8 ms·

The current AI pricing was always going to go away

82 points by arnon 5mo ago

vdelpuerto 5mo ago
[dead]
dtagames 5mo ago
Some of these coming price increases will move dev work back to dedicated shops and teams when individuals and non-devs won't want to pay the AI bill to finish and ship their projects. An outside small dev shop or internal dev team can pay these prices and spread the cost over several customers or departments, but the era of giving everyone AI and telling them to dev stuff is about to be over.
throwa356262 5mo ago
This is only true if your world is limited to openai, antropic and alike. There are a whole bunch of companies somewhere else in the world that are getting better and cheaper every month, hardware side included. all without the infinite VC money
fallpeak 5mo ago
This is slightly more tasteful slop than average (I'm thinking probably Claude rather than ChatGPT?), but it's still 100% AI written: https://www.pangram.com/history/c55ab69b-e0a9-49a0-8056-2fcd7d305463 https://www.pangram.com/history/c55ab69b-e0a9-49a0-8056-2fcd...
- 0x3f 5mo ago
  This... is not a reliable AI detection method at all.
  - extr 5mo ago
    Pangram is highly reliable.
    - jemmyw 5mo ago
      It can't be that good, I gave it some posts I wrote before llms were a thing and it gave them all 60%+ of the content as written by AI.
      - extr 5mo ago
        ok thank you for this anecdote
        jemmyw 5mo ago
        Not really an anecdote, strictly, just a single point of evidence.
  - fallpeak 5mo ago
    You are incorrect. There, now we've both made unsupported assertions. Care to provide any evidence for your position? For what it's worth, when I provide a Pangram link it's because I can already tell something is AI and I'm attempting to provide objective third-party confirmation so the conversation doesn't just degrade into me asserting that I have superior taste to you.
- arnon 5mo ago
  sorry, but i wrote this myself
_fat_santa 5mo ago
I wonder how much of Uber blowing their AI budget and MSFT pulling their claude code licenses can be attributed to "tokenmaxxing". When Meta announced token leaderboards and other followed, I could see this being the logical conclusion. That whole trend is so dumb because it leads to this. Company announces they will measure developer performance by how many tokens they burn and constantly talks about how the best developers burn the most tokens. Developers see the message and start burning tokens. And then the company acts surprised when their bills go through the roof. I personally use my OpenAI subscription pretty heavily, 2-3 agents running practically all day on various tasks but I never even get close to running into limits while I hear about others blowing through limits on multiple accounts in the same time period. I'm convinced that most of those folks and their elaborate workflows aren't really for productivity but for bragging rights about how much they use AI.
- cayleyh 5mo ago
  > I personally use my OpenAI subscription pretty heavily, 2-3 agents running practically all day on various tasks but I never even get close to running into limits Same. But if I was working for an organization that measured token usage, you can bet I would be doing things like creating a cron job that uses claude to create a customized bespoke report update of the current status of all my open assigned tickets and message that to myself 4 times a day... token burn for zero purpose whatsoever.
- bdcravens 5mo ago
  The same here, where I haven't come close to hitting any of my CC limits. Even though I'm more productive than I've ever been (as measured by finished, valuable tasks running in production) and I'm clearing out months of backlog, I have either one of two conclusions when I hear about others who suggest they need more: 1. I'm doing it wrong. Apparently I'm supposed to give it a vague paragraph about what the business does, and I can run off and sip margaritas and wake up to a fully fleshed business 2. They don't know what they're doing, and they're sending the LLM off on a wild goose chase that it does a reasonable job of working it's way out of, so they consider it success despite the waste.
- ai_fry_ur_brain 5mo ago
adamesque 5mo ago
It's hard to take this piece seriously if he's citing _Ed Zitron's_ math, and equally hard to make the blanket statement that flat-rate plans = "the current AI pricing". But yes, those pricing models were pretty silly and unsustainable.
- kimixa 5mo ago
  Get back to me when there's an AI company that's actually profitable and we can compare their service and pricing. Claiming that there's some small subset of their services (like inference per token) that's "profitable" doesn't mean anything when it relies on everything else that company is still paying for. If you could make money from it at current prices - why aren't they? Otherwise it's just "how much they're willing to subsidize".
  - dragontamer 5mo ago
    There is probably going to be a quarter or two of profits when the prices dramatically increase. Vibe coding techbros are hooked on the Iron Lung and may not want to get off. At my work are multiple developers bragging about overnight AI usage to solve problems hands off. Yes they are wasting money and resources but the fad is here. People be vibe coding for now. In like 6 months when all the costs need to be paid and the prices go up, we will see if these companies stay profitable. But I'm of the opinion that the vibe coding tech bros are more than enough to sustain a short or even medium term profit for these companies. Just on fad-energy alone (see OpenClaw) The fad probably collapses soon after. I hope anyway, the waste I see is nauseating. ------------ I dunno where this is all going. But I do have faith in human ingenuity still. Things are changing, possibly for the worse, but we need to make the best of it. The worst of behaviors is wasteful and blatant fraud. There's something useful here though.
