You're correct, but that's a different market segment and not the market GLM 5.2 and its peers compete in.

The labs are not interested in the small, fast, single purpose end of the market. Google increased their pricing on Flash so much that it stopped becoming a cheap model; instead, they released Gemma 4 open source, which is actually easier to use from a third-party inference provider than from Google.

From a total token volume perspective, these "utility" models (classifiers, simple summarizers, small OCR models) will absolutely drive enormous volumes of tokens, at low prices and margin and modest overall market size. Because the models are small and the performance requirements are modest, and because their use cases are specialized rather than general, there are poor economies of scale: they can run cost effectively on rented small GPUs, and a big player doesn't get a structural cost advantage. These models are usually 1b - 30b in size, and can run on a rented 5090. I've productized these myself: I run millions of pages through a fine tuned 1b OCR language model that runs on 5090s at a cost far lower than commercial providers.

But that's not the segment of the market where GLM 5.2, Kimi 3, etc., play. They compete with frontier capabilities, and they are not particularly cheaper than OpenAI models at a cost per task. (I do actually think they compete well with Anthropic, because Anthropic's model efficiencies are poor compared to OpenAI.) And although this part of the market may not be the bulk of the token volume, it is the bulk of the market value.

That's because a lot of human knowledge work is too generalized and fuzzy for dedicated, fine-tuned models, so they are almost entirely different markets that don't particularly compete with each other. (Though if SaaS companies successfully build around verticals that can use small models applied against well-defined jobs, there may be opportunity to push the small/big capability boundary to subsume marginally more valuable tasks that today would require mid-grade reasoning.)