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
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
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.
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.
When I have a feeling that these tools will speed me up, I use them.
My client pays for a couple of these tools in an enterprise deal, and I suspect most of us on the team work like that.
If my goal was to max out every tool my client pays, I’d be working 24hrs a day and see no sunlight ever.
I guess it’s like the all you can eat buffet. You eat a lot, but if you eat so much that you throw up and get sick, you are special.
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.
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.
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.
Are Anthropic currently unable to sell subscriptions because they don’t have capacity?
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
I'm sure Anthropic is making money off the API but I highly doubt it's 90% profit margins.
Unlikely. Amazon Bedrock serves Opus at 120tokens/sec.
If you want to estimate "the actual price to serve Opus", a good rough estimate is to find the price max(Deepseek, Qwen, Kimi, GLM) and multiply it by 2-3. That would be a pretty close guess to actual inference cost for Opus.
It's impossible for Opus to be something like 10x the active params as the chinese models. My guess is something around 50-100b active params, 800-1600b total params. I can be off by a factor of ~2, but I know I am not off by a factor of 10.
42 tps for Claude Opus 4.6 https://openrouter.ai/anthropic/claude-opus-4.6
143 tps for GLM 4.7 (32B active parameters) https://openrouter.ai/z-ai/glm-4.7
70 tps for Llama 3.3 70B (dense model) https://openrouter.ai/meta-llama/llama-3.3-70b-instruct
For GLM 4.7, that makes 143 * 32B = 4576B parameters per second, and for Llama 3.3, we get 70 * 70B = 4900B, which makes sense since denser models are easier to optimize. As a lower bound, we get 4576B / 42 ≈ 109B active parameters for Opus 4.6. (This makes the assumption that all three models use the same number of bits per parameter and run on the same hardware.)I'd say Opus is roughly 2x to 3x the price of the top Chinese models to serve, in reality.
Of course, intense sparsification via MoE (and other techniques ;) ) lets total model size largely decouple from inference speed and cost (within the limit of world size via NVlink/TPU torrus caps)
So the real mystery, as always, is the actual parameter count of the activated head(s). You can do various speed benchmarks and TPS tracking across likely hardware fleets, and while an exact number is hard to compute, let me tell you, it is not 17B or anywhere near that particular OOM :)
Comparing Opus 4.6 or GPT 5.4 thinking or Gemini 3.1 pro to any sort Chinese model (on cost) is just totally disingenuous when China does NOT have Vera Rubin NVL72 GPUs or Ironwood V7 TPUs in any meaningful capacity, and is forced to target 8gpu Blackwell systems (and worse!) for deployment.
However, I'd say its relatively well assumed in realpolitik land that Chinese labs managed to acquire plenty of H100/200 clusters and even meaningful numbers of B200 systems semi-illicitly before the regulations and anti-smuggling measures really started to crack down.
This does somewhat beg the question of how nicely the closed source variants, of undisclosed parameter counts, fit within the 1.1tb of H200 or 1.5tb of B200 systems.
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.
“what X actually is”
“the X reality check”
Overuse of “real” and “genuine”:
> The real story is actually in the article. … And the real issue for Cursor … They have real "brand awareness", and they are genuinely better than the cheaper open weights models - for now at least. It's a real conundrum for them.
> … - these are genuinely massive expenses that dwarf inference costs.
This style just screams “Claude” to me.
It has enough tells in the correct frequency for me to consider it more than 50% generated.
Popular content is popular because it is above the threshold for average detection.
In a better world, platforms would empower defenders, by granting skilled human noticers flagging priority, and by adopting basic classifiers like Pangram.
Unfortunately, mainstream platforms have thus far not demonstrated strong interest in banning AI slop. This site in particular has actually taken moderation actions to unflag AI slop, in certain occasions...
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.
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.
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.
[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.
So the article's title is obviously sensationalized.
but $5 that I amortize over 7 years might end up being $1.7 maybe if I don't rapidly combust (supply chain risk)
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.
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