Routing tasks across models can cut cost and improve results
There may be no single best AI model for every kind of work. Low-cost models such as Flash V4 can handle fast jobs, basic code , and one-off scripts without much cost pressure. glm-5.1 is used for more of the real building work, especially backend tasks, and its generous limits help during .
Its drawback is that it can spend too much effort on debugging, which can slow things down. Opus 4.6 is better suited for hard problems, such as reasoning across several connected files or fixing a issue that has been stuck for a while. Kimi 2.6 fits quick questions because it is fast and does not get stuck repeating itself on simple tasks.
The tradeoff is extra setup: several must be tracked, and context does not automatically move between models, so the right model has to be chosen before the work starts.
Key points
- Using different models for different tasks can work better than searching for one perfect model.
- Flash V4 is useful for fast, simple coding tasks because the cost is low.
- glm-5.1 handles backend work and well, but may overdo debugging.
- Opus 4.6 is reserved for harder reasoning and serious problems.
- Multiple models create extra work around , context, and setup.