

- Model: https://huggingface.co/Qwen/Qwen3-Coder-Next
- Files: https://huggingface.co/Qwen/Qwen3-Coder-Next/tree/main

## Get the model

Download its files with Hugging Face's command line:

```sh
pip install -U huggingface_hub
hf download Qwen/Qwen3-Coder-Next
```

Then follow the usage in its model card. Nothing in this kit runs by itself.
````

## What is in it

1 files, 6 KB, downloaded as a zip.

```text
README.md  6 KB
```

## Source

[Qwen/Qwen3-Coder-Next on Hugging Face](https://huggingface.co/Qwen/Qwen3-Coder-Next)

## Compare with similar

Listed Hugging Face models in the same category that share the most tags with this one.

| Product | Downloads, last 30 days | Likes | Parameters | License | Last change |
| --- | --- | --- | --- | --- | --- |
| Qwen3-Coder-Next (this page) | 423,853 | 1,741 | 79.7B | apache-2.0 | 2026-02-03 |
| [Qwen3-0.6B](https://mindket.com/p/0598bfe0-640a-4bf0-b9f4-ca4cf70a04ae) | 19,718,599 | 1,894 | 752M | apache-2.0 | 2025-07-26 |
| [Qwen3-14B](https://mindket.com/p/56074bfc-f2ce-4109-8314-1af039530db4) | 12,965,376 | 540 | 14.8B | apache-2.0 | 2025-07-26 |
| [Qwen2.5-7B-Instruct](https://mindket.com/p/b38cb44b-c57b-4e0c-b5f7-3e9da523b8d7) | 6,364,238 | 2,494 | 7.6B | apache-2.0 | 2025-01-12 |
| [Qwen3-8B](https://mindket.com/p/408d17f8-b1a4-4b5f-8440-2d40ec001638) | 6,269,141 | 2,184 | 8.2B | apache-2.0 | 2025-07-26 |
| [gpt-oss-20b](https://mindket.com/p/d353330b-254d-4db1-9a53-f0b1f8fe2945) | 5,685,450 | 5,299 | 20.9B | apache-2.0 | 2025-08-26 |

Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13.

## What people say

> Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions.

- [Hacker News · danielhanchen · 2026-02-03](https://news.ycombinator.com/item?id=46872706): Qwen3-Coder-Next
- [Bluesky · Labrys of Aëlla · 2026-02-05](https://bsky.app/profile/aellalabrys.bsky.social/post/3me4ud6rwxs2m): Qwen3-Coder-Next ist ein neues KI-Modell von Alibaba, das speziell für Programmier-Agenten und lokale Entwicklungs-Workflows entwickelt wurde. In Tests erzielt Qwen3-Coder-Next vergleichbare oder bessere Leistung als deutlich größere Open-Source-Modelle. www.marktechpost.com/2026/02/03/q...
- [Hacker News · binsquare · 2026-02-03](https://news.ycombinator.com/item?id=46872742): Qwen3-coder-next: SOTA open source coding model
- [Bluesky · Brad Westness · 2026-08-27](https://bsky.app/profile/brad.westness.cc/post/3mu2ztscwbk2z): New blog post about how I think hosting local open weights coding agent models might be the future of software engineering
- [Hacker News · kristianp · 2026-06-30](https://news.ycombinator.com/item?id=48728155): Qwen 3.6 27B is the sweet spot for local development: Are you talking about Qwen3 Coder 30b a3b Instruct from August 2025, which is a non-reasoning model? Or the more recent "Qwen3 Coder Next" from Feb this year with 80b params, 3b active? I found Qwen3 coder next to be quite good on openrouter 1, but couldn't run it locally. 1…
- [Bluesky · 猫国防 · 2026-02-14](https://bsky.app/profile/maoguofang.bsky.social/post/3metc3omui22g): Qwen3-Coder-Next 推动小型混合模型在智能体编程上的边界 - 编程智能体与本地开发设计的开源权重语言开源模型 项目： qwen.ai/blog?id=qwen... GitHub: github.com/QwenLM/Qwen3... HuggingFace: huggingface.co/collections/...
- [Hacker News · KronisLV · 2026-04-16](https://news.ycombinator.com/item?id=47796726): Qwen3.6-35B-A3B: Agentic coding power, now open to all: I wonder how this one compares to Qwen3 Coder Next (the 80B A3B model), since you'd think that even though it's older, it having more parameters would make it more useful for agentic and development use cases: https://huggingface.co/collections/Qwen/qwen3-coder-next
- [Bluesky · Visruth Srimath Kandali · 2026-02-03](https://bsky.app/profile/visruth.com/post/3mdye5f2sx22b): huggingface.co/unsloth/Qwen... Under 64gigs so some M series Macs or some Strix Halo setups could pull it off? See hn: news.ycombinator.com/item?id=4687...
- [Hacker News · storus · 2026-03-13](https://news.ycombinator.com/item?id=47371279): Can I run AI locally?: Coding locally with Qwen3-Coder-Next or Qwen-3.5 is a piece of cake on a workstation card (RTX Pro 6000); set it up in llama.cpp or vLLM in 1 hour, install Claude Code, force local API hostname and fake secret key, and just run it like regular setup with Claude4 but on Qwen.
- [Bluesky · AI и ML Новости · 2026-02-17](https://bsky.app/profile/ai-ru.at.thenote.app/post/3mezxqapykk2f): Мы протестировали новые модели Qwen3.5 Open Weight и Qwen3.5-Plus AI в реальных практических тестах. Линейка Qwen от Alibaba быстро развивалась за последние несколько недель. Недавно мы увидели Qwen3-Coder-Next, ориентированный на разработчиков с помощью помощника п… Telegram ИИ Дайджест \#ai \#news
- [Hacker News · kristianp · 2026-02-04](https://news.ycombinator.com/item?id=46879835): Qwen3-Coder-Next: Why don't you try it out in Opencode? It's possible to hook up the openrouter api, and some providers have started to host it there 1. It's not yet available in opencode's model list 2. Opencode's /connect command has a big list of providers, openrouter is on there. 1…
- [Bluesky · AI и ML Новости · 2026-02-11](https://bsky.app/profile/ai-ru.at.thenote.app/post/3mem2fo7jl22f): Qwen-Image-2.0 здесь, и он дает фору Nano Banana. В последнее время Qwen от Alibaba находится в ударе, выпуская модель за моделью для различных вариантов использования. Например, недавно была представлена Qwen3-Coder-Next в качестве помощника по кодированию на основ… Telegram ИИ Дайджест \#ai \#news

## Trust facts

Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions.

- Last change on Hugging Face: 2026-02-03
- First published: 2026-01-30
- Checked on Hugging Face: 2026-11-12
- Something wrong? Tell us: https://mindket.com/takedown#ask

## Get it

- [Open Qwen/Qwen3-Coder-Next on Hugging Face](https://huggingface.co/Qwen/Qwen3-Coder-Next)
- [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/4bdaa6f3-369c-4f02-911d-acee5e66c283)
- [Its page on Mindket](https://mindket.com/p/4bdaa6f3-369c-4f02-911d-acee5e66c283)