[Chyzwar]

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.

[simonw]

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.

[alexriddle]

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.

[m_kos]

$300 is my employer's monthly cap on Claude Enterprise. It lasts me at most a week of moderate use. I would much rather get Codex Pro and Claude Pro or Max, which would cost ≤ $200. For $300, one could also add Gemini Ultra to the mix so I could have all three review each other's code, etc.

Claude can be very good but enterprise pricing doesn't make sense to me.

[lunar_mycroft]

The $200 plan you're talking about is subsidized by Anthropic. They cannot afford to keep offering that to everyone indefinitely. Absolute best case scenario for current users is that they can continue to subsidize it as way to sell enterprise plans, but there's no way that they can keep offering it to everyone at those prices.

[wahnfrieden]

They can if it is a way to get individuals hooked on it to then introduce it at their workplaces, who pay enterprise rates.

[lunar_mycroft]

Right, they can do it to sell enterprise plans, but they can't offer said plans to those enterprise customers indefinitely. So if your employer wants to spend $200/month on tokens, you're going to get however many tokens $200 buys you each month, not the order of magnitude more you can get with a consumer subscription.

[goosejuice]

> They cannot afford to keep offering that to everyone indefinitely.

Common talking point. There's enough evidence for the counter argument that this is essentially misinformation. I have no idea why it's so often repeated with confidence.

[cdata]

> There's enough evidence for the counter argument that this is essentially misinformation.

> No evidence is shared

Help an open-minded critic out.

[ilikehurdles]

That’s a shocking number. I don’t know how much my employer is billed, but based on the numbers reported by Claude code in its optional status bar, I’m often exceeding $300 in a day across sessions, when working on meatier tickets.

[stavros]

We deployed OpenWebUI with the Claude API the other day for employees. Someone sent ten messages (which appeared to just be reasonable day-to-day work), and we paid $200 for it. There were 44M input tokens, 100k output tokens, no cache hits at all. OpenWebUI reports 3M tokens used, Claude reports 44M, and I have no idea where the rest of the tokens went. This was all on a brand new API key, installed directly to the service, too.

With this kind of opaque billing, how can I reasonably deploy any AI?

[SyneRyder]

No cache hits seems ominous, could this be an OpenWebUI issue? It also seems ominous that Anthropic models are basically nowhere on the OpenWebUI leaderboards.

I'm only doing a cursory search, but it seems OpenWebUI doesn't support Anthropic caching, and they don't intend to? Other providers handle caching automatically (apparently?) but caching has to be specifically managed by the client with Anthropic. If that's correct that OpenWebUI doesn't support it, it would really send your costs spiralling, because you're being billed for all the tokens in the entire multi-turn conversation on every turn:

https://github.com/open-webui/open-webui/issues/4887

I have no experience with OpenWebUI though (honestly, first time I've heard of it). Just trying to be helpful. If I'm completely incorrect then apologies in advance for sending you down the wrong path.

[stavros]

Really? Huh, I've never heard of Anthropic caching needing to be specifically enabled. I'll look into that, thank you! Sounds like the culprit.

[stymaar]

I hope your company is keeping the input/response pair in case they need to break free at some point.

[dd8601fn]

Wouldn’t people mostly just want any artifacts?

[speed_spread]

Like Slack history, LLM history can be used to build searchable knowledge base. Questions are often more valuable than answers.

[jgreid]

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.

[zaphirplane]

What is the governance and audit trail on offer ?

[zackify]

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.

[htrp]

> 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).

Can large enterprises just not use the API ? I have audit logs and what seem to be enterprise features through my anthropic account (platform.claude.ai)

[simonw]

They can do that, but I expect they see individual user accounts and enterprise account management and easy rollout of Claude.ai/Cowork/Claude Code/etc as worth an extra $20/month/person.

[thewebguyd]

The devs can, sure. The "enterprise plan" is more for that + giving Claude to all the non-technical employees for access to the chatbot + Cowork. Plus SSO and all that jazz.

