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

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.

This is the relevant quote from the original article.

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.

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.

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.

anthropic doesn't have that. single provider, single pricing decision. whether or not $5k is accurate the more interesting question is what happens to inference pricing when the supply side is genuinely open. we're seeing hints of it with open router but its still intermediated

not saying this solves anthropic's cost problem, just that the "what does inference actually cost" question gets a lot more interesting when providers are competing directly

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.

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?

…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.

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.

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.

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.

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

> Anthropic is looking at approximately $500 in real compute cost for the heaviest users.

Aren't they losing money on the retail API pricing, too?

> ... comparisons to artificially low priced Chinese providers...

Yeah, no this article does not pass the sniff test.

It’s worth it, but I know they aren’t making money on me. But, of course I’m marketing them constantly so…