Switching AI models by task can save real money
After testing different AI models over several weeks for coding, debugging, document analysis, and writing, it becomes clear that most tasks do not need the most expensive model. The biggest gain came not from finding one better model, but from being able to quickly switch between models depending on the task.
For example: complex debugging and discussions go to , general writing goes to GPT-5.5, and large code analysis goes to GLM-5.2. Having all these models available in one place saves a large amount of time compared to constantly switching between separate platforms.
Key points
- used for complex debugging and discussions
- GPT-5.5 used for general writing, GLM-5.2 for large code analysis
- The most expensive model isn't always necessary — task-based model choice matters
- Switching between models in one place saves significant time
- Discussion around using separate , direct APIs, or a router tool to manage multiple models