[acdha]

Enterprises can, but then they have to show their auditors that this has been done in a way which is robust and can’t be bypassed, and they have to build the kind of reports people need to be convinced of that — nothing is ever “just” in enterprise IT.

Longer term, you also have to be careful about building things around details which could change at any time. OpenAI and Anthropic have a ton of pressure to start banking huge profits and they very closely monitor customer activity. A time-honored strategy in this space is to shuffle the features enterprise customers depend on but which aren’t deal-breakers for most other customers into expensive enterprise plans. There’s possibly some counter pressure from companies like Google which have healthier finances but I wouldn’t count on that since they also have MBAs who’d be all too happy to invent pretexts to hike their prices to match.

[opsnooperfax]

Your CISO is paying to not be responsible. That’s it. That’s always the reason.

[pyreko]

Yep, where I work I know people easily spending over a few thousand dollars a month.

[datadrivenangel]

I've heard that the $20/seat gets waved if you have large enough committed spend.

[isoprophlex]

Would they even care at that scale, if the average employee spends $3000 every month because mgmt mandates slopmaxxing?

[chinathrow]

Do we know that they all pay the API rates or will they negotiate individually?

[stymaar]

> 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).

[gpugreg]

> 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]

> 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 bigger than Chinese flagship. Otherwise it really doesn't bode well.)

[striking]

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.

[fluidcruft]

Musk has a lot of incentive to explain away how horrible Grok is relative to Opus.

It's certainly a better sell that Grok sucks because it's small and Opus is impressive because it's large, than the alternative that Grok is also large and sucks which points to xAI incompetence and mismanagement.

Particularly when you're trying to IPO a rocket company based on rosy forecasted valuations of Grok dominating the market.

[UltraSane]

Elon Musk has absolutely no credibility anymore. I'm more likely to believe the opposite of what he claims to be true.

[kakacik]

aka the russian strategy

[orphea]

  Elon Musk tweeted

Come on. The Onion would be a more credible source.

[Chyzwar]

https://arxiv.org/abs/2604.24827

From this paper

[stymaar]

That's not what the paper says though:

    Claude Opus 4.6 Anthropic 68.0% ∼5.3T [1.8–15.6T]
    Claude Opus 4.7 Anthropic 66.4% ∼4.0T [1.4–12.0T]
    Claude Opus 4.5 Anthropic 65.2% ∼3.4T [1.1–10.0T]
    Claude Opus 4.1 Anthropic 64.9% ∼3.2T [1.1–9.5T]
    Claude Opus 4 Anthropic 59.7% ∼1.4T [478B–4.2T

According to their estimation, Opus is likely between 1T and 15T, which really doesn't tell you much that you couldn't have guessed otherwise. It doesn't say “Opus is a 5T model”.

The fact that there's absolutely no consistency in the predicted size between models from the same lab should tell you all you need about the predictive power of this method (and they aren't really lying about their numbers, their confidence interval is huge enough to fit anything in it, but their prose is making very strong claims out of their statistical nothingburger).

(somebody already posted this paper earlier, and I spent some time reading it, and this paper is really not that good even though there are a bunch of interesting ideas in it).

[layer8]

> What's your source for Opus being a 5T model?

Probably Elon Musk: https://eu.36kr.com/en/p/3760679047267075

[UltraSane]

I don't know why stymaar's comment is flagged and dead, he is 100% correct.

[xbmcuser]

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.

[jonfromsf]

Yes, for indie developers and small startups. Large corps won't want their code /email/etc data being looked at by the Chinese government.

[LUmBULtERA]

For Deepseek and other openweight models, you can use non-Chinese hosted infrastructure that offer zero data retention and still save a whole lot of money. A large corp could even host their own Deepseek v4.0 Flash model internally for some basic work.

[runtime_terror]

> 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.

[Npovview]

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).

[runtime_terror]

So kinda like how stuff is now at a lot of big companies? I've worked at many different companies and almost always there are a few out-performers and a lot of people just found enough not to get fired (no hate, power to them lol).

