| ▲ | Chyzwar 3 hours ago |
| When discussing LLM pricing, people are missing the plot. The subscription token price is 10x-40x cheaper than API pricing. Your 90$ Claude subscriptions give you close to $1000 to $4000 in equivalent API token pricing. The second issue is that the quality of the model “operator” makes a massive difference in the outcomes. Highly skilled senior devs who know how to prompt and have high agency will outperform team people that lack motivation and foundational skills. Lastly, there is a massive difference in capabilities, determinism, and error handling between 5T SOTA models like Opus and tiny distillations from DeepSeek that perform well only in benchmarks. |
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| ▲ | simonw 2 hours ago | parent | next [-] |
| I learned today that the Anthropic "Enterprise" plan - the one big companies use because they need governance features and audit logs and all of that jazz - is billed at API token rates (plus $20/seat/month). So large companies are getting billed a lot more than those discount subscription plans. |
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| ▲ | zackify 10 minutes ago | parent | next [-] | | We are on it at my job. It saves money due to other parts of the org not using as many tokens. The real cost effective way is giving a team $20 cursor $20-100 Claude $20-200 codex. I'm spending 1k on Claude enterprise easily and that's with trying to spread it on codex and cursor using pi. | |
| ▲ | alexriddle 2 hours ago | parent | prev | next [-] | | Anything over 150 seats means you need to pay at token rates plus the $20/user. My day job is operational (no coding at all) and I'm spending ~$300 a month on a few chats with Claude/Cowork a day over the course of a month. | | |
| ▲ | stymaar 2 hours ago | parent [-] | | I hope your company is keeping the input/response pair in case they need to break free at some point. |
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| ▲ | jgreid an hour ago | parent | prev | next [-] | | Governance and audit trail are incredibly valuable to large enterprise organizations, especially those working in regulated spaces. Companies will pay a premium if the security/privacy/compliance issues are handled effectively. | |
| ▲ | datadrivenangel 2 hours ago | parent | prev [-] | | I've heard that the $20/seat gets waved if you have large enough committed spend. | | |
| ▲ | isoprophlex an hour ago | parent [-] | | Would they even care at that scale, if the average employee spends $3000 every month because mgmt mandates slopmaxxing? |
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| ▲ | stymaar 2 hours ago | parent | prev | next [-] |
| > Lastly, there is a massive difference in capabilities, determinism, and error handling between 5T SOTA models like Opus What's your source for Opus being a 5T model? > and tiny distillations from DeepSeek that perform well only in benchmarks. I don't think you know what you're talking about. Local models aren't “distillations from Deepseek”. And they don't perform well “only in benchmarks”, Qwen 3.6 is a very decent model (obviously it's not Opus, but it's also much faster and speed is a quality of its own). |
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| ▲ | layer8 2 hours ago | parent | next [-] | | > What's your source for Opus being a 5T model? Probably Elon Musk: https://eu.36kr.com/en/p/3760679047267075 | | |
| ▲ | stymaar 2 hours ago | parent [-] | | Sigh, it's year 2026 and there are still people believing something Musk… |
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| ▲ | gpugreg 2 hours ago | parent | prev [-] | | > What's your source for Opus being a 5T model? Elon Musk tweeted that Grok is 0.5T or 1/10th the size of Opus. https://xcancel.com/elonmusk/status/2042123561666855235#m While this source's reliability is certainly debatable, the size matches the results of this paper, in which researchers estimated the parameter count from model knowledge. https://01.me/research/ikp/ | | |
| ▲ | stymaar an hour ago | parent [-] | | > While this source's reliability is certainly debatable Massive understatement. Nowadays it has become hard to find a single Musk statement that doesn't contain at least one lie. > the size matches the results of this paper, in which researchers estimated the parameter count from model knowledge. https://01.me/research/ikp/ Thanks for the pointer. This estimation has Grok 6 times bigger than Musk claims it is, so maybe that's where the lie is. (I'm quite skeptical about that number though, it would be quite disappointing for the US tech if their flagship models had to be that much larger than the Chinese ones for such a small edge in performance. Because I don't think US labs are incompetent, I'd bet that US flagships aren't more than 2/3 times faster than Chinese flagship. Otherwise it really doesn't bode well.) | | |
