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Opus doubled in speed with version 4.5, leading me to speculate that they had promoted a sonnet size model. The new faster opus was the same speed as Gemini 3 flash running on the same TPUs. I think anthropics margins are probably the highest in the industry, but they have to chop that up with google by renting their TPUs.

> That's a tautology. People think chinese models are 10x more efficient because they're 10x cheaper

They do have different infrastructure / electricity costs and they might not run on nvidia hardware.

It's not just the models.

No, Opus cannot be 10x larger than the chinese models.

If Opus was 10x larger than the chinese models, then Google Vertex/Amazon Bedrock would serve it 10x slower than Deepseek/Kimi/etc.

That's not the case. They're in the same order of magnitude of speed.

GPT-4 was likely much larger than any of the SOTA models we have today, at least in terms of active parameters. Sparse models are the new standard, and the price drop that came with Opus 4.5 made it fairly obvious that Anthropic are not an exception.

> 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.

Define "much worse".

  +--------------------------------------+-------------+-----------+------------------+
  | Benchmark                            | Claude Opus | DeepSeek  | DeepSeek vs Opus |
  +--------------------------------------+-------------+-----------+------------------+
  | SWE-Bench Verified (coding)          | 80.9%       | 73.1%     | ~90%                 |
  | MMLU (knowledge)                     | ~91         | ~88.5     | ~97%               |
  | GPQA (hard science reasoning)        | ~79–80      | ~75–76    | ~95%             |
  | MATH-500 (math reasoning)            | ~78         | ~90       | ~115%            |
  +--------------------------------------+-------------+-----------+------------------+

I’m not maxing them out… I have issues that I need to fix, features I need to develop, and I have things I want to learn.

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. Everybody eats a lot, but if you eat so much that you throw up and get sick, you are special.

My employer bought me a Claude Max subscription. On heavy weeks I use 80% of the subscription. And among software engineers that I know, I'm a relatively heavy user.

Why? Because in my experience, the bottleneck is in shareholders approving new features, not my ability to dish out code.

goal? yeah. but in reality just timing it right (starting a session at 7-8am, to get 2 sessions in a workday, or even 3 if you can schedule something at 5am), i rarely hit limits.

if i hit the limit usually i'm not using it well and hunting around. if i'm using it right i'm basically gassed out trying to hit the limit to the max.

There’s absolutely no way that’s true.

In saas this is not true. Most saas is highly profitable or was i suppose because they knew that most of their customers would never max out their plans.

The article is about compute cost though. By "lose money on inference" I mean the assertion that inference has negative gross margins which a lot of people truly believe. This is important because it's common to reason from this that LLM's are uneconomical and a ticking time bomb where prices will have to be jacked up several orders of magnitude just to cover the compute used for the tokens.

You're missing costs.

- Amortized training costs.

- SG&A.

- Capex depreciation.

All the above impact profitability over various time horizons and have to rolled into present and projected P&L and cash flow analysis.

> which would imply that the money their making off it isn't enough

I don't think this logically follows. An unlimited buffet doesn't let you resell all of the food out the backdoor. At some level of usage any fixed price plan becomes unprofitable.

I agree the 5k cap is interesting as evidence although as you said I suspect there are other reasons for it.

But a simple assumption that Anthropic runs a normal large MoE LLM (which it almost certainly does) suggests that the actual price of running it (mostly energy) is pretty small.

Yea basically we have an app that’s like Netflix but for dogs, so people can leave on dog oriented shows for their dogs when they get kombucha or coffee

Same for me, but I suppose it is letting agents more loose and less checking of the code and rather throw away lots of generated output.

There is nothing in the TOS last time I checked forbidding it's use with Claude code. It's only forbidden to utilize it in the running of the business.

So getting Claude code subscriptions for developers should be permissable and not be against anything... However, if you created a rest endpoint to eg run a preconfigured prompt as part of your platform, that'd be against it

But I'm neither a lawyer nor work for anthropic

Surely that can't be true? The expectation would be that people pay $200 a month for building open source and personal hobby software with Claude?

If they believe a sufficient number is locked in then they may consider doing this later.

If that were true, then everyone I know is violating that tos

love tools like that. just type a few characters at your terminal and bam a small problem solved

Opportunity costs are real. In many cases they are more real than 'actual costs'. However, I otherwise agree with you.

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

Absolutely! Im currently paying $170 to google to use Opus in antigravity without limit in full agent mode, because I tried Anthropic $20 subscription and busted my limit within a single prompt. Im not gonna pay them $200 only to find out I hit the limit after 20 or even 50 prompts.

And after 2 more months my price is going to double to over $300, and I still have no intention of even trying the 20x Max plan, if its really just 20x more prompts than Pro.

You’re looking through the wrong end of the telescope. An investor is buying opportunity and it is a real cost to them.

Google's Antigravity has Opus access, and I suspect it's subsidised.

> production cost is nowhere close to selling price

When has production cost had anything to do with selling price?

> I find it likely Opus is larger.