  - energy123 5mo ago
    The problem with fixating on earnings as you're doing is that it's a bad metric for a growth company. COGS is much more important. What you're doing is setting it up so every growth company is terrible until they've matured into a 20 year old company. That's obviously dumb.
PiRho3141 5mo ago
This is where open source models are important. The latest deepseek v4 pro model is 2-5x cheaper than Claude Sonnet 4.6. Cursor's Compose 2.5 that was just recently released is 6x cheaper than Sonnet. The state of the art models are going to get better and more expensive and smaller models are going to get cheaper. There will be a point where the intelligence of both the cheap and state of the art models are indistinguishable by humans like it is indistinguishable for me to understand the difference the difference between Terrance Tao and my university math professor. I don't always need the smartest and most expensive models. I will need it every once in awhile and will gladly pay that price if I had to. What I do need is the model that will solve the current problem I have in a reasonable amount of time.
- squidbeak 5mo ago
  Deepseek V4 Flash is far cheaper still, and a better model to compare to Sonnet 4.6. I'm finding it a reliable workhorse.
  - anonzzzies 5mo ago
    Yep, people who never used it say it is not good.
- greenmilk 5mo ago
  > The state of the art models are going to get better and more expensive and smaller models are going to get cheaper. Why do you think this will be true? Right now I see the major US labs betting on gaining an advantage from having way more compute, and I see Chinese labs competing with one another in a resource-scarce environment, so they place much more emphasis on compute-efficiency. But the supply chains that feed into the massive data center growth in the US are strained; there are energy, memory, and logistical bottlenecks to name a few. In the medium-long run, compute capacity will not grow exponentially forever. Somehow it has for decades, but there can be no infinite exponential growth, and that point may be when the planet really starts to cook itself. Maybe the US labs will become more compute-constrained, and then have to compete on efficiency. Or maybe things change fundamentally in some other way I'm not thinking of.
Havoc 5mo ago
Inference costs absolutely did fall. And even more so when looking at intelligence it buys you. eg compare say gpt 3.5 to latest deepseek. Both cheaper and more at more capable
- arnon 5mo ago
  but are you using less of them because they're cheaper, or are you using more of the more advanced models and running them harder?
abtinf 5mo ago
Insofar as I can tell, inference is on a certain path toward becoming "free". The models are now extremely powerful on high-end consumer hardware, and the efficiency trend seems likely to continue. Here is a recent non-rigorous benchmark I ran against a bunch of models. Qwen3.6 35B A3B fine-tuned with opus data runs plenty fast on my local machine and produce outstanding results - easily in the top 5, comparable to GPT 5.5 Pro (which is $180/mtok). https://gistpreview.github.io/?31d66ef69e4aed3efae1aec69d86c298/ https://gistpreview.github.io/?31d66ef69e4aed3efae1aec69d86c... I've predicted for years now that the industry will head down the path of the virus scanning vendors: selling subscriptions to be able to download the latest versions of models. I simply don't see how any other business model is remotely viable, except at the very highest end of inference or video gen.
- anonzzzies 5mo ago
  That local hardware is not consumer though but prosumer. Consumer is a 500$ laptop running that and that is not currently the case.
extr 5mo ago
What is the OP talking about. $/unit intelligence is going down rapidly. You can achieve what would have been considered miracles in 2022 with < $10.
- bdcravens 5mo ago
  Absolutely, though I think the expectations are being set by those who have watched too many "OpenClaw business on autopilot" videos.
- deleted 5mo ago
  [deleted]
infecto 5mo ago
Has this not been true for a long time now? Most companies have had enterprise/business level prices that was highly connected to usage for a what feels like at least a year.
YetAnotherNick 5mo ago
You are comparing two different model. It's like saying roadster is more expensive than model S. No model pricing actually increased, and I am using GPT-4o in the same price as it was before. You can see price vs performance in artificial analysis and the the pareto optimal is all just 6 months old model.
vitalysemenov 5mo ago
[flagged]
plaidfuji 5mo ago
kind of sobering to realize that whether your job can be profitably automated away comes down to what $/token some hyperscale AI provider can deliver… I suppose it’s nice that this article highlights some upward pressure on that number.
pacman1337 5mo ago
I get similar results for deepseek and opus but opus is way faster. I guess deepseek streams thinking and makes it slower?