| ▲ | striking an hour ago | parent [-] | | In tiny gray text right above the table is written "90% PI ≈ ±3.00× either side." Is GPT-5.5-Pro 3.4T or 30.8T in size, or somewhere in between? We just don't know. |
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| ▲ | lelanthran 3 hours ago | parent | prev | next [-] |
| > When discussing LLM pricing, people are missing the plot. [ ... snipped ...] Your 90$ Claude subscriptions give you close to $1000 to $4000 in equivalent API token pricing. And you think it is unreasonable to consider this unsustainable? |
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| ▲ | z2 2 hours ago | parent | next [-] | | And the direction is definitely towards removing that subsidy really soon. We can see it with OpenAI's shift to API-equivalent pricing for enterprise customers last month. Anecdotally my company saw OpenAI credit usage grow 2x with stable use across the ChatGPT platform, which is pretty terrifying considering just 2% of the company uses Codex. For context, ChatGPT business subscriptions give you a fixed pool of credits to use, after which you get billed a la carte at inflated 1.75x rates vs API, or if you don't want to pay, you get access to anything but the non-reasoning models turned off for the month. We also tried Claude Enterprise, which was unusable as people blew through their monthly limits in a matter of hours. | |
| ▲ | wongarsu 2 hours ago | parent | prev [-] | | Depends on what their actual costs are. Either they are losing lots of money on subscriptions, or they make absolute bank on API pricing. Looking at the pricing of 1-2T models like Kimi or DeepSeek on the open market, I'm tempted to assume that inference costs are closer to subscription pricing than to API pricing. Especially considering that subscriptions a) distribute load over time via rate limits, and b) will include a lot of users who get only a fraction of the possible value, whether they are on a personal account where they are on the rate limit on the weekend but barely use it during the week, or are corporate users who were issued an account they rarely use. Subscription prices are usually measured on the average case, not the most extreme value a power user can get out of it | | |
| ▲ | runtime_terror an hour ago | parent | next [-] | | > I'm tempted to assume that inference costs are closer to subscription pricing than to API pricing So just going on vibes? While some people don't like his content, Ed Zitron shows a lot of evidence for your assumption being very wrong. These companies are bleeding cash at ungodly rates. It's likely their API pricing is still subsidized if you look at their overall financial picture. Related, there's a good reason those API prices keep going up a lot every new version and it's not just because the models are better. | | |
| ▲ | wongarsu an hour ago | parent [-] | | Selling inference for more than inference costs is not incompatible with bleeding cash at ungodly rates. They do in fact pay ungodly amounts of cash for other things, like training, marketing, etc. Heck, you can bleed cash while being profitable (in the accounting sense) Also, API prices going up a lot every new version is more an OpenAI thing, and even there it's a recent trend: GPT 5.0 was a big price drop compared to 4.1, and 4.1 was cheaper than 4o, which itself got a price cut at some point and is cheaper than 4. Meanwhile Anthropic's API pricing stayed stable for many versions, then got slashed to a third with the 4.2 release and have stayed at that level since. |
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| ▲ | Forgeties79 2 hours ago | parent | prev [-] | | Considering not one company is in the black yet I don’t really know how we can say anyone is making bank, unless we want to count absurd levels of VC funding (now slowing down) I guess. | | |
| ▲ | wongarsu 2 hours ago | parent | next [-] | | I am conveniently not counting training costs (since they add no marginal costs, selling more tokens doesn't impact them), and hardware and DC costs only amortized Of course they do have to "make bank" in some way to offset the insane training costs. But whether they go for high prices or high volume, or offer some services as a loss leader to drive profits elsewhere is somewhat orthogonal to that | |
| ▲ | anthonypasq an hour ago | parent | prev [-] | | https://www.wsj.com/tech/ai/mind-blowing-growth-is-about-to-... | | |
| ▲ | Forgeties79 an hour ago | parent [-] | | Let’s see it first. And without omitting training/infrastructure costs at that. Until then my comment is still accurate. | | |
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| ▲ | xbmcuser an hour ago | parent | prev | next [-] |
