Qwen3.5 Inference Mode Smart Switching: Innovative Practice of Enabling Deep Thinking on Demand

With the launch of Alibaba Tongyi Qianwen Qwen3.5 series models, balancing inference quality and response speed has become an important issue for developers. A recent innovative project in the open-source community implements dynamic switching of inference modes through a lightweight proxy layer, allowing users to flexibly choose the depth of thinking based on task complexity—retaining the deep inference capabilities required for complex tasks while reducing computational costs and response time for simple tasks.

The Qwen3.5 series models have enhanced inference performance, especially the 27B version which excels in tasks like mathematical reasoning, code generation, and logical analysis—thanks to in-depth learning of chain-of-thought data during training. However, enabling the full inference mode increases token consumption and response time, which is unnecessarily excessive for simple tasks like Q&A and text summarization.

The project inserts a control layer between user requests and model inference via a lightweight proxy layer, parsing the inference preference settings in the request and adjusting model parameters. When an inference-enabled instruction is detected, it guides the model to generate a detailed response including the thinking process; when fast mode is selected, it directly outputs the final answer. The design is backward-compatible—existing applications integrated with Qwen3.5 do not need to modify their business logic, only adding simple control parameters.

Interactive chat can offer "Quick Reply" and "Deep Thinking" modes for users to choose from; automated workflows select modes automatically based on task type (e.g., inference mode for code review, fast mode for code completion); enterprise-level deployments control API call costs through intelligent switching while ensuring the quality of critical tasks.

This project embodies the "small but beautiful" characteristic of open-source innovation, precisely addressing practical pain points. It improves the peripheral tools of the Qwen ecosystem, lowering the threshold for model usage to attract more developers. Its on-demand enabling concept may influence future model API designs, promoting native support for fine-grained capability control.

In the future, it can integrate a task classifier to automatically judge content complexity; support progressive inference (first fast response, then upgrade to deep inference when confidence is insufficient); expand to multi-modal scenarios (control the depth of visual understanding and the degree of reflection in tool calls). It is recommended that developers integrate this open-source tool to improve the cost-performance of their applications in different scenarios.