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Comment by nubg | Hacker News Reader

As much as I want local and open-weights models to succeed, nothing beats a paid frontier model for now. Anybody who claims otherwise is simply not a daily user of such models. So this "investor" here should invest sime time in actually using the various LLM models and get a real taste of what it's like.

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Their point isn’t that local models are better or even as good more but that if you can do 50%+ of tasks with local then that’s 50% of tokens that aren’t captured as compute done in data centers.

> As you can see, on average, SLMs are as good if not better than LLMs in 81.2% of the cases, with the LLMs having a significant advantage only in areas like engineering, life sciences, transportation and computer sciences.

So what I’m reading here is “LLMs have a significant advantage” in the most critical areas that have practically infinite demand for more intelligence.

The article is kinda dumb, and yes this is clearly the area where frontier models having and advantage matters the most, but I'd point out that these smaller open-weight models are performing better than the big Frontier models of just 4-6 months ago.

This means that the Frontier labs are under immense pressure to maintain that lead, and could end up in serious trouble if they stumble at all.

The other thing id point out is that a lot of us who are token-sensitive do things like build plans using expensive, smart models, and then execute those plans using cheaper dumber models.

Then there's the fact that we are still in the age of heavily subsidized Frontier subscriptions + tokenmaxxing initiatives from megacorps. Neither of which are sustainable, and will drive more usage to smaller open models once they end.

While that's true, the open / local models are getting good enough. Given time and the technology trend people may prefer a private local model for most use cases. Nobody is arguing that a Ferrari isn't a faster car, but the Honda is the more practical choice.

You completely missed the thesis here, and that is supported by the numbers being presented. It is that a large share of ordinary inference can be routed away from the hyperscalers.

How does a current local model compare to the best frontier model 12 months ago. Or 24 months ago?

It beats a frontier model from 12 months according to this bench: https://news.ycombinator.com/item?id=49334544

It is not the whole story, and knowledge is very lacking, but it has gotten a lot of attention. That model together with DeepSeek V4 Flash are the highlights of this summer on the open/local models side.

I have found qwen 3.8's coding quality using opencode to be similar to claude or gpt from 6-9 months ago, except much slower.

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