I suppose it is both. Basically all frontier models are inference-time compute bound thanks to reasoning. And actual reasoning traces are locked behind closed doors at all American labs. So whenever they want to push a new model and need to give it hardware, it would make sense to cut into the reasoning budgets of older models. Users will not be able to see that directly, it will only become apparent on high-end, difficult tasks - exactly the kind of tasks where the provider wants you to use the new model anyway, so they can further improve it.

The economics of AI fall apart if you stay with the old model forever. No need to buy new GPUs or build new data centers.

Can you think of many examples of a SaaS provider who regularly keeps old versions of a product around for customers to use?

A far more common scenario is that new versions are rolled out to everyone, without offering a choice, as soon as they're considered stable.

Older versions consume resources and require staff to spend time on operating and supporting them. Those resources could be used to run a newer version.

The tl;dr is the simple economics of any SaaS product.

If you want to be able to run old versions indefinitely and control the resources assigned to it, you need to self-host (an open model).