If Dense Models are better for Coding, why are Qwen-Coders MoE?

Reddit r/LocalLLaMA / 4/12/2026

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The author questions why Qwen’s coding-focused models use MoE (e.g., 30B MoE and later an 80B sparse MoE) even though recent dense models reportedly perform better on precision-heavy coding tasks.
They ask what the MoE “experts” are specializing in if they are not simply separate language/syntax experts, and what architectural choices motivated not using smaller dense baselines like the 27B or 9B.
The post hypothesizes that inference speed and throughput tradeoffs (pipeline parallelism and token generation rate) may be a major driver for choosing MoE over dense models.
The author also expresses disappointment that a smaller coding model (e.g., around 14B) was not released to run efficiently on limited VRAM setups with quantization and reasonable context length.

Hi all,

have been reading here for over two years and finally have a question I can't find an answer to.

Qwen 3.5 27B and Gemma 4 31B have been the latest examples of dense models performing much more accurately and in general tasks requiring higher precision, where vast knowledge isn't of highest priority. Hence, I wonder what specifically made Qwen (as the only known developer of coding-specific models) choose their 30B MoE, and the subsequent 80B A3B super-sparse MoE, as the suitable architecture to fine-tune into a coding model? What are these models using the experts for, I certainly don't think each expert is their own language/syntax...

Why did they not proceed on the 27B for example? Or even the 9B dense?

I can only assume it has to do with inference speed, both PP and TG is certainly much slower on the dense models. I am hence even more sad that they didn't release a 14B successor, something that could run on 16GB VRAM quantised with ample room for context.

Any insight would be highly appreciated.

submitted by /u/LocalLLaMa_reader
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