This is 3x the price of GPT-5.1, released just 6 months ago. Is no one else alarmed by the trend? What happens when the cheaper models are deprecated/removed over time?

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Night_Thastus 6 hours ago | parent | next [-]

This is entirely expected. The low prices of using LLMs early on was totally and completely unsustainable. The companies providing such services were (and still are) burning money by the truckload.

The hope is to get a big userbase who eventually become dependent on it for their workflow, then crank up the price until it finally becomes profitable.

The price for all models by all companies will continue to go up, and quickly.

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oezi 4 hours ago | parent [-]

I recently looked at this a bit but came away with the impression that at least on API pricing the models should be very profitable considering primarily the electricity cost.

Subscriptions and free plans are the thing that can easily burn money.

	▲	Night_Thastus 2 hours ago \| parent [-]
		The physical buildouts and massive R+D spending is the big part.

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Schlagbohrer 4 hours ago | parent | prev | next [-]

As others have mentioned you're ignoring the long tail of open-weights models which can be self hosted. As long as that quasi-open-source competition keeps up the pace, it will put a cap on how expensive the frontier models can get before people have to switch to self-hosting.

That's a big if, though. I wish Meta were still releasing top of the line, expensively produced open-weights models. Or if Anthropic, Google, or X would release an open mini version.

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Wowfunhappy 3 hours ago | parent [-]

Well, Google does release mini open versions of their models. https://deepmind.google/models/gemma/gemma-4/

	▲	deaux 3 hours ago \| parent [-]
		And they're incredibly good for their size.

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energy123 7 hours ago | parent | prev | next [-]

Look a cost per intelligence or cost per task instead of cost per token.

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yokoprime 6 hours ago | parent | next [-]

How do I reliably measure 1 unit of intelligence?

	▲	wellthisisgreat 5 hours ago \| parent [-]
		In pelicans, obviously

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ulimn 6 hours ago | parent | prev [-]

Isn't the outcome / solution for a given task non-deterministic? So can we reliably measure that?

	▲	foota 6 hours ago \| parent \| next [-]
		Yes, sort of. Generally you can measure the pass rate on a benchmark given a fixed compute budget. A sufficiently smart model can hit a high pass rate with fewer tokens/compute. Check out the cost efficiency on https://artificialanalysis.ai/ (say this posted here the other day, pretty neat charts!)
	▲	torginus 6 hours ago \| parent \| prev \| next [-]
		This is the only correct take. The only metric that matters is cost per desired outcome.
	▲	genericresponse 6 hours ago \| parent \| prev \| next [-]
		Statistically. Do many trials and measure how often it succeeds/fails.
	▲	dns_snek 6 hours ago \| parent \| prev \| next [-]
		Repetition and statistics, if you have $1000++ you didn't need anyway.
	▲	throwuxiytayq 6 hours ago \| parent \| prev [-]
		It's much easier to measure a language model's intelligence than a human's because you can take as many samples as you want without affecting its knowledge. And we do measure human intelligence.

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operatingthetan 7 hours ago | parent | prev | next [-]

We know they cost much more than this for OpenAI. Assume prices will continue to climb until they are making money.

	▲	horiap 3 hours ago \| parent \| next [-]
		How do we know that? There is a large gap between API pricing for SOTA models and similarly sized OSS models hosted by 3rd party providers. Sure, they’re distilled and should be cheaper to run but at the same time, these hosting providers do turn a margin on these given it’s their core business, unless they do it out of the kindness of their heart. So it’s hard for me to imagine these providers are losing money on API pricing.
	▲	beering 4 hours ago \| parent \| prev [-]
		source? There have also been a bunch of people here saying the opposite

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dannyw 6 hours ago | parent | prev | next [-]

It's far more meaningful to look at the actual cost to successfully something. The token efficiency of GPT-5.5 is real; as well as it just being far better for work.

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dandaka 7 hours ago | parent | prev | next [-]

SOTA models get distilled to open source weights in ~6 months. So paying premium for bleeding edge performance sounds like a fair compensation for enormous capex.

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kuatroka 5 hours ago | parent | prev | next [-]

Not really a big problem. Switch to KIMI, Qwen, GLM. You’ll get 95% quality of GPT or Anthropic for a 10th of a price. I feel like the real dependency is more mental, more of a habit but if you actually dip your toes outside OpenAI, Anthropic, Gemini from time to time, you realise that the actual difference in code is not huge if prompted in a good way. Maybe you’ll have to tell it to do something twice and it won’t be a one shot, but it’s really not an issue at all.

	▲	nubg 3 hours ago \| parent [-]
		God I hope this is true. Where can i find up to date resources on open source models for coding?

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typs 3 hours ago | parent | prev | next [-]

GPT-4 cost 6x on input and 2x output tokens when it was released as compared go GPT-5.5

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msdz 7 hours ago | parent | prev [-]

Such an increase tracks the company's valuation trend, which they constantly, somehow have to justify (let alone break even on costs).