alligatorplum 5mo ago
I seldom use my PC anymore ever since i got a laptop. with the cost per token increasing along with the random "features" where models will just eat through your tokens in one hour. I really have been tempted to turn my PC into a server to run local models on there
alfiedotwtf 5mo ago
> Memory for 4x expensive > Did we collectively forget second order thinking? I bought 2x 16Gb NVIDIA cards this week because I don’t see hardware getting cheaper anytime soon, and because of that I totally don’t see the point of “waiting until prices go lower for graphics cards” because that might not for a long time yet! In fact, if you include factoring in world events (and the ones that haven’t happened yet but eventually will e.g. China’s 2027 long planned take of Taiwan), then there’s no way graphics prices are going to be accessible to mere mortals until at least 2028. But my real reasoning is that you’re going to see a flood of OpenAI and Anthropic users leave because of a) increasing pricing plans, and b) impeding business laws on the horizon about protecting sovereign data from AI (i.e data in cloud for training is a no no). So what happens when people and companies one by one start leaving the SOTA AI cloud for from-good-enough-to-wow models? RAM and graphics cards become the new toilet paper, which is going to double again current prices. Upgrade now before it’s too late folks!
energy123 5mo ago
Capex and revenue should not be compared like this, unless revenue is small and not growing.
anthonypasq 5mo ago
Guys, we are the in the mainframe era of AI. People in the 60's thought computing was expensive too and the idea of having a computer on every desk, nevermind every pocket, nevermind every single piece of electronics in the world basically seemed like a complete pipe dream. if you told someone in the 70's their toaster would have a supercomputer it in, they would think you were crazy. in 10 years your doorknob is going to have a local AI model it in. This is computing 2.0 not the dot com bubble. 90% of inference will be at the edge in the future and there will still be super-computers and giant clusters doing cutting edge science and research, but for 90% of use cases youll just need a tiny local model, same reason you dont need a giant GPU in your smart tv.
- stephc_int13 5mo ago
  The main issue with this reasoning is that the hardware substrate for AI and good old computing is the same. All governed by Moore's Law, what happened then seems extremely unlikely to happen again, the curve is a sigmoid and we're much closer to the flat end now.
  - anthonypasq 5mo ago
    I think this has some truth to it I would say, but im pretty confident that small models will continue to get better and runnable on dedicated consumer hardware
tekacs 5mo ago
> Anthropic’s CFO testified under oath this March that the company spent $10 billion on compute and made $5 billion in revenue (Ed Zitron has the math). The labs are underwater on inference. They’re raising prices to keep the lights on. 'The labs are underwater on inference' is an absurd thing to say whilst not separating the cost of _compute_ out into training and inference.
- dismalaf 5mo ago
  I mean, I guess they could just stop training new models and coast, but they ARE training models so you have to include those costs.
- JimDabell 5mo ago
  According to Dario Amodei, Anthropic are even profitable when including inference as long as you look at it on a per-model basis; it’s just that every model is more expensive to train than the last one. For instance, if you have already spent $n to train a model and are currently earning $2n selling inference with it; but are concurrently spending $3n training the next model in anticipation of earning $6n with it, then you are already in the hole for $n and are currently also losing $n – but you are doubling your money with each model because your $n investment in the first model returns $2n and your $3n investment in the second model returns $6n. Also: > Ed Zitron has the math Ed Zitron is constantly wrong about AI economics: https://www.theargumentmag.com/p/ais-biggest-critic-has-lost-the-plot https://www.theargumentmag.com/p/ais-biggest-critic-has-lost...
  - otabdeveloper4 5mo ago
    > According to Dario Amodei That's a big ask. No thanks.
- saltcured 5mo ago
  How is training vs inference any different than other product spaces, where all the costs of bringing a product to market have to be considered for profitability? You can't just look at marginal production cost. You are still underwater if the other development costs are not being recouped by the final sales revenue. The whole commercial AI enterprise is not economically viable if the inference revenue will not cover both inference and the amortized training costs. Given how fast they are churning through models to compete, you cannot act like the training is an asymptotically low cost.
anonymousiam 5mo ago
Not mentioned in the article/blog was the local alternative. Many applications will run just fine locally and not in the cloud. This is also more secure. Running local will probably eventually become the norm. It makes me wonder about the future of all these VC funded AI companies...
shay_ker 5mo ago
