Qwen3 models introduce both Mixture-of-Experts (MoE) and dense architectures. They utilize hybrid thinking modes, allowing users to balance response speed and reasoning depth for tasks, controllable via parameters or tags. Developed through a multi-stage post-training pipeline, Qwen3 is trained on a significantly expanded dataset of approximately 36 trillion tokens across 119 languages. This enhances its multilingual support for global applications. The models also feature improved agentic capabilities, notably excelling in tool calling, which increases their utility for complex, interactive tasks.

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