That seems unlikely. There are many providers for open models on openrouter. It seems unlikely that they are throwing money away for each token they sell.

Also, there a good technical reasons for inference being much more efficient at scale.

The blog compares the cost of running Gemma4 31b, which on OpenRouter is offered by small no-name inference providers, not by frontier AI companies. It seems like a fair comparison to me.

So many more efficiencies possible at scale though. I cannot keep a local model 98% utilized 24/7, at least not with my current workload. A big cloud can. I can’t power my servers with DC, I have this AC to DV conversion nonsense. The list goes on.

This is not true. API tokens are not sold at a loss, and hardware gets more efficient over time, so serving inference on the same model gets cheaper. LLAMA 3.1 405B parameters was $6/$12/M tokens in 2024, but in 2026 that same model is $3/$3/M tokens.

The most intelligent model at a given time is much larger than the previous, which is why token costs for GPT5.5 are higher than 5.4. But you should expect that 2 years from now, serving a GPT5.5 sized model will be cheaper than GPT5.5 today. You should expect it to be even cheaper to get an equally intelligent model 2 years from now, because distillation techniques are effective at reducing the necessary parameter count for the same benchmark scores.

Do you have a proof? Anthropic’s CEO said they Are profitable. Same with OpenAI.

The models have been dropping 10x in price for completing the same tasks, year over year. Even if you think Anthropic is losing money charging 10x more than everyone else for their 400B model, the prices will continue to go down based on model improvement alone

It is the model training that is dragging them down.

If the arms race stopped tomorrow the current price pays for the inference.

https://old.reddit.com/r/GithubCopilot/comments/1tbb5bj/gith...

Seems to be on its way! I know of at least one person whose company is looking at a 20x increase, and afaict (from related looking around, nothing concrete tho) business accounts are missing some costs in the calculator so it'll likely be higher.

You should probably take some stay-on-topic pills, as this article is clearly and unambiguously talking about open weight models (e.g. gemma 4), not the ones allegedly being sold at a loss (Opus, ChatGPT, etc)[0].

[0]: these API are not sold at a loss either, by the way. But it's a nice meme so let's just pretend they are.

Except that’s not what the analysis is. They’re spending < $1 to get $1 from you and the other $9 to figure out how to improve the model further and build up products on top of that to turn that $1 spend into $5 in the future.

In other words, inference is fairly profitable for them and the rest of the money is spent growing revenue as quickly as possible. Building models is still an expensive line item but the costs for that are going down with time.

There is also maybe a “capture the market” mentality but I don’t think that’s necessarily it - the tools and processes are largely fungible and that’s a huge problem. They need to figure out how to make it sticky for “capture the market”, but there’s also a very real “grow as big as possible as quickly as possible to take on Google”; Google has an existential threat here.

Well, I'd be surprised if non-R&D inference providers were selling at a loss. There are a plethora to choose from, competition is quite healthy. Will they keep providing cheap tokens while the labs raise their prices? Probably, but then I don't see how they could be raised in the first place. And what timescale are you talking about? A couple of years? It is appropriate to assume inference will become more efficient over time. If you raise your prices, you are going to be out competed before it's profitable (if you assume it is unprofitable) which would be negligent. I don't see how this makes sense.

The Michael Scott AI Companies.

These models will become more expensive over time, it's functionally impossible for them not to, they just want to capture the market before they have to stop selling at a huge loss.

They could have said the same about transistors. People keep inventing new ways to keep the costs down. Just look at the latest Qwen, DeepSeek, BitNet. Interesting tidbit: they’re all open, and as Google said in 2022: they have no moat.

The inference is absolutely not sold at a loss, at least not when paying API prices (the subscriptions are less clear). The reason frontier model companies aren’t profitable is because training the models is so costly, not inference.

> Frontier AI companies are selling at a loss.

How big/deep of a loss?

I feel like I read this every day for years that Uber did this same "idiotic, losing" strategy (how it was pitched/discussed) and then one day we woke up and... without much fuss, boom, they were profitable seemingly overnight.

> I feel like I'm taking crazy pills.

Why? It's no less crazy than when Uber and Lyft were doing the same thing. Or when the entire tech industry was doing it in the dot com boom.

Investment-driven market growth at a loss is like the least surprising thing in all of this. The tech is new and fascinating. The bubble is just another trip through the funhouse.

> Frontier AI companies are selling at a loss.

There are huge economies to be had by batching requests and using lots of RAM for MoE (sparse models). You can't achieve that efficiency at batch size 1 on a single node.