Artificial Intelligence is reshaping how we build, test, and ship software. Over the past year, coding-focused AI models have evolved from simple code autocompletion tools into intelligent agents that can plan, reason, and even interact with real-world development environments. With this shift, we’re moving toward what’s being called Agentic Coding —a new way for AI to act not just as a helper, but as a hands-on collaborator in software engineering.
![qwen3-coder-main]()
Related Image: © Qwen.ai
Recently, the Qwen team introduced Qwen3-Coder , their most advanced agentic code model to date. Designed for developers, researchers, and automation enthusiasts, Qwen3-Coder bridges the gap between code generation and full-stack, context-aware software problem-solving.
A Quick Overview of Qwen3-Coder
Qwen3-Coder is part of the Qwen3 family of models developed by Alibaba Cloud. The version that caught everyone’s attention is the Qwen3-Coder-480B-A35B-Instruct —a Mixture-of-Experts (MoE) model with 480 billion parameters, out of which 35 billion are active at any given time. This approach gives the model massive scale while keeping inference efficient.
It supports a 256K token context window natively , and with extrapolation methods, it can handle up to 1 million tokens . That’s long enough to manage large codebases, read full documentation, and reason across multiple files without losing context.
In simpler terms, Qwen3-Coder is not just an AI that “writes code.” It’s a system that understands repositories , follows instructions , and acts as a coding agent —a step closer to AI-driven software development.
Pushing the Boundaries of Agentic Coding
Agentic coding refers to an AI model’s ability to act autonomously in multi-turn, tool-based environments. Think of it as an AI that can open files, edit them, test the code, and iterate on feedback—all within the same session.
In recent benchmark comparisons (shown in the visuals above), Qwen3-Coder achieved state-of-the-art results among open models across Agentic Coding, Agentic Browser Use, and Agentic Tool Use. On the SWE-bench Verified benchmark, which tests a model’s ability to fix real-world GitHub issues, Qwen3-Coder scored 69.6% with 500 turns and 67.0% without , outperforming other open models like Kimi-K2 and DeepSeek-V3, and even approaching the proprietary Claude Sonnet-4.
When you look at these scores, it’s clear that Qwen3-Coder isn’t just generating code—it’s reasoning, planning, and executing like an autonomous coding assistant.
How Qwen3-Coder Was Trained
1. Scaling the Pre-Training Process
Qwen3-Coder’s training was built around three scaling principles: tokens, context, and data quality .
Scaling Tokens : The model was trained on 7.5 trillion tokens , with about 70% of the data being code . This balance ensures that it excels at programming tasks while retaining strong general and mathematical reasoning skills.
Scaling Context : With its 256K context window (extendable to 1M via YaRN ), it’s optimized for repo-scale understanding . This allows it to read entire pull requests, large datasets, or even full project folders without truncating information.
Scaling Synthetic Data : Using its predecessor, Qwen2.5-Coder , the team cleaned and restructured noisy data to improve quality. That step gave Qwen3-Coder more reliable, diverse training samples and a stronger foundation for reasoning.
2. Post-Training with Reinforcement Learning
While pre-training builds the foundation, post-training is what makes Qwen3-Coder so effective in real-world scenarios. The team focused on two major forms of reinforcement learning:
a. Scaling Code RL: Hard to Solve, Easy to Verify
![coderl]()
Related Image: © Qwen.ai
Most code tasks have one key advantage—they can be verified through execution. Qwen’s team took this idea and scaled execution-driven reinforcement learning across diverse, real-world coding problems.
By running massive test suites and automatically generating new code challenges, they were able to fine-tune Qwen3-Coder for code correctness and execution success rates , not just syntactic accuracy. This approach helped the model perform better not only in coding tasks but also in logical reasoning and multi-step workflows.
b. Scaling Long-Horizon RL (Agent RL)
![swe]()
Related Image: © Qwen.ai
In real engineering environments, coding tasks rarely end in one turn. Developers need to plan, test, fix, and iterate. To replicate this, Qwen’s researchers introduced Agent RL, a long-horizon reinforcement learning approach that trains the model to engage in multi-turn, interactive workflows.
To make this possible, they built a scalable system capable of running 20,000 environments in parallel using Alibaba Cloud’s infrastructure. This gave Qwen3-Coder the ability to simulate thousands of developer interactions at once—testing hypotheses, integrating feedback, and improving dynamically.
Benchmark Performance: Where It Stands
The results speak for themselves. In the Agentic Coding category, Qwen3-Coder scored 37.5 on the Terminal-Bench and led most open-source models on SWE-Bench Live and Multilingual variants.
In Agentic Browser Use , it achieved 49.9 on WebArena and 55.8 on Mind2Web, showing how well it can navigate web-based environments to gather context or verify data.
And in Agentic Tool Use, its performance on TAU-Bench Retail and Airline tests (77.5 and 60.0, respectively) showed its ability to interact with APIs and tools like real agents.