| Its not like the non frontier are not improving. If someone can use deepseek to get 90% of the work done for $100 then pay another $100 to anthropic or openai to complete it I think they will rather do that than pay anthropic or openai for $1000. |
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| ▲ | runtime_terror an hour ago | parent | prev | next [-] |
| > The subscription token price is 10x-40x cheaper than API pricing This is a temporary phenomenon. Expect either drastic price increases or draconian throttling or both in the coming months. These companies are operating at huge loses and have hundreds of billions in liabilities and commitments. They need to turn on the money faucet sooner than later. |
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| ▲ | Npovview an hour ago | parent | next [-] | | Even with increased prices, AI enables velocity both in development and bugs fixing. Would companies want that? If prices are biting the company, I think companies will route all development and bugs fixing requests through few superperfomer developers with complete knowledge of the different components within the company (they will be the Queen Bees holding the company on their head). The rest of the company will be tasked with requirment gathering, specs cleaning, deambiguation and so on (worker bees). | |
| ▲ | alfiedotwtf an hour ago | parent | prev | next [-] | | Incentives matter… If prices keep going up, watch for companies to exit frontier models and go to local llama.cpp instances for 6-month-ago SOTA, with the flex of being housed within the office - no more privacy leakage, no more price gouging. To be honest, I’m not sure why a Y-Combinator backed company hasn’t come out yet flooding the market with highly capable OPAI (pronounced “Oh-pah” as in what Greeks shout as the drink shots), which stands for “On-Prem AI” … yes, I just made up OPAI right now lol | |
| ▲ | anthonypasq an hour ago | parent | prev [-] | | Theres recent reporting that Anthropic will be profitable this quarter... edit: I see in other comments on this thread you think Ed Zitron is a reliable pundit so that explains everything. |
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| ▲ | stingraycharles 2 hours ago | parent | prev | next [-] |
| Also, your local hardware is in no way capable of running the types of models that the cloud providers do, it’s just not economically feasible, and it never will be. |
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| ▲ | cortesoft 11 minutes ago | parent | next [-] | | NEVER will be is a pretty big leap. Never is a long time. | |
| ▲ | bachmeier an hour ago | parent | prev | next [-] | | Very much dependent on the situation. For many business tasks, local hardware is good enough. But what a lot of folks overlook when saying these things is that (a) workers do more than run AI models on a piece of hardware, (b) significant computer hardware is already sitting idle outside normal work hours, when it can be running batch jobs, and (c) employees can share local hardware. | |
| ▲ | zozbot234 2 hours ago | parent | prev | next [-] | | It can run open-weight models that are roughly as capable. It's going to be slow unless you're using actual datacenter hardware, but they'll run. | | | |
| ▲ | devmor an hour ago | parent | prev [-] | | > it never will be. Giving strong “640k is enough for anyone” vibes here. |
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| ▲ | cyanydeez 2 hours ago | parent | prev | next [-] |
| Isn't the plot that it's like an infinite bikeshed but 10% of the biksheds are actually trailer parks and when you finally realize it's a trailer park and not a bike shed you're down 10-100$ because it's token gen is faster than you can actually validate? Some might say the price wouldn't be great if you could actually process and validate it... |
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| ▲ | kelseyfrog 2 hours ago | parent | prev [-] |
| > The quality of the model “operator” makes a massive difference in the outcomes. My hunch is that this is the source of much of the variability in outcomes upstream of HN commenters claiming extremes of, "This model changes everything!" to "This[same] model is crap." We haven't operationalized what it means to "be good at prompting," nor developed proxies/heuristics/shibboleths for accessing prompting skill. There's community skepticism over whether prompting skill even exists. Besides even if prompting skill is real, who wants to hear, "Actually you kinda suck at prompting." |
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| ▲ | danielmarkbruce an hour ago | parent [-] | | It's 100% this. Many people suck at prompting. It's likely that habits from search are ingrained. But in general some people are just so bad at it . |
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