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> Qwen 3.5 397B-A17B is a good comparison

It is not. It's a terrible comparison. Qwen, deepseek and other Chinese models are known for their 10x or even better efficiency compared to Anthropic's.

That's why the difference between open router prices and those official providers isn't that different. Plus who knows what open routed providers do in term quantization. They may be getting 100x better efficiency, thus the competitive price.

That being said not all users max out their plan, so it's not like each user costs anthropic 5,000 USD. The hemoragy would be so brutal they would be out of business in months

That's a tautology. People think chinese models are 10x more efficient because they're 10x cheaper, and then you use that to claim that they're 10x more efficient.

Opus isn't that expensive to host. Look at Amazon Bedrock's t/s numbers for Opus 4.5 vs other chinese models. They're around the same order of magnitude- which means that Opus has roughly the same amount of active params as the chinese models.

Also, you can select BF16 or Q8 providers on openrouter.

Agree, but I guess the Opus 4.6 is 10x larger, rather than Chinese models being 10x more efficient. It is said that GPT-4 is already a 1.6T model, and Llama 4 behemoth is also much bigger than Chinese open-weight models. Chinese tech companies are short of frontier GPUs, but they did a lot of innovations on inference efficiency (Deepseek CEO Liang himself shows up in the author list of the related published papers).
Comparing open-source models like Qwen against Anthropic’s models is absolutely foolish. First of all, Anthropic has never disclosed the actual parameter count or architecture of their models. Second, it’s well known that these open-source models more or less distill from other models and use MoE, which allows them to run at much lower computational costs. Using Qwen as a comparison point only proves the blog post author is foolish. The article devoted such a large portion to discussing Qwen on OpenRouter, I find it hard to believe.
No they wouldn't. They have tons of funding. They absolutely can and do absorb costs like this. Don't think anyone is ever gonna tell you precise numbers (and it also varies based on workload of course)...but this is literally the business model of AI providers.

They're goal (similar to Uber, DoorDash, Robin Hood, etc.) is to get mass adoption. Their business models only work at this kind of scale.

It's completely impossible to have consumers pay $20-60/mo and be a profitable business without mass adoption where some are not using it as much as others...and, perhaps more importantly, the masses put pressure on their employers to pay for their tooling. This is why pricing does not need to come down.

Quite literally I have engineers spending over $1,000/mo on Opus. That's the goal.

> Plus who knows what open routed providers do in term quantization

The quantisation is shown on the provider section.

Actually, Opus might achieve a lower cost with the help of TPUs.
>It is not. It's a terrible comparison. Qwen, deepseek and other Chinese models are known for their 10x or even better efficiency compared to Anthropic's.

I find it a good comparison because it is a good baseline since we have zero insider knowledge of Anthropic. They give me an idea that a certain size of a model has a certain cost associated.

I don't buy the 10x efficiency thing: they are just lagging behind the performance of current SOTA models. They perform much worse than the current models while also costing much less - exactly what I would expect. Current Qwen models perform as good as Sonnet 3 I think. 2 years later when Chinese models catchup with enough distillation attacks, they would be as good as Sonnet 4.6 and still be profitable.

> That being said not all users max out their plan,

These are not cell phone plans which the average joe takes, they are plans purchased with the explicit goal of software development.

I would guess that 99 out of every 100 plans are purchased with the explicit goal of maxing them out.

A huge number of people are convinced that OpenAI and Anthropic are selling inference tokens at a loss despite the fact that there's no evidence this is true and a lot of evidence that it isn't. It's just become a meme uncritically regurgitated.

This sloppy Forbes article has polluted the epistemic environment because now theres a source to point to as "evidence."

So yes this post author's estimation isn't perfect but it is far more rigorous than the original Forbes article which doesn't appear to even understand the difference between Anthropic's API costs and its compute costs.

I'd love to be a fly on the wall when this argument is tried in front of a bankruptcy court. It drives me nuts. Of course there's evidence that they're selling tokens at a loss.

The only thing these companies sell are tokens. That's their entire output. OpenAI is trying to build an ad business but it must be quite small still relative to selling tokens because I've not yet seen a single ad on ChatGPT. It's not like these firms have a huge side business selling Claude-themed baseball caps.

