I agree that there may be a lot of variation between models that leads to different use cases, at least today. But I’m not sure the car analogy works.

An X5 is not simply “inferior” to a CR-V, or vice versa. A Camry is not “inferior” to an F-150, or vice versa. They are optimized for different buyers, budgets, constraints, and use cases.

That may actually be the better analogy for AI models: there probably is not one universal “best” model. There are models that are better or worse for particular tasks, price points, latency requirements, deployment constraints, privacy needs, etc.