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simianwords

GPT-4 (original API):

Input: $30 / 1M tokens

Output: $60 / 1M tokens

GPT-5.5:

Input: $5 / 1M tokens

Output: $30 / 1M tokens

Costs have been reducing by over 5x year over year. Inference cost concern is mostly performative.

https://simianwords.bearblog.dev/conclusive-proofs-that-llm-...

Edit: can't reply but companies aren't selling inference at loss. In the blog post I point to third party hosting of open models like Deepseek which are also going down. They are not VC backed.

I also point to Gemma 31B which you can run on your laptop today that beats most models from 2024.

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What they charge people says nothing about what it costs them. Off the top of my head, one confounding factor is trying to win back marketshare from Anthropic.

We will only know the actually situation once Anthropic goes public and we can look at their books.

I think it's pretty safe to assume they are not losing money on inference.

Based on what? They haven't even IPOed.

I think it’s safe to assume that they are bleeding cash.

It's silicon valley and they are trying to aggressively grow. Your baseline assumption should be the exact opposite.

"Neither Mr. Edison nor anyone else can override the well-known laws of Nature, and when he is made to say that the same wire which brings you light will also bring you power and heat, there is no difficulty in seeing that more is promised than can possibly be performed. To talk about cooking food by heat derived from electricity is absurd."

Good quote. Doesn't apply well to this situation tho.

Wait, this person knew that the wire could bring you light, but not that it could bring you heat? Hadn't they noticed that light bulbs heat up?

It could be a reasonable argument from the point of view of scale: you need a lot more energy for cooking than for lighting (even with incandescent lightbulbs, though they were a fair bit dimmer and colder in the earlier days of them).

Sure, but then that's just scale, not the laws of nature.

The price a company charges, _particularly_ a high growth VC-backed one, is a poor signal for their costs.

That blog post is not very compelling either. Without knowing details of the architecture, comparing the various frontier models to open models doesn’t make sense.

> That blog post is not very compelling either. Without knowing details of the architecture, comparing the various frontier models to open models doesn’t make sense.

Why do you need to know the architecture? Just compare Deepseek V4's performance with GPT 4 and treat internals as a blackbox. Deepseek is much cheaper and way more performant. If you can agree to reasonable assumptions

1. that closed source models are more efficient than open source

2. Deepseek is served at a profit and not a loss

Then it is pretty clear that the prices have gone down. If the prices have gone down more than 20x-30x then surely it is not _still_ subsidised is it?

I think this amount of skepticism is not warranted here. Every reasonable explanation or proxy is met with "but you don't know what they really do" is naive.

It is borderline conspiratorial to believe it this way.

I don’t find it at all reasonable that closed source models are more efficient. The people involved had different circumstances and it naturally affects their work

> 1. that closed source models are more efficient than open source

Not a reasonable assumption for a variety of reasons.

> 2. Deepseek is served at a profit and not a loss

Not a reasonable assumption either.

> Why do you need to know the architecture? Just compare Deepseek V4's performance with GPT 4 and treat internals as a blackbox.

Because the internals are what actually matter and what drives inference cost.

It would be entirely reasonable to expect that GPT-5.5 has some sort of optimizations or changes to the architecture to make it easier to train, or to make runtime ablation easier, or to better handle large batches, or whatever.

Those changes, particularly if they are non-public, can easily result in worse inference performance than a comparably sized model without those changes.

> It is borderline conspiratorial to believe it this way.

It's not any sort of conspiracy. It's how land-grab tech companies have always worked. To presume otherwise is silly.

That's pricing.

Pricing has no correlation with profit. It can be artificially lowered to kill competition, and artificially inflated to maximize profit.

It definitely correlates with profit. It doesn't correlate with cost, at least when you have VC money to burn.

If you go to https://developers.openai.com/api/docs/pricing, you will see the actual prices, which do not match what you posted:

GPT-4.1 Input: $2.00 / 1M Tokens Output: $8.00 / 1M Tokens

The parent comment is correct. They are talking about GPT-4, which was really expensive by today's standard. After GPT4o came out, GPT-4 was completely forgotten.

Yeah, even back then, ~nobody was using GPT-4 because it was released as some weird Sam Altman flex. Super expensive, not that capable.

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