In the three options OP presents, I wonder if there's a fourth: BYO model Customers give vendors metered access to their model. They can budget tokens per vendor. Vendors selling "AI products" can have a cleaner story and win on the margin. The first step to is to iron out a reasonable protocol, basically authorizing a, access token, and then the model providers (OpenAI, Anthropic, etc.) do the rate limiting. Theoretically this could be done by OpenRouter too. But even so - do customers want an "AI product" packaged cleanly, or do they want to manage token capacity? They may be forced to do the latter....
- arnon 5mo ago
  It could happen, but it seems "regressive" almost as most companies are completely not ready to build this muscle.
yogthos 5mo ago
My expectation is that local models will be the default for coding within a year or two. You can already run Qwen 3.6 with MTP at a pretty reasonable speed without needing a huge amount of VRAM. And while it's not as good as current frontier models, it's already quite competent for a lot of tasks. And there's no sign that people are running out of ideas for how to optimize models further. You see a bunch of papers come out literally every few weeks right now. So, it's entirely plausible to me that we'll see models that are superior to current frontier ones in a year or two that will run on your machine. Once we get to that point, I don't think it's even going to matter if frontier models keep improving for most people. Being able to run the model on your machine, use it as much as you want in any way you want, without having to worry about it changing from under you or the company changing pricing, and not have to send all your data to the vendor are going to be the deciding factors. At some point the models are just good enough to do what you need to do. On top of that, I expect tooling around models and coding patterns will evolve as well. That could compensate significantly for the capabilities of the model. We already see this happening with two prime examples here: https://github.com/itigges22/ATLAS https://github.com/itigges22/ATLAS https://arxiv.org/abs/2509.16198 https://arxiv.org/abs/2509.16198
samhoss93 5mo ago
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koliber 5mo ago
EDIT: [ IGNORE THIS COMMENT -- IT IS WRONG - I had a "bad math moment" myself ] The math seems off. How is 7.8 million vs 4 million 95% more expensive. Article makes good points but I doubt the numbers as they don’t add up. Still agree with the conclusion though.
- pikminguy 5mo ago
  7.8 is higher than 4 by 3.8 3.8 is 95% of 4 The price went up by 3.8 or in other words the price went up by 95%. I'm not sure what math you're not mathing.
  - koliber 5mo ago
    Absolutely right. I had a "math moment" myself. I was thinking margin instead of markup.
MarkusQ 5mo ago
This is just wrong. The pricing so far has been a classic case of loss-leader to build market share and ramp up until you can find a moat. Normally, the huge cost of training would provide such a moat, or the amount of training data required, but both of those seem to have been overcome by enough players to keep the ball in play. The next target to keep out the riffraff seems to be "Gigawatts of Data Center" (gack, I hate that metric!) and you might think that it would hold, given the finite size of the planet. But in space, no one can hear you bleed cash, so...
mark_l_watson 5mo ago
> "which use cases earn the inference cost they burn?" That is the question. I love using OpenCode with paid inference providers and seeing the cost of every little thing I do. On the other hand, right now I am flipping between Antigravity CLI and the two Antigravity apps burning Claude Opus tokens like crazy, knocking off a ton of work. Google must be losing money on me.
kittikitti 5mo ago
Thank you for sharing this article. I think the graphs in it were useful in understanding the different pricing structures. One thing that I would have included is pricing based on AI that I own, through capital expenditure (CapEx). However, it's much harder to compare. For one, the cost per token is difficult to measure until a sufficient amount of time has passed so that an extrapolation is more accurate. Also, there are performance considerations where a local solution might be more or less accurate than an equivalent online AI. In addition, the reduced compliance risk is hard to quantify or it makes online AI practically useless. I don't understand how people got buy-in for a business model that assumed token costs would go down indefinitely. All tech startups follow a blitz-scaling pattern where they practically give away their services for free, trap customers in a moat, and then extort as much money as they can.
xnx 5mo ago
Lost me at "Ed Zitron has the math"
deleted 5mo ago
[deleted]
hereme888 5mo ago
NVIDIA’s published specs imply much larger gains in NVFP4 inference compute and GPU memory bandwidth than in BOM cost. That said, more intelligence and automation = higher costs.
DeathArrow 5mo ago
I use cheap Chinese models. For all I care, both OpenAI and Anthropic can raise their prices until they'll have no customers left.
ibtheory 5mo ago
also will bring up some good opportunities in the optimization space. Smaller and cheaper models + optimization can bring performance up, especially in certain domain specific applications of ai.
mingqiz 5mo ago
Deepseek dropped their price permanently.Now v4 pro costs 3.48% of the output token price of opus 4.7. (now that opus 4.7 has a more costly tokenizer). Is it fair to say that Anthropic/OpenAI is marching to bankruptcy as opensource models like deepseek improves? Note that deepseek also supports 500 concurrent requests per account for any individuals for v4 pro.

