AI revenue has been going up while the cost per token has been rapidly falling. The Jevons paradox applies here. The cheaper software is, the more software is written. There is not a finite demand for software.
Every model release now has been straight price increases since what GPT 4 ? When was the last time a new flagship model decreased prices compared to the previous one ?
2. We are not interested in how different model naming schemes relate to prices, we are interested in the capabilities. So if you want to learn something about price development you need comparative levels of capabilities, and then look at the prices. 4o is not comparable to 5.5 in the first regard. It is (according to the benchmarks) maybe more comparable to current 5 nano - which is 98% cheaper.
- AI revenue going up & cost/token are not related metrics, at least not in the way you are assuming - basically all players (except OAI for the moment) struggling with capacity and/or reducing-dismissing subscription based solutions in favour of pay-per-use. If token cost/token was falling, we would see quite the opposite.
Apart from that, I'm not sure if focusing on tokens is even a good idea, because they are so different from model to model. I'd almost consider them a red herring now.
We could look at tasks instead. Is there anything even remotely suggesting that your typical task you give an LLM now costs less in inference than before?