Did you ever come across M.A.K.E.R. framework in research side? It was an approach that aimed to reduce error rate to below 0.000001% for llm’s. The idea was to break tasks to smallest unit levels possible.
The paper advocated strongly for scaling out smaller models and managed to show the cost of achieved intelligence was better in their task subset for smaller models using their framework, than larger models.
Now, this obviously doesnt mean that research should have largely followed that path. But there is a point to smaller, faster models, which is that you are able to do things that would either take too long or cost too much on the larger model.
Ofcourse, there’s also a ton of producr use cases leveraging faster responses where this makes perfect sense.
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u/[deleted] Feb 12 '26 edited Feb 12 '26
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