82 points by arnon 5mo ago

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Some of these coming price increases will move dev work back to dedicated shops and teams when individuals and non-devs won't want to pay the AI bill to finish and ship their projects. An outside small dev shop or internal dev team can pay these prices and spread the cost over several customers or departments, but the era of giving everyone AI and telling them to dev stuff is about to be over.

throwa356262 5mo ago

This is only true if your world is limited to openai, antropic and alike. There are a whole bunch of companies somewhere else in the world that are getting better and cheaper every month, hardware side included. all without the infinite VC money

fallpeak 5mo ago

This is slightly more tasteful slop than average (I'm thinking probably Claude rather than ChatGPT?), but it's still 100% AI written: https://www.pangram.com/history/c55ab69b-e0a9-49a0-8056-2fcd7d305463 https://www.pangram.com/history/c55ab69b-e0a9-49a0-8056-2fcd...

0x3f 5mo ago
This... is not a reliable AI detection method at all.
- extr 5mo ago
  Pangram is highly reliable.
  - jemmyw 5mo ago
    It can't be that good, I gave it some posts I wrote before llms were a thing and it gave them all 60%+ of the content as written by AI.
    - extr 5mo ago
      ok thank you for this anecdote
      - jemmyw 5mo ago
        Not really an anecdote, strictly, just a single point of evidence.
- fallpeak 5mo ago
  You are incorrect. There, now we've both made unsupported assertions. Care to provide any evidence for your position? For what it's worth, when I provide a Pangram link it's because I can already tell something is AI and I'm attempting to provide objective third-party confirmation so the conversation doesn't just degrade into me asserting that I have superior taste to you.
arnon 5mo ago
sorry, but i wrote this myself

_fat_santa 5mo ago

I wonder how much of Uber blowing their AI budget and MSFT pulling their claude code licenses can be attributed to "tokenmaxxing". When Meta announced token leaderboards and other followed, I could see this being the logical conclusion. That whole trend is so dumb because it leads to this. Company announces they will measure developer performance by how many tokens they burn and constantly talks about how the best developers burn the most tokens. Developers see the message and start burning tokens. And then the company acts surprised when their bills go through the roof. I personally use my OpenAI subscription pretty heavily, 2-3 agents running practically all day on various tasks but I never even get close to running into limits while I hear about others blowing through limits on multiple accounts in the same time period. I'm convinced that most of those folks and their elaborate workflows aren't really for productivity but for bragging rights about how much they use AI.

cayleyh 5mo ago
> I personally use my OpenAI subscription pretty heavily, 2-3 agents running practically all day on various tasks but I never even get close to running into limits Same. But if I was working for an organization that measured token usage, you can bet I would be doing things like creating a cron job that uses claude to create a customized bespoke report update of the current status of all my open assigned tickets and message that to myself 4 times a day... token burn for zero purpose whatsoever.
bdcravens 5mo ago
The same here, where I haven't come close to hitting any of my CC limits. Even though I'm more productive than I've ever been (as measured by finished, valuable tasks running in production) and I'm clearing out months of backlog, I have either one of two conclusions when I hear about others who suggest they need more: 1. I'm doing it wrong. Apparently I'm supposed to give it a vague paragraph about what the business does, and I can run off and sip margaritas and wake up to a fully fleshed business 2. They don't know what they're doing, and they're sending the LLM off on a wild goose chase that it does a reasonable job of working it's way out of, so they consider it success despite the waste.
ai_fry_ur_brain 5mo ago

adamesque 5mo ago

It's hard to take this piece seriously if he's citing _Ed Zitron's_ math, and equally hard to make the blanket statement that flat-rate plans = "the current AI pricing". But yes, those pricing models were pretty silly and unsustainable.

kimixa 5mo ago
Get back to me when there's an AI company that's actually profitable and we can compare their service and pricing. Claiming that there's some small subset of their services (like inference per token) that's "profitable" doesn't mean anything when it relies on everything else that company is still paying for. If you could make money from it at current prices - why aren't they? Otherwise it's just "how much they're willing to subsidize".
- dragontamer 5mo ago
  There is probably going to be a quarter or two of profits when the prices dramatically increase. Vibe coding techbros are hooked on the Iron Lung and may not want to get off. At my work are multiple developers bragging about overnight AI usage to solve problems hands off. Yes they are wasting money and resources but the fad is here. People be vibe coding for now. In like 6 months when all the costs need to be paid and the prices go up, we will see if these companies stay profitable. But I'm of the opinion that the vibe coding tech bros are more than enough to sustain a short or even medium term profit for these companies. Just on fad-energy alone (see OpenClaw) The fad probably collapses soon after. I hope anyway, the waste I see is nauseating. ------------ I dunno where this is all going. But I do have faith in human ingenuity still. Things are changing, possibly for the worse, but we need to make the best of it. The worst of behaviors is wasteful and blatant fraud. There's something useful here though.
- energy123 5mo ago
  The problem with fixating on earnings as you're doing is that it's a bad metric for a growth company. COGS is much more important. What you're doing is setting it up so every growth company is terrible until they've matured into a 20 year old company. That's obviously dumb.