We're already seeing slash their AI budgets. I expect that will increase till we hit more of an equilibrium.

[Npovview]

I think people will start measuring (features * time taken to implement) to tokens consumed ratio and then redistribute token budgets to developers. This will measure how effective/efficient people are LLMs.

[curt15]

> Even with increased prices, AI enables velocity both in development and bugs fixing.

What about human understanding of the codebase that's essential to any project's long term health? Even "superperformer developers" eventually leave the company.

[Npovview]

Ask multiple AIs (if you can't trust one) to explain the project.

[0xbadcafebee]

Most software development teams are pushing back on the deluge of bad changes from AI tools and are moving slower again to regain trust and stability. It is likely that future software development will not actually be higher velocity.

[Npovview]

Bad changes will be eliminated because better people are using the AI tools. They will reduce cost as well as slop.

[kakacik]

Yes and we will all hold hands together singing and bring world peace from now on forever. Back in reality, I can see some obstacles without even trying

[Npovview]

Do you want 60% of people to be employed in farming? I am being rhetorical because that is what you sarcasm implies. Today only 2% of people in farming support so many people in America.

[0xbadcafebee]

The models are the same whether you're smart or dumb

[DougN7]

From what I understand, that is sort of how IBM Bob works - multiple models behind the scenes and they route the request to the model that will handle it best at the lowest price.

[otabdeveloper4]

> AI enables velocity both in development and bugs fixing

Or so they say. You'll have to trust those vibes blindly, because double-checking these claims apparently makes you an anti-science luddite.

[alfiedotwtf]

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

[overfeed]

> I’m not sure why a Y-Combinator backed company hasn’t come out yet flooding the market with highly capable OPAI

If we momentarily disregard the fact that YC itself owns billions of dollars worth of OpenAI shares[1], YC would plan to find demo-day investors willing to drive down the value of frontier labs. The coöpetition among VCs and the existing web of AI investments will mean no VC will be interested in investing in local AI...until after the frontier labs IPO.

1. Thanks to the self-dea^w foresight of former YC president Sam Altman

[nicoburns]

> 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.

That or just hiring people to do the work! I hear rumours that this is already starting to happen in some places (perhaps those that were a little overzealous with AI-hype driven layoffs).

[alfiedotwtf]

Fool me once… I think applying for jobs at a company that only within the last 12 months shed thousands of people “because of AI” should be seen as laughable, and employees collectively rejecting to work there should be seen as the norm

[runtime_terror]

I do think many will move to lower cost models or self hosted over the next few years as prices balloon. And the privacy/control story is compelling.

If we're able to see some big increases in hardware capabilities that can be self-hosted, that will be an accelerant.

That said, most companies just want to pay a provider to delegate responsibility in exchange for cost and control.

[anthonypasq]

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.

[runtime_terror]

How will it be profitable, really?

You can dismiss Ed (and me vicariously) but what's your compelling evidence to counter their extremely uphill battle towards profitability?

Either way it will be very interesting to see their S1 when they try and IPO.

If it's anything like SpaceX's then I suspect my post will age better than yours.

[brookst]

I sincerely doubt Anthropic’s IPO will say that their AI business is only 2% of their future revenue, and they’re bundling in totally unrelated, unprofitable things they expect to account for 98%.

[runtime_terror]

I'm not sure what you're talking about or referring to...

I haven't heard anyone claim their S1 will show that but that it will show how poorly their revenue figures look against their costs.

[brookst]

Space'x IPO docs say that launch + Starlink will be 2% of their revenue opportunity, with enterprise AI being 98%.

[8note]

the alternative is that api prices change to be more in line with deepseek's

[runtime_terror]

So then how do these companies return profits to investors?

[tomaskafka]

Here is the fun part: they don’t

[lelanthran]

> 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?

[wongarsu]

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]

> 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]

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.