That means the cost of "inference" is all their costs combined. You can't just arbitrarily slice out anything inconvenient and say that's not a part of the cost of generating tokens. The research and training needed to create the models, the salaries of the people who do that, the salaries of the people who build all the serving infrastructure, the loss leader hardcore users - all of it is a part of the cost of generating each token served.

Some people look at the very different prices for serving open weights models and say, see, inference in general is cheap. But those costs are distorted by companies trying to buy mindshare by giving models away for free, and of those, both the top labs keep claiming the Chinese are distilling them like crazy including using many tactics to evade blocks! So apparently the cost of a model like DeepSeek is still partly being subsidized by OpenAI and Anthropic against their will. The cost of those tokens is higher than what's being charged, it's just being shifted onto someone else's books. Nice whilst it lasts, but this situation has been seen many times in the past and eventually people get tired of having costs externalized onto them.

For as long as firms are losing money whilst only selling tokens, that means those tokens are selling at a loss. To not sell tokens at a loss the companies would have to be profitable.

"Any conversation about token costs devolves into an ad-hoc, informally-specified, bug-ridden implementation of half of generally accepted accounting principles."

We have a way of determining if Anthropic is, or has the capability of being profitable, and what the levers to that may be. AI may be world-changing, but the accounting principles behind AI labs are no different than those behind a Pizza Hut.

Even if the cost of "inference + serving" is lower than the cost of selling a token, the relevant question is what is the depreciation schedule of the cost of training. ie, if I spend $1 on training, how long do I have before I have to spend $1 again?

Almost certainly, any reasonable depreciation schedule of the cost of training will result in leading labs being presently wildly unprofitable. So the question is:

What can be done to make training depreciate more slowly? Perhaps users can be persuaded to stick around using non-fronteir models for longer, although then there's a shift in the competitive landscape.

If users cannot be persuaded (forced?) to use legacy models, then the entire business model is thrown into question, because there's no reason why training frontier models would ever get cheaper: even if it gets cheaper on the margin, surely that will result in more compute used to generate an even "better" model, resulting in more spend in the aggregate.

This doesn't mean that the AI industry is "doomed". A couple things could happen, and this is where the fronteir labs should be focusing their attention:

1. They could find a way to climb up the value chain and capture more of the consumer surplus.

2. There could be a paradigm shift in compute architecture/compute cost.

3. We could reach a limit of marginal utility, shifting consumption to legacy models, thereby lengthening the depreciation/utility of training.

Edit: My assertion of "Almost certainly, any reasonable depreciation schedule of the cost of training will result in leading labs being presently wildly unprofitable." is made with no real information, just a gut feeling, and should not be taken seriously.

I calculated only last weekend that my team would cost, if we would run Claude Code on retail API costs, around $200k/mo. We pay $1400/month in Max subscriptions. So that's $50k/user... But what tokens CC is reporting in their json -> a lot of this must be cached etc, so doubt it's anywhere near $50k cost, but not sure how to figure out what it would cost and I'm sure as hell not going to try.
> Cost remains an ever present challenge. Cursor’s larger rivals are willing to subsidize aggressively. According to a person familiar with the company’s internal analysis, Cursor estimated last year that a $200-per-month Claude Code subscription could use up to $2,000 in compute, suggesting significant subsidization by Anthropic. Today, that subsidization appears to be even more aggressive, with that $200 plan able to consume about $5,000 in compute, according to a different person who has seen analyses on the company’s compute spend patterns.

This is the relevant quote from the original article.

If Anthropic's compute is fully saturated then the Claude code power users do represent an opportunity cost to Anthropic much closer to $5,000 then $500.

Anthropic's models may be similar in parameter size to model's on open router, but none of the others are in the headlines nearly as much (especially recently) so the comparison is extremely flawed.

The argument in this article is like comparing the cost of a Rolex to a random brand of mechanical watch based on gear count.

How confident are you in the opus 4.6 model size? I've always assumed it was a beefier model with more active params that Qwen397B (17B active on the forward pass)
There's a huge difference between cost of inference and profit margin of the "big" providers, and the cost of inference for cloud-hosted open-weights. It's the same as R&D cost of the pharmaceutical industry, versus cost of producing generic drugs. One is massively expensive, the other is cheap.

That said, for inference, the margins for OpenAI were estimated at 70% [1] [2], and the margins for Anthropic were estimated between 90% and 40% [3] [4], last year. They will not be profitable for years.

