Qwen3-Coder-Next
Qwen3-Coder-Next is Alibaba Cloud's elite Apache 2.0 coding model, featuring an 80B MoE architecture and 256k context window for advanced local development.
alibabaQwen3February 3, 2026About Qwen3-Coder-Next
Learn about Qwen3-Coder-Next's capabilities, features, and how it can help you achieve better results.
Model Architecture
Qwen3-Coder-Next is a specialized open-weight model designed by Alibaba Cloud for software engineering agents. It utilizes a Mixture-of-Experts (MoE) architecture with 80 billion total parameters, but it only activates 3 billion parameters per token. This design combines the intelligence of a massive model with the inference speed of a small one. The architecture includes a hybrid attention mechanism, integrating Gated DeltaNet with standard Gated Attention to process contexts up to 262,144 tokens.
Agentic Specialization
The model is trained on over 800,000 verifiable coding tasks and executable environments. This training emphasizes long-horizon reasoning and the ability to recover from execution failures. It scores 70.8% on SWE-Bench Verified, demonstrating its capacity to handle multi-step development tasks from initial planning to final code execution. It excels in autonomous agentic frameworks like OpenClaw and Qwen Code.
Deployment and Privacy
Licensed under Apache 2.0, this model provides a secure alternative for developers who require local, private development environments. It can run on consumer-grade hardware with sufficient RAM through quantization. The high context window allows for repository-scale analysis without the performance degradation typically seen in smaller context models.
Use Cases
Discover the different ways you can use Qwen3-Coder-Next to achieve great results.
Autonomous Coding Agents
Powers frameworks to handle multi-step development tasks from planning to final execution.
Local Private Development
Runs elite coding assistance on consumer GPUs with 16GB VRAM using quantized MoE layers.
Large-Scale Repository Analysis
Processes entire codebases within its 256k window to identify technical debt.
Code Repair and Refactoring
Updates legacy code to modern standards by providing executable environment feedback.
Multilingual Scripting
Generates high-fidelity code across more than 40 programming languages including Rust and Go.
Interactive 3D Simulation
Builds complex web-based visualizers and simulations using rapid one-shot generation.
Strengths
Limitations
API Quick Start
alibaba/qwen-3-coder-next
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.DASHSCOPE_API_KEY,
baseURL: "https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
});
async function main() {
const completion = await client.chat.completions.create({
model: "qwen3-coder-next",
messages: [
{ role: "system", content: "You are a professional coding assistant." },
{ role: "user", content: "Write a React component for a sortable list." },
],
});
console.log(completion.choices[0].message.content);
}
main();Install the SDK and start making API calls in minutes.
Community Feedback
See what the community thinks about Qwen3-Coder-Next
“Nearly matches Claude in overall coding capabilities. Beats Claude 3.5 Sonnet on HumanEval at 92.7%.”
“The efficiency of the MoE version is insane for local hardware. I am getting 26 TPS on a mid-range system.”
“Self-speculative decoding is mathematically impossible for Qwen Coder Next due to recurrent states.”
“Qwen3-Coder-Next is based on MoE, and way stronger and smarter than before!”
“Demonstrating the ability to switch providers mid-project with the new 480B model variants.”
“The agentic training recipe on 800k tasks shows in the way it recovers from build errors.”
Related Videos
Watch tutorials, reviews, and discussions about Qwen3-Coder-Next
“Although it is an 80 billion total parameter model, there are only 3 billion active.”
“That's actually pretty nice. There's almost seemingly some color here. A real time audio visualizer.”
“that is we can we can run it locally. There are a bunch of quants out right now. There's quants for Apple systems. I think I saw an NVFP4 quant, but don't quote me on that. So that would be something interesting as well to explore”
“It's surprisingly practical, even at 3-bit quantized.”
“However, if we compare the result with Gemini 3.1 Pro, even though Gemini handled the animation a lot better, Qwen 3 Coder next result doesn't really look that far apart.”
“This time the prompt took around 30 minutes to complete and here is the result. This time we start to see the limit of the heavily quantized 80 billion model with complex concept prompt. The first”
“The speed of this guy is just very impressive.”
“memory. So we got two model content quantizations here. One for 128 GB RAM people and one for the 64 GB RAM people.”
“is the best one to get and 5.5 is still 95% token accuracy. And with 95, you can get that in a 64 GB RAM computer. So,”
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What to get right first
The decisions that are painful to change later in Qwen3-Coder-Next.
Use Long System Prompts
Provide the model with detailed examples and documentation to align its agentic behavior.
Iterative Error Feedback
Feed browser console error logs back into the model for high-success rate self-correction.
Optimize Layer Offloading
Offload specific MoE expert layers to system RAM to balance inference speed and reasoning.
Align Sampling Parameters
Use a temperature of 1.0 with top_p 0.95 and top_k 40 for the most accurate coding results.
Testimonials
What Our Users Say
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Frequently Asked Questions
Find answers to common questions about Qwen3-Coder-Next