AI systems may increasingly route tasks across models instead of defaulting to the largest one

Model selection is described as a multidimensional optimization problem: systems must weigh price, speed, context knowledge, working style and specialization, not only a model’s raw logical ability. Combining models and routing each use case to the appropriate option can outperform relying on any single model.

The assessment is that this optimization will persist and may become more important as models improve. For many practical tasks, intelligence is no longer the main bottleneck, making cheaper and faster models more viable; newer, stronger models also do not necessarily produce an immediate change in ChatGPT retention metrics.