PiRho3141 5mo ago

This is where open source models are important. The latest deepseek v4 pro model is 2-5x cheaper than Claude Sonnet 4.6. Cursor's Compose 2.5 that was just recently released is 6x cheaper than Sonnet. The state of the art models are going to get better and more expensive and smaller models are going to get cheaper. There will be a point where the intelligence of both the cheap and state of the art models are indistinguishable by humans like it is indistinguishable for me to understand the difference the difference between Terrance Tao and my university math professor. I don't always need the smartest and most expensive models. I will need it every once in awhile and will gladly pay that price if I had to. What I do need is the model that will solve the current problem I have in a reasonable amount of time.

squidbeak 5mo ago
Deepseek V4 Flash is far cheaper still, and a better model to compare to Sonnet 4.6. I'm finding it a reliable workhorse.
- anonzzzies 5mo ago
  Yep, people who never used it say it is not good.
greenmilk 5mo ago
> The state of the art models are going to get better and more expensive and smaller models are going to get cheaper. Why do you think this will be true? Right now I see the major US labs betting on gaining an advantage from having way more compute, and I see Chinese labs competing with one another in a resource-scarce environment, so they place much more emphasis on compute-efficiency. But the supply chains that feed into the massive data center growth in the US are strained; there are energy, memory, and logistical bottlenecks to name a few. In the medium-long run, compute capacity will not grow exponentially forever. Somehow it has for decades, but there can be no infinite exponential growth, and that point may be when the planet really starts to cook itself. Maybe the US labs will become more compute-constrained, and then have to compete on efficiency. Or maybe things change fundamentally in some other way I'm not thinking of.

Havoc 5mo ago

Inference costs absolutely did fall. And even more so when looking at intelligence it buys you. eg compare say gpt 3.5 to latest deepseek. Both cheaper and more at more capable

arnon 5mo ago
but are you using less of them because they're cheaper, or are you using more of the more advanced models and running them harder?

abtinf 5mo ago

Insofar as I can tell, inference is on a certain path toward becoming "free". The models are now extremely powerful on high-end consumer hardware, and the efficiency trend seems likely to continue. Here is a recent non-rigorous benchmark I ran against a bunch of models. Qwen3.6 35B A3B fine-tuned with opus data runs plenty fast on my local machine and produce outstanding results - easily in the top 5, comparable to GPT 5.5 Pro (which is $180/mtok). https://gistpreview.github.io/?31d66ef69e4aed3efae1aec69d86c298/ https://gistpreview.github.io/?31d66ef69e4aed3efae1aec69d86c... I've predicted for years now that the industry will head down the path of the virus scanning vendors: selling subscriptions to be able to download the latest versions of models. I simply don't see how any other business model is remotely viable, except at the very highest end of inference or video gen.

anonzzzies 5mo ago
That local hardware is not consumer though but prosumer. Consumer is a 500$ laptop running that and that is not currently the case.

extr 5mo ago

What is the OP talking about. $/unit intelligence is going down rapidly. You can achieve what would have been considered miracles in 2022 with < $10.

bdcravens 5mo ago
Absolutely, though I think the expectations are being set by those who have watched too many "OpenClaw business on autopilot" videos.
deleted 5mo ago
[deleted]

infecto 5mo ago

Has this not been true for a long time now? Most companies have had enterprise/business level prices that was highly connected to usage for a what feels like at least a year.

