Qwen3 Core Mechanisms of the Hybrid Thinking Model
Qwen3 innovatively introduces two complementary modes of thinking:Thinking Modecap (a poem)Non-Thinking Mode. In thinking mode, the model will show the complete reasoning chain (e.g., step-by-step disassembly, intermediate conclusions, etc.), and finally give a systematic answer, which is suitable for complex scenarios that require in-depth analysis (e.g., mathematical proofs, code debugging). The non-thinking mode skips the intermediate steps and outputs the final result directly, which is especially suitable for simple Q&A scenarios that require high response speed.
The efficiency gains of this design are reflected in three dimensions:
- Computing resource optimization: Users can dynamically switch modes according to the complexity of the task, avoiding simple tasks from consuming additional computing resources.
- Budgetary control: The system enables accurate inference cost management through visual monitoring of token consumption.
- Enhanced human-machine collaboration: developers have both quick access to simple answers and the ability to understand the modeling decision-making process through thought patterns
For the technical implementation, the team ensured seamless integration of the two models through a four-phase post-training process (incorporating long thought chain fine-tuning and inference reinforcement learning), which allowed the model to demonstrate deep inference while maintaining responsiveness.
This answer comes from the articleQwen3 Released: A New Generation of Big Language Models for Thinking Deeply and Responding FastThe
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