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Comment by boogerlad | Hacker News Reader

> I’ve been running Kimi K3 alongside Claude on my normal coding work, and for all practical purposes I can’t tell them apart

When you say "Claude", do you mean Opus? Fable? What effort level?

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This is comparing Fable High with K3 High. I'm mostly using these models for game development. The tasks I usually send are ambiguous visual bugs, changing the look of a scene or models, or adding a large feature. The wording wasn't accurate there. I don't use Fable or K3 most of the time. I'm usually working on smaller scoped tasks that I review myself afterwards.

Please update the blog post to clarify. “Claude” is not a model, and your writing makes no sense without specifying.

There’s probably some other things that weren’t exactly clear or could be misinterpreted in the post. I don’t see it as worth my time to go back and edit each little thing.

This line made me think by 'normal coding work' the author means doing something they don't understand well enough to be able to distinguish the models' output.

Steve Yegge calls this is the "discernment horizon" - https://steve-yegge.medium.com/the-flat-curve-society-36c8b0...

Interesting, thanks for sharing.

Although in the month since that most recent post, his other points about open models are undercut by K3.

And at least of data available as of 2026-01, AI compute capacity was doubling every 7 months, so I expect every major country to host AI compute farms, and self-host AI feasibility to majorly increase in the next 2-3 years as well. (Partially undercutting, but not fully disproving, his points.)

And I wish his posts were 4 times less wordy.

I think a better framing is the marginal utility of the models capability growth. At a certain point frontier models will only be needed for frontier problems. The demand for that capability will decrease with time. The hand wringing about not understanding is to my mind anthropomorphic - AI of today lack agency and awareness. Even the constructed stuff Anthropic puts out there in the model docs involve contrived scenarios to elicit “scary” behaviors. It’s unclear that as models become more sophisticated whether they’re better at instruction following or not but it certainly feels that way - even if it’s through better alignment or just an artifact of scaling. However I think the malign actors of humans using powerful models for bad stuff isn’t unreasonable to be concerned about.

The marginal utility problem is a real one for AI companies. I think the current generations are already saturating marginal utility for 95% of the population. Almost everyone I know outside of my career has no use for a more powerful model. This is a serious problem for the economics of AI and semiconductor investment. This is a bigger problem than Chinese models. It leads to a demand curve problem - that supply outstrips demand.

I've been trying Chinese models, like GLM 5.2, as substitutes for Claude or GPT5.X, and my experience is that they underperformed them in real world metrics like prompt adherence, hallucination or code quality, even though they benchmark better.

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