Comment by mudkipdev
19 hours ago
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?
19 hours ago
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?
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.
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.
The physical buildouts and massive R+D spending is the big part.
> This is entirely expected. The low prices of using LLMs early on was totally and completely unsustainable.
Do you think this is true for DeepSeek as well?
> The price for all models by all companies will continue to go up, and quickly.
This might entirely be true but I'm hoping that's because the frontier models are just actually more expensive to run as well.
Said another way, I would hope, the price of GPT-5.5 falls significantly in a year when GPT-5.8 is out.
Someone else on this post commented:
> For API usage, GPT-5.5 is 2x the price of GPT-5.4, ~4x the price of GPT-5.1, and ~10x the price of Kimi-2.6.
Having used Kimi-2.6, it can go on for hours spewing nonsense. I personally am happy to pay 10x the price of something that doesn't help me, for something else that does, in even half the time.
Look a cost per intelligence or cost per task instead of cost per token.
How do I reliably measure 1 unit of intelligence?
In pelicans, obviously
Isn't the outcome / solution for a given task non-deterministic? So can we reliably measure that?
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!)
Statistically. Do many trials and measure how often it succeeds/fails.
This is the only correct take. The only metric that matters is cost per desired outcome.
Repetition and statistics, if you have $1000++ you didn't need anyway.
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.
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.
Well, Google does release mini open versions of their models. https://deepmind.google/models/gemma/gemma-4/
And they're incredibly good for their size.
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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.
We know they cost much more than this for OpenAI. Assume prices will continue to climb until they are making money.
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.
source? There have also been a bunch of people here saying the opposite
When technology settles rent seeking beancounters will squeeze out of this the last drop of value they can. In theory it just needs to be cheaper than human doing the same job. In practice it will have to be cheaper than a human using open source model on his own hardware, that can do the job.
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.
GPT-4 cost 6x on input and 2x output tokens when it was released as compared go GPT-5.5
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.
I use glm and I like it, not they also increased the price to 18 usd /month.
I think Kimi and qwen are similar?
Wasn't there a limited offer? I wouldn't call that "increasing the prices"
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God I hope this is true.
Where can i find up to date resources on open source models for coding?
https://old.reddit.com/r/LocalLLaMA/
Bit of a hype madhouse whenever a new model is released, but it's pretty easy to filter out simple hype from people showing reproducible experiments, specific configs for llama.cpp, github links etc.
Apparently the cost/price is 20x in the major providers. Not clear how it is a business
Such an increase tracks the company's valuation trend, which they constantly, somehow have to justify (let alone break even on costs).