Forgive my ignorance but how is a 27B model better than 397B?

Reddit r/LocalLLaMA / 4/23/2026

💬 OpinionIdeas & Deep AnalysisModels & Research

共有:

Key Points

The post questions why a 27B-parameter model could outperform a much larger 397B model in practice.
It suggests that the advantage may relate to architecture trade-offs, such as dense models being stronger than MoE (mixture-of-experts) models in this specific case.
The author wonders what the additional “experts” in an MoE setup are doing if overall performance is still worse.
The discussion implicitly points to evaluation-by-efficiency and design choices (training quality, routing, and specialization) as likely factors beyond raw parameter count.
Overall, it’s a community-level curiosity and technical skepticism about how model size and architecture affect real-world results.

Is Qwen just incredibly good at doing dense and not so good at doing MoE?

I get that dense is generally better than MoE but 27B being better than 397B just doesn’t sit right with me.

What are those additional experts even doing then?

This article is featured in our daily AI news digest — key takeaways and action items at a glance.

Dev.to

Dev.to

Dev.to

Dev.to

Dev.to