Qwen3-Coder: Agentic Coding in the World (via) It turns out that as I was typing up my notes on Qwen3-235B-A22B-Instruct-2507 the Qwen team were unleashing something much bigger:
Today, we’re announcing Qwen3-Coder, our most agentic code model to date. Qwen3-Coder is available in multiple sizes, but we’re excited to introduce its most powerful variant first: Qwen3-Coder-480B-A35B-Instruct — a 480B-parameter Mixture-of-Experts model with 35B active parameters which supports the context length of 256K tokens natively and 1M tokens with extrapolation methods, offering exceptional performance in both coding and agentic tasks.
This is another Apache 2.0 licensed open weights model, available as Qwen3-Coder-480B-A35B-Instruct and Qwen3-Coder-480B-A35B-Instruct-FP8 on Hugging Face.
I used qwen3-coder-480b-a35b-instruct on the Hyperbolic playground to run my "Generate an SVG of a pelican riding a bicycle" test prompt:
I actually slightly prefer the one I got from qwen3-235b-a22b-07-25.
It's also available as qwen3-coder on OpenRouter.
In addition to the new model, Qwen released their own take on an agentic terminal coding assistant called qwen-code, which they describe in their blog post as being "Forked from Gemini Code" (they mean gemini-cli) - which is Apache 2.0 so a fork is in keeping with the license.
They focused really hard on code performance for this release, including generating synthetic data tested using 20,000 parallel environments on Alibaba Cloud:
In the post-training phase of Qwen3-Coder, we introduced long-horizon RL (Agent RL) to encourage the model to solve real-world tasks through multi-turn interactions using tools. The key challenge of Agent RL lies in environment scaling. To address this, we built a scalable system capable of running 20,000 independent environments in parallel, leveraging Alibaba Cloud’s infrastructure. The infrastructure provides the necessary feedback for large-scale reinforcement learning and supports evaluation at scale. As a result, Qwen3-Coder achieves state-of-the-art performance among open-source models on SWE-Bench Verified without test-time scaling.
To further burnish their coding credentials, the announcement includes instructions for running their new model using both Claude Code and Cline using custom API base URLs that point to Qwen's own compatibility proxies.
Pricing for Qwen's own hosted models (through Alibaba Cloud) looks competitive. This is the first model I've seen that sets different prices for four different sizes of input:
This kind of pricing reflects how inference against longer inputs is more expensive to process. Gemini 2.5 Pro has two different prices for above or below 200,00 tokens.
Awni Hannun reports running a 4-bit quantized MLX version on a 512GB M3 Ultra Mac Studio at 24 tokens/second using 272GB of RAM, getting great results for "write a python script for a bouncing yellow ball within a square, make sure to handle collision detection properly. make the square slowly rotate. implement it in python. make sure ball stays within the square".