[runtime_terror]

But explain to me how these companies will recoup these costs outside of increasing inference pricing?

Their business model is selling inference but the training and other costs have to be accounted for somehow. Unless I'm missing something obvious, inference costs must go up drastically if these companies are going to survive beyond the subsidy stage.

[wongarsu]

Sell more. The hope is that there is a huge addressable market that includes huge per-worker demand in almost all white collar work and lots of inference in people's private lives

If that doesn't work, then yes, then prices will have to go up

[HDThoreaun]

Obviously they need more paying users. The entire game in tech is taking advantage of (comparatively)low marginal costs to pay off capex once you corner the market

[Forgeties79]

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]

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]

https://www.wsj.com/tech/ai/mind-blowing-growth-is-about-to-...

[dminik]

Does Anthropic really expect to double their income without also doubling their expenses?

[wongarsu]

There we go back to the original question: are subscriptions profitable, API pricing wildly profitable, and they just lose all that money on fixed costs like model training; or do they actually barely make money on inference?

That's why talking about the profitability of inference without accounting for model training is interesting, because that is the deciding factor in whether more customers would help getting them in the green

[Forgeties79]

I’ve been hearing that anthropic is on the verge of profitability for probably a year straight. Until all the companies agree to stop the training arms race I just don’t see how it’s in the cards

[i2km]

This is one of the things people miss. If they double their customers, of course they double their expenses. Unlike SW, the marginal cost here is still high

[Forgeties79]

Let’s see it first. And without omitting training/infrastructure costs at that. Until then my comment is still accurate.

[anthonypasq]

its a private company, what exactly do you expect to 'see'?

[z2]

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.

[stingraycharles]

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.

[bachmeier]

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.

[ajb]

SanDisk has designed a flash equivalent to HBM, which has 1.6TB/s of bandwidth. I expect that it will be available initially to server manufacturers only, but once supply ramps up will be built into individual machines. At that point it will be practical to run local inference on much larger models. Of course, maybe the SOTA providers will find some way to use even larger ones, but it seems like the returns to scale aren't as much as they were.

[devmor]

> it never will be.

Giving strong “640k is enough for anyone” vibes here.

[3form]

640k statement was absolute, this one is comparative.

Cloud should have more compute and efficiency than local. I wouldn't be 100% sure, as I don't know what I might not be seeing, but still.

Whether that comparative advantage will matter, though, is a completely different question.

[devmor]

Gotcha, I think I misunderstood the statement as saying today’s cloud-required will never be local-capable.

[adrian_b]

Depends on what you mean by "economically feasible".

Even very cheap mini-PCs and laptops can run any of the models run by cloud providers, albeit at a much lower speed (i.e. with the weights stored on SSDs).

Whether such a low speed is useful, depends on the application. For something like a coding assistant or bug scanning, an instant response is desirable, but certainly not necessary.

[christina97]

The SSD would wear out in days while the laptop generates two responses a day. This is like saying you could power your home with AA batteries, yes technically you could but in practice entirely infeasible.

[adrian_b]

There is no wear on the SSDs, because the weights are just read, they are not written during inference.

For model training, the requirements are very different, and the training of a big LLM cannot be done with home equipment. On the other hand, inference can be done on almost any PC, even for LLMs with thousands of billions of parameters, just very slowly.

The only problem is that the inference becomes limited by the SSD reading throughput. Most of the cheap new personal computers available today can read simultaneously only 2 SSDs (if there are more they share a reading path), which are typically 1 PCIe 5.0 SSD and 1 PCIe 4.0 SSD. This has an upper throughput limit of 24 Gbyte/s, with 15 to 20 GB/s achievable in practice.

Then the speed in token/s is limited by the amount of weights that must be read per inference cycle. The ratio between output tokens and the amount of weights that must be read can be improved by various methods, like batching multiple tasks or using speculative decoding.

[jurgenburgen]

Does more RAM increase performance? This approach sounds like it could eventually be fast enough for local use as hardware and models improve.

