Alibaba unveils Qwen3-Coder-Next AI model for coding agents and local development tasks

Alibaba has launched Qwen3-Coder-Next, an open-weight language model tailored for coding agents and local development tasks. The release aims to address the needs of developers seeking high-performance, locally operable AI solutions for code synthesis and automation workflows.

Qwen3-Coder-Next draws on the design of Qwen3-Next-80B-A3B-Base, embracing a hybrid attention mechanism combined with a mixture-of-experts (MoE) architecture. Agentic training at scale leverages executable task synthesis, environment interaction, and reinforcement learning, which combine to yield capable coding and agentic performance at reduced inference costs.

Rather than advancing primarily through parameter scaling, the model's training process relied on expanding agentic training signals. Alibaba used comprehensive sets of verifiable coding tasks executed in real-time environments, allowing Qwen3-Coder-Next to learn directly from environment-based feedback. This method aimed to teach the model skills such as long-horizon reasoning, effective tool usage, and the ability to recover from execution failures, all seen as essential for deployment in production-grade coding agents.

Regarding benchmarks, Qwen3-Coder-Next delivers positive results for speed and practical reasoning. It performs at a competitive level against some larger open-source models, but there is still opportunity for improvement in future iterations.