YetAnotherNick 5mo ago

You are comparing two different model. It's like saying roadster is more expensive than model S. No model pricing actually increased, and I am using GPT-4o in the same price as it was before. You can see price vs performance in artificial analysis and the the pareto optimal is all just 6 months old model.

vitalysemenov 5mo ago

[flagged]

plaidfuji 5mo ago

kind of sobering to realize that whether your job can be profitably automated away comes down to what $/token some hyperscale AI provider can deliver… I suppose it’s nice that this article highlights some upward pressure on that number.

pacman1337 5mo ago

I get similar results for deepseek and opus but opus is way faster. I guess deepseek streams thinking and makes it slower?

alligatorplum 5mo ago

I seldom use my PC anymore ever since i got a laptop. with the cost per token increasing along with the random "features" where models will just eat through your tokens in one hour. I really have been tempted to turn my PC into a server to run local models on there

alfiedotwtf 5mo ago

> Memory for 4x expensive > Did we collectively forget second order thinking? I bought 2x 16Gb NVIDIA cards this week because I don’t see hardware getting cheaper anytime soon, and because of that I totally don’t see the point of “waiting until prices go lower for graphics cards” because that might not for a long time yet! In fact, if you include factoring in world events (and the ones that haven’t happened yet but eventually will e.g. China’s 2027 long planned take of Taiwan), then there’s no way graphics prices are going to be accessible to mere mortals until at least 2028. But my real reasoning is that you’re going to see a flood of OpenAI and Anthropic users leave because of a) increasing pricing plans, and b) impeding business laws on the horizon about protecting sovereign data from AI (i.e data in cloud for training is a no no). So what happens when people and companies one by one start leaving the SOTA AI cloud for from-good-enough-to-wow models? RAM and graphics cards become the new toilet paper, which is going to double again current prices. Upgrade now before it’s too late folks!

energy123 5mo ago

Capex and revenue should not be compared like this, unless revenue is small and not growing.

anthonypasq 5mo ago

Guys, we are the in the mainframe era of AI. People in the 60's thought computing was expensive too and the idea of having a computer on every desk, nevermind every pocket, nevermind every single piece of electronics in the world basically seemed like a complete pipe dream. if you told someone in the 70's their toaster would have a supercomputer it in, they would think you were crazy. in 10 years your doorknob is going to have a local AI model it in. This is computing 2.0 not the dot com bubble. 90% of inference will be at the edge in the future and there will still be super-computers and giant clusters doing cutting edge science and research, but for 90% of use cases youll just need a tiny local model, same reason you dont need a giant GPU in your smart tv.

stephc_int13 5mo ago
The main issue with this reasoning is that the hardware substrate for AI and good old computing is the same. All governed by Moore's Law, what happened then seems extremely unlikely to happen again, the curve is a sigmoid and we're much closer to the flat end now.
- anthonypasq 5mo ago
  I think this has some truth to it I would say, but im pretty confident that small models will continue to get better and runnable on dedicated consumer hardware

tekacs 5mo ago

> Anthropic’s CFO testified under oath this March that the company spent $10 billion on compute and made $5 billion in revenue (Ed Zitron has the math). The labs are underwater on inference. They’re raising prices to keep the lights on. 'The labs are underwater on inference' is an absurd thing to say whilst not separating the cost of _compute_ out into training and inference.

dismalaf 5mo ago
I mean, I guess they could just stop training new models and coast, but they ARE training models so you have to include those costs.
JimDabell 5mo ago
According to Dario Amodei, Anthropic are even profitable when including inference as long as you look at it on a per-model basis; it’s just that every model is more expensive to train than the last one. For instance, if you have already spent $n to train a model and are currently earning $2n selling inference with it; but are concurrently spending $3n training the next model in anticipation of earning $6n with it, then you are already in the hole for $n and are currently also losing $n – but you are doubling your money with each model because your $n investment in the first model returns $2n and your $3n investment in the second model returns $6n. Also: > Ed Zitron has the math Ed Zitron is constantly wrong about AI economics: https://www.theargumentmag.com/p/ais-biggest-critic-has-lost-the-plot https://www.theargumentmag.com/p/ais-biggest-critic-has-lost...
- otabdeveloper4 5mo ago
  > According to Dario Amodei That's a big ask. No thanks.
saltcured 5mo ago
How is training vs inference any different than other product spaces, where all the costs of bringing a product to market have to be considered for profitability? You can't just look at marginal production cost. You are still underwater if the other development costs are not being recouped by the final sales revenue. The whole commercial AI enterprise is not economically viable if the inference revenue will not cover both inference and the amortized training costs. Given how fast they are churning through models to compete, you cannot act like the training is an asymptotically low cost.

anonymousiam 5mo ago

Not mentioned in the article/blog was the local alternative. Many applications will run just fine locally and not in the cloud. This is also more secure. Running local will probably eventually become the norm. It makes me wonder about the future of all these VC funded AI companies...

shay_ker 5mo ago

In the three options OP presents, I wonder if there's a fourth: BYO model Customers give vendors metered access to their model. They can budget tokens per vendor. Vendors selling "AI products" can have a cleaner story and win on the margin. The first step to is to iron out a reasonable protocol, basically authorizing a, access token, and then the model providers (OpenAI, Anthropic, etc.) do the rate limiting. Theoretically this could be done by OpenRouter too. But even so - do customers want an "AI product" packaged cleanly, or do they want to manage token capacity? They may be forced to do the latter....

