Just to point this out: many of these frontier models cost isn't that far away from two orders of magnitude more than what DeepSeek charges. It doesn't compare the same, no, but with coaxing I find it to be a pretty capable competent coding model & capable of answering a lot of general queries pretty satisfactorily (but if it's a short session, why economize?). $0.28/m in, $0.42/m out. Opus 4.5 is $5/$25 (17x/60x).

I've been playing around with other models recently (Kimi, GPT Codex, Qwen, others) to try to better appreciate the difference. I knew there was a big price difference, but watching myself feeding dollars into the machine rather than nickles has also founded in me quite the reverse appreciation too.

I only assume "if you're not getting charged, you are the product" has to be somewhat in play here. But when working on open source code, I don't mind.

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happyopossum 5 days ago | parent | next [-]

Two orders of magnitude would imply that these models cost $28/m in and $42/m out. Nothing is even close to that.

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jauntywundrkind 5 days ago | parent | next [-]

To me as an engineer, 60x for output (which is most of the cost I see, AFAICT) is not that significantly different from 100x.

I tried to be quite clear with showing my work here. I agree that 17x is much closer to a single order of magnitude than two. But 60x is, to me, a bulk enough of the way to 100x that yeah I don't feel bad saying it's nearly two orders (it's 1.78 orders of magnitude). To me, your complaint feels rigid & ungenerous.

My post is showing to me as -1, but I standby it right now. Arguing over the technicalities here (is 1.78 close enough to 2 orders to count) feels besides the point to me: DeepSeek is vastly more affordable than nearly everything else, putting even Gemini 3 Flash here to shame. And I don't think people are aware of that.

I guess for my own reference, since I didn't do it the first time: at $0.50/$3.00 / M-i/o, Gemini 3 Flash here is 1.8x & 7.1x (1e1.86) more expensive than DeepSeek.

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minraws 5 days ago | parent | prev [-]

Gpt 5.2 pro is well beyond that iirc

	▲	jauntywundrkind 4 days ago \| parent [-]
		Whoa! I had no idea. $21/$168. That's 75x / 400x (1e1.875/1e2.6). https://platform.openai.com/docs/pricing

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KoolKat23 5 days ago | parent | prev [-]

I struggle to see the incentive to do this, I have similar thoughts for locally run models. It's only use case I can imagine is small jobs at scale perhaps something like auto complete integrated into your deployed application, or for extreme privacy, honouring NDA's etc.

Otherwise, if it's a short prompt or answer, SOTA (state of the art) model will be cheap anyway and id it's a long prompt/answer, it's way more likely to be wrong and a lot more time/human cost is spent on "checking/debugging" any issue or hallucination, so again SOTA is better.

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lukan 5 days ago | parent [-]

"or for extreme privacy"

Or for any privacy/IP protection at all? There is zero privacy, when using cloud based LLM models.

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Workaccount2 5 days ago | parent [-]

Really only if you are paranoid. It's incredibly unlikely that the labs are lying about not training on your data for the API plans that offer it. Breaking trust with outright lies would be catastrophic to any lab right now. Enterprise demands privacy, and the labs will be happy to accommodate (for the extra cost, of course).

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mistercheph 4 days ago | parent [-]

No, it's incredibly unlikely that they aren't training on user data. It's billions of dollars worth of high quality tokens and preference that the frontier labs have access to, you think they would give that up for their reputation in the eyes of the enterprise market? LMAO. Every single frontier model is trained on torrented books, music, and movies.

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user34283 4 days ago | parent [-]

Considering that they will make a lot of money with enterprise, yes, that's exactly what I think.

What I don't think is that I can take seriously someone's opinion on enterprise service's privacy after they write "LMAO" in capslock in their post.

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lukan 4 days ago | parent [-]

I just know many people here complained about the very unclear way, google for example communicates what they use for training data and what plan to choose to opt out of everything, or if you (as a normal buisness) even can opt out. Given the whole volatile nature of this thing, I can imagine an easy "oops, we messed up" from google if it turns out they were in fact using allmost everything for training.

Second thing to consider is the whole geopolitical situation. I know companies in europe are really reluctant to give US companies access to their internal data.

	▲	KoolKat23 3 days ago \| parent [-]
		To be fair, we all know googles terms are ambiguous as hell. It would not be a big surprise nor an outright lie if they did use it. Its different if they proclaimed outright they won't use it and then do. Not that any of this is right, it wouldn't be a true betrayal. On a related note, these terms to me are a great example of success for EU GDPR regulations, and regulations on corporates in general. It's clear as day, additional protections are afforded to EU residents in these terms purely due to the law.