On narrow domains, it is very common for small models to match or outperform larger ones at a fraction of the parameter count.
replyFor example in language, this is called the “curse of multilinguality”. Small models that handle a single translation direction can easily outperform big ones that try to handle them all.
https://arxiv.org/pdf/2311.09205
In any case, for most tasks the question is not “how many tasks can this model kind of do well” but “given time/cost constraints, what is the maximum level of quality we can achieve”. And for that, small models are usually very competitive.
I think the context here is that small models run locally, not rented from a cloud.
replyYes small models are and will be useful for lots of stuff for several reasons.
replyBut the idea they’d be better than a bigger model is cope, you’re pretty much always better off running the biggest one you can bring to bear within your constraints.
Fable 5 is actually a lousy writer. Opus 4.6 is the best for writing and prose assessment. Gemini 3 is smarter at reading comprehension but tends to be more unstable in judgement.
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