arnon 5mo ago
It could happen, but it seems "regressive" almost as most companies are completely not ready to build this muscle.

yogthos 5mo ago

My expectation is that local models will be the default for coding within a year or two. You can already run Qwen 3.6 with MTP at a pretty reasonable speed without needing a huge amount of VRAM. And while it's not as good as current frontier models, it's already quite competent for a lot of tasks. And there's no sign that people are running out of ideas for how to optimize models further. You see a bunch of papers come out literally every few weeks right now. So, it's entirely plausible to me that we'll see models that are superior to current frontier ones in a year or two that will run on your machine. Once we get to that point, I don't think it's even going to matter if frontier models keep improving for most people. Being able to run the model on your machine, use it as much as you want in any way you want, without having to worry about it changing from under you or the company changing pricing, and not have to send all your data to the vendor are going to be the deciding factors. At some point the models are just good enough to do what you need to do. On top of that, I expect tooling around models and coding patterns will evolve as well. That could compensate significantly for the capabilities of the model. We already see this happening with two prime examples here: https://github.com/itigges22/ATLAS https://github.com/itigges22/ATLAS https://arxiv.org/abs/2509.16198 https://arxiv.org/abs/2509.16198

samhoss93 5mo ago

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koliber 5mo ago

EDIT: [ IGNORE THIS COMMENT -- IT IS WRONG - I had a "bad math moment" myself ] The math seems off. How is 7.8 million vs 4 million 95% more expensive. Article makes good points but I doubt the numbers as they don’t add up. Still agree with the conclusion though.

pikminguy 5mo ago
7.8 is higher than 4 by 3.8 3.8 is 95% of 4 The price went up by 3.8 or in other words the price went up by 95%. I'm not sure what math you're not mathing.
- koliber 5mo ago
  Absolutely right. I had a "math moment" myself. I was thinking margin instead of markup.

MarkusQ 5mo ago

This is just wrong. The pricing so far has been a classic case of loss-leader to build market share and ramp up until you can find a moat. Normally, the huge cost of training would provide such a moat, or the amount of training data required, but both of those seem to have been overcome by enough players to keep the ball in play. The next target to keep out the riffraff seems to be "Gigawatts of Data Center" (gack, I hate that metric!) and you might think that it would hold, given the finite size of the planet. But in space, no one can hear you bleed cash, so...

mark_l_watson 5mo ago

> "which use cases earn the inference cost they burn?" That is the question. I love using OpenCode with paid inference providers and seeing the cost of every little thing I do. On the other hand, right now I am flipping between Antigravity CLI and the two Antigravity apps burning Claude Opus tokens like crazy, knocking off a ton of work. Google must be losing money on me.

kittikitti 5mo ago

Thank you for sharing this article. I think the graphs in it were useful in understanding the different pricing structures. One thing that I would have included is pricing based on AI that I own, through capital expenditure (CapEx). However, it's much harder to compare. For one, the cost per token is difficult to measure until a sufficient amount of time has passed so that an extrapolation is more accurate. Also, there are performance considerations where a local solution might be more or less accurate than an equivalent online AI. In addition, the reduced compliance risk is hard to quantify or it makes online AI practically useless. I don't understand how people got buy-in for a business model that assumed token costs would go down indefinitely. All tech startups follow a blitz-scaling pattern where they practically give away their services for free, trap customers in a moat, and then extort as much money as they can.

xnx 5mo ago

Lost me at "Ed Zitron has the math"

deleted 5mo ago

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hereme888 5mo ago

NVIDIA’s published specs imply much larger gains in NVFP4 inference compute and GPU memory bandwidth than in BOM cost. That said, more intelligence and automation = higher costs.

DeathArrow 5mo ago

I use cheap Chinese models. For all I care, both OpenAI and Anthropic can raise their prices until they'll have no customers left.

ibtheory 5mo ago

also will bring up some good opportunities in the optimization space. Smaller and cheaper models + optimization can bring performance up, especially in certain domain specific applications of ai.

mingqiz 5mo ago

Deepseek dropped their price permanently.Now v4 pro costs 3.48% of the output token price of opus 4.7. (now that opus 4.7 has a more costly tokenizer). Is it fair to say that Anthropic/OpenAI is marching to bankruptcy as opensource models like deepseek improves? Note that deepseek also supports 500 concurrent requests per account for any individuals for v4 pro.