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.

You can estimate on tok/second

The Trillions of parameters claim is about the pretraining.

It’s most efficient in pre training to train the biggest models possible. You get sample efficiency increase for each parameter increase.

However those models end up very sparse and incredibly distillable.

And it’s way too expensive and slow to serve models that size so they are distilled down a lot.

GPT 4 was rumoured/leaked to be 1.8T. Claude 3.5 Sonnet was supposedly 175B, so around 0.5T-1T seems reasonable for Opus 3.5. Maybe a step up to 1-3T for Opus 4.0

Since then inference pricing for new models has come down a lot, despite increasing pressure to be profitable. Opus 4.6 costs 1/3rd what Opus 4.0 (and 3.5) costs, and GPT 5.4 1/4th what o1 costs. You could take that as indication that inference costs have also come done by at least that degree.

My guess would have been that current frontier models like Opus are in the realm of 1T params with 32B active

Anthropic CEO said 50%+ margins in an interview. I'm guessing 50 - 60% right now.

Opus 4.6 likely has in the order of 100B active parameters. OpenRouter lists the following throughput for Google Vertex:

    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

Try 10s of trillions. These days everyone is running 4-bit at inference (the flagship feature of Blackwell+), with the big flagship models running on recently installed Nvidia 72gpu rubin clusters (and equivalent-ish world size for those rented Ironwood TPUs Anthropic also uses). Let's see, Vera Rubin racks come standard with 20 TB (Blackwell NVL72 with 10 TB) of unified memory, and NVFP4 fits 2 parameters per btye...

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 in 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.

Not only that, but since the release of 5.4 and 5.3 codex I've been running them in parallel and I've been let down by Opus 4.6 with maximum thinking way more than I've been let down with OpenAI models.

In fact I'm more and more inclined to run my own benchmarks from now on, because I seriously distrust those I see online.

Even if the benchmarks are indeed valid, they just don't reflect my use cases, usages and ability to navigate my projects and my dependencies.

imho they're mostly better at a subset of different tasks. I find codex to be better at reasoning through bugs and reviewing code when compared to Opus, but for writing code I find Claude a lot better.

Maybe that's just CLAUDE.md and memory causing the difference of course.

As a matter of preference however I like the way Claude Code works just a lot better, instructing it to work with parallel subagents in work trees etc. just matches the way I think these things should work I guess.

My impression as well, especially since 5.2 which I felt was on par or better than Opus 4.5

> Have they announced this?

No and indeed they have said they never do this at all.

inference compute is vastly different versus training, also it has to stay hot in vram which probably takes up most of it. There is limited use for THAT much compute as well, they are running things like claude code compiler and even then they're scratching the surface of the amount of compute they have.

Training currently requires nvidia's latest and greatest for the best models (they also use google TPU's now which are also technically the latest and greatest? However, they're more of a dual purpose than anything afaik so that would be a correct assesment in that case)

Inference can run on a hot potato if you really put your mind to it

I had a similar reaction to OP for a different post a few weeks back - I think some analysis on the health economy. Initially as I was reading I thought - "Wow, I've never read a financial article written so clearly". Everything in layman's terms. But as I continued to read, I began to notice the LLM-isms. Oversimplified concepts, "the honest truth" "like X for Y", etc.

Maybe the common factor here is not having deep/sufficient knowledge on the topic being discussed? For the article I mentioned, I feel like I was less focused on the strength of the writing and more on just understanding the content.

LLMs are very capable at simplifying concepts and meeting the reader at their level. Personally, I subscribe to the philosophy of - "if you couldn't be bothered to write it, I shouldn't bother to read it".

I think you're just hallucinating because this does not come across as an AI article

It's really unfortunate that we call well-structured writing 'LLM-isms' now.

I don’t see the usual tells in this essay

People care, when they can tell.

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...

It is certainly very obvious a lot of the time. I wonder if we revisited the automated slop detection problem we’d be more successful now… it feels like there are a lot more tells and models have become more idiosyncratic.

Maybe I'm misreading it, but I don't see him saying it's just the cost of *inference* alone (which is the strawman that the article in the OP is arguing against). He says:

> this company is wilfully burning 200% to 3000% of each Pro or Max customer that interacts with Claude Code

There is of course this meme that "Anthropic would be profitable today if they stopped training new models and only focused on inference", but people on HN are smart enough to understand that this is not realistic due to model drift, and also due to comeptition from other models. So training is forever a part of the cost of doing business, until we have some fundamental changes in the underlying technology.

I can only interpret Ed Zitron as saying "the cost of doing business is 200% to 3000% of the price users are paying for their subscriptions", which sounds extremely plausible to me.

That's claiming that worst case, a subscriber _can_ use that much. It's possible that's wrong too, but in any case a lot of services are built on the assumption that the average user doesn't max out the plan.

So the article's title is obviously sensationalized.

The $5k power user is the one that consistently uses all input and output tokens available under the Max subscription

Weird that they're all looking for outside money then