No, it doesn't cost Anthropic $5k per Claude Code user

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.

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.

The quantisation is shown on the provider section.

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.

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.

However, the GAAP P&L tells the opposite story. You book $200M revenue in the same year you spend $1B training the next model, so you report an $800M loss. Next year you book $2B against $10B in training spend, reporting an $8B loss. The business looks like it's dying when every individual model generation actually generates a healthy profit.

That's actually Dario's answer to your depreciation question. If each cohort earns back its training cost within its natural lifespan (however short that lifespan is), the depreciation schedule is already baked in. The model doesn't need to live forever, it just needs to return more than it cost before the next one replaces it. Whether that's actually happening at Anthropic is a different question, and one we can't answer without audited financials, but it's the claim Dario makes (and seems entirely reasonable from a distance).

Yes, this is exactly why OpenAI and Anthropic are hyping AGI. If LLMs ever become good enough to replace workers, the first sign will be frontier model companies launching competitor businesses. It doesn't make sense to sell the formula for gold when you can just use it yourself.

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

Possible, but no signs of this on the horizon. If it does happen, it's impossible to predict when it will.

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

I'm not sure market dynamics will allow this any time soon. We seem to have already achieved a marginal utility equilibrium in terms of model size, so training new models on trending use-cases (e.g. synthetic data targeting tool calls, agentic workflows, computer use, etc) is really the driving force behind product differentiation. Nobody wants to admit "training new models isn't profitable" because that deflates the AGI singularity narrative that all this investment hinges on.

Maybe not? This is an argument that has to be made using numbers. We can't do the estimate without the numbers.

This is what the elites of the gilded age called "ruinous competition", and the solution today will be the same as back then: monopoly power. This has been the business plan of the tech VC industry for 25+ years.

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.

Theres quite a lot of evidence, no proof I'd agree, but then there's no absolute proof I'm aware to the contrary either, so I don't know where you're getting this from.

The two pieces of evidence I'm aware of is that 1) Anthropic doesn't want their subsidised plans being used outside of CC, which would imply that the money their making off it isn't enough, and 2) last time I checked, API spending is capped at $5000 a month

Like I say, neither of these are proof, you can come up with reasonable arguments against them, but once again the same could be said for evidence on the contrary

I think it’s fairly obvious that Anthropic is lighting cash on fire and focusing on whether or not they’re losing money per token on inference is missing the forest for the trees.

Tokens become less valuable when the models aren’t continuously trained and we have zero idea what Anthropic is paying for training.

https://www.wheresyoured.at/oai_docs/

We don’t have clear evidence either way but it heavily leans to API pricing at least covering inference cost. Models these days have less and less differentiation and for API use there must be some thought to compete on cost but it’s not going to be winner take all. They leap frog each other with each new model.

Ask Opus to figure out how much it would cost. Lol.

Are Anthropic currently unable to sell subscriptions because they don’t have capacity?

I think it's the other way around? Sparse use of GPU farms should be the more expensive thing. Full saturation means that we can exploit batching effects throughout.

The entertainment industry. They still tell you about how much money they're leaving on the table because people pirate stuff.

What would happen in reality for entertainment is people would "consume" far less "content".

And what would happen in reality for Anthropic is people would start asking themselves if the unpredictability is worth the price. Or at best switch to pay as you go and use the API far less.

I mean... rolex is overpriced brand whose cost to consumers is mainly just marketting in itself. Its production cost is nowhere close to selling price and looking at gears is fair way of evaluating that

Only thing that matters is if the users would have paid $5000 if they don't have option to buy subscription. And I highly doubt they would have.

I'm sure Anthropic is making money off the API but I highly doubt it's 90% profit margins.

Profit implies a GAAP accrual of some sort. On any accrual schedule tied to reality, the companies are profitable now - that is, inference margin on each given model has more than paid for capital costs of training and deploying those models.

That the companies get to show a loss is a feature of cash-basis accounting: they made $100m net on that last model? Good news, We’re spending $1b on the next! Infinite tax losses!

The companies will not be cashflow positive for years. Why does this persnickety difference matter? It matters to me because I care about the engineers here - and they seem collectively likely to either short every AI company IPOing, or just quietly ignore AI impact on their livelihood, or head off into a corner and go catatonic - all based on a worldview that “this is collective insanity and everything here is going to eventually go bankrupt” — none of those are good outcomes. Shorting might be, but it should be done judiciously, and understanding the financial factors at play. So, anyway, long plea over - but, allow me to plead: cashflow positive if you want to make the point you were making.

The gamble with caching is to hold a KV cache in the hope that the user will (a) submit a prompt that can use it and (b) that will get routed to the right server which (c) won't be so busy at the time it can't handle the request. KV caches aren't small so if you lose that bet you've lost money (basically, the opportunity cost of using that RAM for something else).

I'm incredibly salty about this - they're essentially monetizing intensely something that allows them to sell their inference at premium prices to more users - without any caching, they'd have much less capacity available.

Why would it go idle? It would go to their next best use. At least they could help with model training or let their researchers run experiments etc.

This hasn't been true in a long time.

Have they announced this?

So no, Claude would not be getting NEARLY as much usage as it's currently getting if it weren't for the $100/$200 monthly subscription. You're comparing Kimi to the price that most people aren't paying.

[1] https://www.wheresyoured.at/anthropic-is-bleeding-out/ [2] https://www.wheresyoured.at/costs/

> My LinkedIn and Twitter feeds are full of screenshots from the recent Forbes article on Cursor claiming that Anthropic's $200/month Claude Code Max plan can consume $5,000 in compute.

API inference access is naturally a lot more costly to provide compared to Chat UI and Claude Code, as there is a lot more load to handle with less latency. In the products they can just smooth over load curves by handling some of the requests slower (which the majority of users in a background Code session won't even notice).

now, the consensus of the commentards on this website, who don't have access to any of anthropics financial data, is that the monthly subscriptions are a money loser!

so either the leading AI company's business dev team is wrong or the Jacker News comment section is wrong, it is a mystery

However I think it's fair to say the cost is roughly linear in the number of users other than that.

There may be some aspects which are not quite linear when you see multiple users submitting similar queries... But I don't think this would be significant.

As for LLM, there is probably some cost constant added once it can fit on a single GPU, but should probably be almost linear.

but $5 that I amortize over 7 years might end up being $1.7 maybe if I don't rapidly combust (supply chain risk)

Everyone else pays them at API prices

No, they aren't, and probably neither is anyone else offering API pricing. And Anthropic's API margins may be higher than anyone else.

For example, DeepSeek released numbers showing that R1 was served at approximately "a cost profit margin of 545%" (meaning 82% of revenue is profit), see my comment https://news.ycombinator.com/item?id=46663852

In my case, the access logs alone from bots scanning for vulns grew so large that the server started creating.

Fortunately I wasn't running anything vulnerable!