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

It's not just the 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.

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%            |
  +--------------------------------------+-------------+-----------+------------------+

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.

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

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.

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.

- 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.

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

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.

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

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.

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.

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

    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

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.

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.

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.

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

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

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".

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...

> 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.

So the article's title is obviously sensationalized.