[zozbot234]

Faster SSD access improves performance more than RAM does, at least until all of the model is being cached in RAM. So older and cheaper HEDT platforms with lots of PCIe lanes to attach storage to are best for this approach.

[jyounker]

Weights are write-once data.

[zozbot234]

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.

[colonCapitalDee]

"roughly" is doing a lot of heavy lifting there

[adrian_b]

The difference between datacenter hardware and cheap personal hardware is not in what can be run and what cannot be run.

Anything can also be run on a cheap computer.

The difference is in speed. A cheap computer may run a big model up to a few orders of magnitude slower than datacenter hardware, depending on whether the LLM is small enough to fit in GPU memory, or it is small enough to fit in CPU memory or it is so big that it must spill on SSDs.

Depending on the application, the tradeoff between run time and run cost may happen to favor using local hardware, despite a much slower speed.

There are plenty of applications where doing them for negligible cost during an overnight job can be preferable to obtaining faster results at a very high price, for instance scanning for bugs in a mature code base using a great number of different open-weights LLMs, which can achieve similar bug coverage like using a single, but overpriced and unavailable SOTA LLM, e.g. Mythos.

[stingraycharles]

> The difference between datacenter hardware and cheap personal hardware is not in what can be run and what cannot be run.

You do realize that a model like Opus is (estimated to be) around 5T parameters, and uses around 5TB of GPU memory?

These kind of things are just impossible to run locally.

[adrian_b]

This kind of things can certainly be run locally, even on a small mini-PC, like a NUC, or even on a laptop, with the weights stored on SSDs.

Like I have said, the problem is not that they cannot be run, but that they may run more slowly than it is acceptable for a given application. Depending on the model, the speeds reported for inference with weights stored on SSDs vary from one token every few seconds to at most a few tokens per second.

Computers could solve relatively huge problems even in the early days of vacuum tube computers, when the main memories were measured in kilobytes, because at that time it was not expected that the data needed for problem solving must fit inside the main memory or even in the next tier of memory, with magnetic drums or magnetic disks, but the really big problems were solved by a great number of passes over data stored on magnetic tapes.

An LLM whose inference could not be run on a small mini-PC would have to be one hundred times bigger than the biggest existing SOTA LLMs.

Any LLM that exists today can be run on almost any PC, just extremely slowly in comparison with datacenter hardware.

[cortesoft]

NEVER will be is a pretty big leap. Never is a long time.

[cyanydeez]

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...

[otabdeveloper4]

The subscription plans are the "first hit is free" plans. They're not gonna last and don't build anything serious based on them.

[protocolture]

>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.

These are loss leaders that will not be maintained over the long term. Already we see moves to restrict their usage and redirect people back to API pricing.

[kelseyfrog]

> 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."

[danielmarkbruce]

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 .

[jyounker]

Prompting is just writing specification documents. A lot of people are very bad at this. I suppose that more to the point, a lot of people are just bad at writing.

[danielmarkbruce]

This is probably correct. Perhaps prompting just brings out the very worst in specification.

[latexr]

According to Google, “there’s no wrong way to prompt”.

https://www.youtube.com/watch?v=9bBfYX8X5aU&t=48s

[knollimar]

No wrong way to [consume thing I sell that you'll consume more of if you do it poorly]

[djeastm]

Ehhh, their incentive in their marketing is to get normal people to not be intimidated by the big bad AI.

Power users are always going to have to take the messaging companies send out to the masses with a grain of salt.

[FireCrack]

IDK if it's just me, but I also find Claude, whether it be the model or the harness, is a lot more "forgiving" of poor prompts than many of the open models

[try-working]

DeepSeek and Xiaomi are so cheap there's no need to get a plan. Just use the API.

[jason_s]

something something something China something something intellectual property something something....

[noman-land]

You can just say the words instead of implying their meaning and letting everyone fill in the gaps themselves.

[onesingleblast]

So when you need an LLM in your backend, scrape Claude Code instead of using the API :)