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).

K/V cache compression and context shortening / summarisation. And yes, I suspected Quants too.

Actually, ELO rankings done blinded on models do vary: https://the-frontier.app, that said, your point looks accurate as far as 5.3 - 5.5 on this chart, 40 to 50 point ELO gain.

I find I have to argue with 5.5 less than 5.3, and I therefore use it when I could reach for 5.3, but I don't think it's a major difference.

Exactly this. And it's not really possible to do repeatable trials, it's all just vibes. People have very little awareness of their own cognitive biases.

That's a pretty shallow dismissal, and I bet you $100 I can tell you which model I'm talking to between 4.6 and 4.8 without looking or asking after a handful of messages.

Anthropic famously had a terrible outage back when 4.6 was the latest and greatest, and it was never the same after it came back.

All evidence suggests they simply don't have the compute to keep serving their best models at their most powerful.

You will be amused to hear that when Anthropic "refreshed" 4.6 on AWS Bedrock I found it in my tests and wrote about it – and they actually rolled it back. This is how much non–coding tests may tell you about the model.