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.

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rafaelmn 2 hours ago | parent | next [-]

> AI revenue has been going up while the cost per token has been rapidly falling

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 ?

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jstummbillig 40 minutes ago | parent | next [-]

1. GPT 4 has gotten 6x cheaper over it's evolution (from initial release to Turbo to 4o). Maybe you meant "Only since 4o and only since its final release". Alas.

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 that regard. It is (according to the benchmarks) maybe more comparable to current 5 nano - which is 98% cheaper.

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dktp an hour ago | parent | prev | next [-]

Opus 4.5 became significantly cheaper directly per token

	▲	rafaelmn an hour ago \| parent [-]
		You are right I forgot about that ! I think my point still stands - price per token is not decreasing for frontier capabilities, in fact it's increasing.

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baq 2 hours ago | parent | prev [-]

token efficiency

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chillfox an hour ago | parent | next [-]

Not seeing that either, tried really using Opus 4.7 today, and it ended up at $50 for the same kida thing that came out to $25 last week with Opus 4.6.

	▲	baq an hour ago \| parent [-]
		each model is different and nothing should be taken for granted, run your evals for your use cases. I'm not using Opus 4.7 for almost anything. I've seen very good improvements in GPTs since 5.2 and Opus 4.5 to 4.6 was quite an upgrade.

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wesammikhail 39 minutes ago | parent | prev [-]

Models consume more tokens than ever for the same tasks.

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lompad 29 minutes ago | parent | prev [-]

This is conjecture. There is a reason both openai and anthropic refuse to comment on inference costs. If it were falling so much, they would use it to brag. I really don't understand why so many people keep repeating it without any actual data for the frontier models.

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?