[tensegrist]

> On the Intelligence vs. Cost per Task Pareto Frontier: GLM-5.2 is on the Pareto frontier of the Intelligence vs Cost per Task chart, with the lowest cost per task among models at its intelligence level. GLM-5.2 costs ~$0.46 per task, compared to GLM-5.1 ($0.25), Kimi K2.6 ($0.31), MiniMax-M3 ($0.18) and DeepSeek V4 Pro (max, $0.05)

am i missing something?

[OtherShrezzing]

I think they’ve just picked poor peer examples. Instead of choosing other models near 5.2 on the intelligence scale, they’ve picked some open models from further down the scale.

[xiaoyu2006]

Some models are heavily subsidized. Total params & active params are better measurement of inference cost.

[simianwords]

No models are subsidised -- there are lots of third party hosting services that will still run at breakeven/profit. (except Deepseek after discount)

[stymaar]

> No models are subsidised

We have no proof in either direction, it's not like we had access to their financial numbers in details.

And the pricing itself muddies the water, as input tokens that are already in the KV cache are practically free for the provider, whereas other tokens are expensive. So they could still make money overall thanks to people having multi-turn conversation (and as such, paying multiple times for the same token), but lose money on actual compute done.

> there are lots of third party hosting services that will still run at breakeven/profit.

How can you be sure that they are making profit directly from token price, and are not billing at marginal cost (i.e. electricity price, without counting the cost of the GPUs) and aiming to make a profit later on from the valuable training data that they are collecting in the process?

[simianwords]

> How can you be sure

You are free to believe that they are doing all this. Or you can simply believe the intuition that models are getting cheaper by the day. I can run Gemma 4 31B from my laptop today.