Willingness to pay. If people are willing to use Astra instead of Sol even if it costs twice as much, OpenAI has no need to make it cheaper. Prices only reflect costs in a competitive market where customers actively seek out cheaper alternatives.
Willingness to pay is definitely the primary driver, but you aren't just paying for raw compute, you are paying for queue priority and uptime reliability under heavy concurrent load.
costs are in a large part tied to model parameter count, eg. a small model fits on one GPU, the biggest models require an entire rack
the primary difference you see through those prices is model size
that you don't seem much capability difference is why Big Ai is pushing for regulatory capture, because small models are nearly as good for many tasks at a fraction of the cost, undermining the business model they have used to justify the most expensive infra build out in human history
Also I think there are diminishing returns to larger models and the harness can make up for some weaknesses. Like maybe the huge model can hypothetically answer a question off the cuff but the small model can get some search results and think about those and give you an answer.
I am big believer (based on personal experience) that there is a ton of ROI in harness engineering, to go along with the context engineering, invariably intertwined
It's not always larger models. Often time it's a smaller model fit for a specific use case.
In the future we will see 16B models that are for : Legal, Healthcare, Science, Software Engineering, Education, Forestry, Music production
I created a model for music production that doesn't require AI at all. It is algebraic by nature. This means determinism & creativity are first class citizens.
Willingness to pay. If people are willing to use Astra instead of Sol even if it costs twice as much, OpenAI has no need to make it cheaper. Prices only reflect costs in a competitive market where customers actively seek out cheaper alternatives.
Willingness to pay is definitely the primary driver, but you aren't just paying for raw compute, you are paying for queue priority and uptime reliability under heavy concurrent load.
There still intrinsic loss to the model.
costs are in a large part tied to model parameter count, eg. a small model fits on one GPU, the biggest models require an entire rack
the primary difference you see through those prices is model size
that you don't seem much capability difference is why Big Ai is pushing for regulatory capture, because small models are nearly as good for many tasks at a fraction of the cost, undermining the business model they have used to justify the most expensive infra build out in human history
Also I think there are diminishing returns to larger models and the harness can make up for some weaknesses. Like maybe the huge model can hypothetically answer a question off the cuff but the small model can get some search results and think about those and give you an answer.
100%
I am big believer (based on personal experience) that there is a ton of ROI in harness engineering, to go along with the context engineering, invariably intertwined
It's not always larger models. Often time it's a smaller model fit for a specific use case. In the future we will see 16B models that are for : Legal, Healthcare, Science, Software Engineering, Education, Forestry, Music production
I created a model for music production that doesn't require AI at all. It is algebraic by nature. This means determinism & creativity are first class citizens.
https://monictheory.com/developer-api