[1] https://phemex.com/news/article/openais-ai-profit-margin-cli... [2] https://www.saastr.com/have-ai-gross-margins-really-turned-t... [3] https://www.theinformation.com/articles/anthropic-projects-7... [4] https://www.investing.com/news/stock-market-news/anthropic-t...

This article is hilariously flawed, and it takes all of 5 seconds of research to see why.

Alibaba is the primary comparison point made by the author, but it's a completely unsuitable comparison. Alibab is closer to AWS then Anthropic in terms of their business model. They make money selling infrastructure, not on inference. It's entirely possible they see inference as a loss leader, and are willing to offer it at cost or below to drive people into the platform.

We also have absolutely no idea if it's anywhere near comparable to Opus 4.6. The author is guessing.

So the articles primary argument is based on a comparison to a company who has an entirely different business model running a model that the author is just making wild guesses about.

What people don't realize is that cache is *free*, well not free, but compared to the compute required to recompute it? Relatively free.

If you remove the cached token cost from pricing the overall api usage drops from around $5000 to $800 (or $200 per week) on the $200 max subscription. Still 4x cheaper over API, but not costing money either - if I had to guess it's break even as the compute is most likely going idle otherwise.

This is such a well-written essay. Every line revealed the answer to the immediate question I had just thought of
Claude subscription is equivalant of spot instance

And APIs are on-demand service equivalant.

Priority is set to APIs and leftover compute is used by Subscription Plans.

When there is no capacity, subscriptions are routed to Highly Quantized cheaper models behind the scenes.

Selling subscription makes it cheaper to run such inference at scale otherwise many times your capacity is just sitting there idle.

Also, these subscription help you train your model further on predictable workflow (because the model creators also controls the Client like qwen code, claude code, anti gravity etc...)

This is probably why they will ban you for violating TOS that you cannot use their subscription service model with other tools.

They aren't just selling subscription, but the subscription cost also help them become better at the thing they are selling which is coding for coding models like Qwen, Claude etc...

I've used qwen code, codex and claude.

Codex is 2x better than Qwen code and Claude is 2x better than Codex.

So I'd hope the Claude Opus is atleast 4-5x more expensive to run than flagship Qwen Code model hosted by Alibaba.

Good article! Small suggestions:

1. It would be nice to define terms like RSI or at least link to a definition.

2. I found the graph difficult to read. It's a computer font that is made to look hand-drawn and it's a bit low resolution. With some googling I'm guessing the words in parentheses are the clouds the model is running on. You could make that a bit more clear.

These margins are far greater than the ones Dario has indicated during many of his recent podcasts appearances.
Was anyone under the impression that it does? Serious question. I've never heard that, personally.
By the way, one of the charts in the article shows that Opus 4.6 is 10x costlier than Kimi K2.5.

I thought there was no moat in AI? Even being 10x costlier, Anthropic still doesn't have enough compute to meet demand.

Those "AI has no moat" opinions are going to be so wrong so soon.

Whilst this is interesting I find the topic bought up on odd lots is more interesting. The idea was this: Once you've built a model, if you can sell tokens for a profit, this is a great business - just sell more tokens. But you can't just build a model and sell tokens. You need to build the best model to sell new tokens. So the question is much more "How much does it cost you to build a new SotA model" and then "How effectively can you monetize it". And since you need a SotA model, your only option if you have a bad model that isn't selling is to invest billions more into building a better model whose tokens you can sell.

So this turns into a death march.

If you are behind, the only thing you can do is make massive capital investments to catch up. Once you're ahead you can sell tokens until someone else catches up. And, breaking the model of normal of places like chip fabrication, your billions of investment may only keep you ahead for 2 months. So you have a tiny window to sell those tokens.

I think the main issue I have with the article is that author whole argument is based on 'Qwen wouldn't run at a loss'. But why wouldn't it? Depsite it being a business, there might be a number of arguments why they decide to run without profit for now: from trying to expand the user base, to Chinese government sponsoring Chinese AI business.
What CC costs internally is not public. How efficient it is, is not public.

…You could take efficiency improvement rates from previous models releases (from x -> y) and assume; they have already made “improvements” internally. This is likely closer to what their real costs are.

