What you're looking at there is the cost/performance in inference. The "frontier".

An economically successful step would be to improve training cost/frontier advance.

They essentially failed at that, assuming they kept their training costs for the model step at least constant.

There's a lot of research along this particular line, if you're interested!

This is a dataset basically constructed by taking a bunch of DeepSeek R1 trajectories and filtering out the incredibly long rambling ones: https://huggingface.co/datasets/open-thoughts/OpenThoughts-1...

There's also a bunch of RL research around strategies to coax the student model towards more concise trajectories.

In general, a lot of people in industry are finding cost savings these days by tuning a smaller model over longer agent trajectories (not exactly the same as distilling the reasoning traces, since those aren't exposed by many frontier labs anymore) collected from their production agents. Typically works best when you have a pretty workflow-y agent whose tasks all share a similar shape.

They do this already. Have you noticed the stronger model tends to get released first and the weaker models soon after?

The reasoning/thinking is basically trying many many different branches.

To do that you need a larger model to have many many branches to try.

Diminishing returns would catch you there as well.

Perhaps, it's a bit like being a super smart person in contemporary society: at some point, being smarter doesn't really reliably translate into a better life.

When you learn from available human data, those text books max out at some point. You cannot learn what isn't there.

Not everyone is writing CRUD business apps and personal use software. Pretty much any model can do those now. The frontier is for doing things those models struggle with. Even in vibe coding a simple video game the difference in results between an open source model, the previous frontier model, and the current model is pretty huge.

Security is mandatory now in a way that it wasn't, now that AI has eliminated the skill barrier to hacking.

People looking at frontier models release are not the average person, and let's say that you pay $150 with Fable to save $1000, it's much more interesting than using $10 with a cheap model to save even $500.

Scale numbers to whatever suits you.

Well for tasks that can be done with another model you just use another model.

Seriously though, does every model have to be affordable? Can't there be a model analogue of a rolex/ferrari?

At this point, they are beginning to approach the Tesla Roadster case. The flying one.-

Those are both true, but I don't see how they are meant to rebut what I said.

Most people on plan use do NOT use up all their plan usage.

Users on API plans can still use Opus 5.5 which is a great model. If they want top of the line performance, they can pay more.

When another company puts out a model that undercuts Fable 5.2 on price at similar performance, Anthropic will put out Opus 6 and undercut them right back. That's how the wheel turns. This is just part of the cycle.

Does that mean bigger companies can only get API pricing?