When compared with proprietary models, Qwen3-Coder’s open-source advantage becomes even clearer—it performs at the level of Claude Sonnet-4 and surpasses GPT-4.1 in several benchmarks, all while remaining community-accessible.
Qwen Code: The Command-Line Companion
To make all this power usable, the team released Qwen Code, a command-line interface tool built for agentic coding. It’s forked from Gemini Code and customized with Qwen-specific prompts and function-calling protocols.
Qwen Code is a research-purpose CLI tool adapted from Gemini CLI, with an enhanced parser and tool support for Qwen-Coder models.
Make sure you have installed nodejs 20+:
You could install it via the following commands:
Then install the Qwen code via npm manager:
The other way is to install from the source:
Qwen Code supports the OpenAI SDK when calling LLMs, and you can export the following environment variables or simply put them under the .envfile .
export OPENAI_API_KEY="your_api_key_here"
export OPENAI_BASE_URL="https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
export OPENAI_MODEL="qwen3-coder-plus"
Integrations: Claude Code and Cline
Qwen3-Coder is also compatible with Claude Code, Anthropic’s coding assistant. Developers can use Qwen3-Coder as the backend model for Claude Code by setting Dashscope as the base API endpoint.
You can also configure it with Cline, a visual development tool that supports OpenAI-compatible APIs. This flexibility allows Qwen3-Coder to fit naturally into a developer’s existing workflow, whether they prefer terminal commands or visual editors.
npm install -g @anthropic-ai/claude-code
Using Qwen3-Coder via API
export ANTHROPIC_BASE_URL=https://dashscope-intl.aliyuncs.com/api/v2/apps/claude-code-proxy
export ANTHROPIC_AUTH_TOKEN=your-dashscope-apikey
Then you should be able to use Claude Code with Qwen3-Coder!
claude-code-config npm package for router customization
claude-code-router aims for customizing different backend models for Claude Code. The dashscope team also provide a convenient config npm extension, namely claude-code-config, that provides default configuration for claude-code-router with DashScope support.
Run installation:
and then run configuration:
The command will automatically generate the config json files and plugin directories for CCR. (You could also manually adjust these under ~/.claude-code-router/config.json and ~/.claude-code-router/plugins/ )
Start using Claude code via ccr:
Cline
Configure the Qwen3-Coder-480B-A35B-Instruct to cline
Go to the Cline configuration settings
For API Provider, select 'OpenAI Compatible'
For the OpenAI Compatible API Key, enter the key obtained from Dashscope
Check 'Use custom base URL' and enter: https://dashscope-intl.aliyuncs.com/compatible-mode/v1
Enter qwen3-coder-plus
For developers who like working directly with APIs, Qwen3-Coder is available through Alibaba Cloud Model Studio. You can call it using the openai Python client:
import os
from openai import OpenAI
# Create client - using intl URL for users outside of China
# If you are in mainland China, use the following URL:
# "https://dashscope.aliyuncs.com/compatible-mode/v1"
client = OpenAI(
api_key=os.getenv("DASHSCOPE_API_KEY"),
base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)
prompt = "Help me create a web page for an online bookstore."
# Send request to qwen3-coder-plus model
completion = client.chat.completions.create(
model="qwen3-coder-plus",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
],
)
# Print the response
print(completion.choices[0].message.content.strip())
This simple setup lets you integrate Qwen3-Coder into your own tools, automation pipelines, or web apps with ease.
Why Agentic Coding Matters
From a developer’s point of view, agentic coding isn’t just about having smarter AI—it’s about reducing the time spent on repetitive tasks. Whether it’s fixing bugs, writing boilerplate, or verifying test cases, an agentic model like Qwen3-Coder can take over the grunt work and free developers to focus on architecture, design, and creative problem-solving.
It’s exciting to see how models like this are shaping the next phase of developer productivity. Instead of replacing developers, they’re becoming co-engineers , capable of navigating entire projects, integrating feedback, and reasoning about code the same way a human might.
Looking Ahead
The Qwen team is continuing to improve Qwen3-Coder. More model sizes are on the way, aiming to balance power with deployment efficiency. The researchers are also exploring self-improving coding agents, which could autonomously learn from their own feedback loops—a fascinating next step in agentic AI.
As someone who has spent years working with containerization, DevOps, and automation, I see Qwen3-Coder as a glimpse into the future of software development. It brings AI closer to the developer’s workspace—inside the terminal, inside the IDE, and integrated with the tools we already use.
Conclusion
Qwen3-Coder represents a major leap forward in agentic AI. It combines large-scale data, advanced reinforcement learning, and practical tool integrations to deliver one of the strongest open coding models available today.
With its ability to plan, reason, and act, Qwen3-Coder isn’t just another model—it’s a coding companion ready to change how we build software in the real world.
If you haven’t tried it yet, now’s a good time to explore Qwen Code or the Model Studio API and experience what agentic coding feels like in practice. The next generation of intelligent coding has arrived—and it’s open to everyone.