Is it fair to say the Open Router models aren't subsidized though? They make the case that companies on there are running a business, but there are free models, and companies with huge AI budgets that want to gather training data and show usage.
We'll only know for sure when either of these companies goes public. Google serves inference and Google Cloud is profitable but we don't know how much inference is costing.

If they never go public, there's our answer as well.

What this doesn't mention is the "cost" to the public: the inevitable bailouts after it all comes crashing down again, the massive subsidies that Datacenters get from tax payers, the fresh water they consume, the electricity price hikes for everyone else, the noise, air and water pollution and the massive health impact on the surrounding population of every datacenter. The jobs that it destroys and the innocent people it kills through use of the technology in military targeting and autonomous weapons usage.
“Cursor has to pay Anthropic's retail API prices (or close to it) for access to Opus 4.6. So to provide a Claude Code-equivalent experience using Opus 4.6, it would cost Cursor ~$5,000 per power user per month. But it would cost Anthropic perhaps $500 max.”

Cursor seems to be in a tough spot. Just heard the swix podcast on their big new cloud agents thing, and it’s looking like a pretty small moat these days.

Nobody gets RSI typing “iterate until tests pass”
Been running Claude Code and the $200/month has been one of the better value decisions I've made as a founder.

The more interesting question is where the margins go as inference costs keep dropping. At some point the pricing pressure flows to users.

And on top of that, Anthropic does not run their own compute clusters do they? They probably get completely ripped by whoever is renting them the processors.

$200 worth of actual computation is an awful lot of computation.

I'm using API directly for software developement, i'm on path to pay ~$5k this month per user, some less , some more, with daily use is just growing more and more.
Agent teams change everything. I can easily burn through 1m tokens in 15 minutes. There's no way the $200 price will hold once everyone is doing it.
Nobody seems to mention free users who have a pretty small limit but there are a lot of users in this category. Who’s subsidizing them?
Why does Claude charge 10x for API, compared to subscriptions? They're not a monopoly, so one would expect margins to be thinner.
The comparison with Qwen/Kimi by "comparable architecture size" is doing a lot of heavy lifting. Parameter count doesn't tell you much when the models aren't in the same league quality-wise.

I wonder if a better proxy would be comparing by capability level rather than size. The cost to go from "good" to "frontier" is probably exponential, not linear - so estimating Anthropic's real cost from what it takes to serve Qwen 397B seems off.

Well, IDK, I have used CC with API billing pretty extensively and managed to spend ~$1000 in one month more or less. Moved to a Max 20x subscription and using it a bit less (I'm still scared) but not THAT less and I'm around 10% weekly usage. I'm not counting the tokens, though.
Did anthropic do the oldest SaaS sales trick in the 2010s SaaS playbooks ;)
I have very naive question:

People in comments have assumption that Atropic 10 times bigger than chinese models so calc cost is 10 times more.

But from perspective of Big O notation only a few algorithms gives you O(N). Majority high optimized things provide O(N*Log(N))

So what is big O for any open model for single request?

The compute cost debate misses a subtler point: the real cost multiplier isn't inference, it's context length. Most agent frameworks naively stuff 6-8k tokens into every prompt turn. If you route intelligently and compress memory hierarchically, you can bring that down to 200-400 tokens per turn with no quality loss. The model cost then becomes almost irrelevant.
One consideration to me, regardless of the exact burn rate on inference is the assumed increase in revenues via higher fees. One of the bull cases I often see is that the hockey stick revenue growth continues longer/higher than the hockey stick cost growth. Then it all prints money because people are spending 10x/100x/1000x what they are today.

In the real world ..

Where I work, AI is used heavily, we are already tipping into cost management mode at a firm level. Users are being aggressively steered to cheaper models, usage throttled, and cost attribution reports sent. This is already being done at the under-$1k/mo per user cost level. So some indications of revenue per user leveling out already.

Meanwhile everyone I know who works anywhere near a computer has had AI shoved down their throat, with training, usage KPIs, annual goal setting and mandated engagement. So we are already pretty saturated, it's not like theres giant new frontiers of new users.

They are clearly making money overall.
Yea, it costs more than that.
tldr: the author argues it is closer to costing 500 USD per month IF a user hits their weekly rate limits every week.

Which is probably a lot more correct than other claims. However it's also true that anybody who has to use the API might pay that much, creating a real cost per token moat for Anthropics Claude code vs other models as long as they are so far ahead in terms of productivity.