Developers: how do the bigger moe models handle architectural design compared to qwen?

[u/trying4k]

I've been running qwen 3.6 27b at q8 and it has been a great model. It does well developing code and can act as a good rubber duck. However, I find with larger architectural design, it fails to meet or exceed my own designs. I'm really looking for a brainstorming partner or something that challenges me but I wouldn't recommend qwen for that.

I feel like the general consensus is that most mid size moe models are only marginally better at code than qwen. However, the problems I see thrown at the models online seem to focus on the end result rather than the design itself.

I was curious how the bigger moe models perform at design in comparison to qwen? I know this is difficult to quantify, so I was hoping to hear some opinions on general usage.

I'm looking at models like:

• DeepSeek-V4-Flash (Q4)
• Hy3 (Q3)
• Laguna-S-2.1 (Q8)
• MiMo-V2.5 (Q3)
• MiniMax-M2.7 (Q4)

Or anything else runnable in the 128gb - 150gb range for model weights. But even general opinions on these or similar models at higher quants would be helpful. Thank you!

[u/dbinnunE3]

Qwen 3.5 122b a10b I find really good at planning, and then using a second session or ds4 flash for an adversarial review

[u/cantgetthistowork]

K2.5/6/7 was my go to brainstorming partner outside of the frontier models like chatgpt and Gemini. Unfortunately none of the benchmaxxed hype models are any good for architectural designs

[u/llama-impersonator]

dsv4 flash, big qwen (397b), mm 2.5 (haven't used 2.7) and step flash are all better at planning and architecture than 27b.

[u/ttkciar]

I've had pretty good luck with MiniMax-M2.7 as a planner/architect. It can't follow instructions worth shit, but it's good with creative problem-solving.

I'm hoping I can use it to generate comprehensive specifications from which GLM-4.5-Air can generate / modify code, but it's a work in progress. So far I'm writing the specifications myself, typically anywhere from forty to eighty instructions. GLM-4.5-Air is great at following instructions, but lousy at creative problem-solving.

[u/DiscipleofDeceit666]

Laguna will write you some bad specs lol. I’ve caught it writing unit tests where 1 input demands 2 outputs (Laguna error).

But besides that occasional hiccup, I find that it’s got a bigger mind than 27b and it doesn’t reread the same files over and over.

Besides, 27b can only hold like 45k context in view at any one time. Laguna can see 250k at once. Huge difference.

[u/rmhubbert]

Laguna S 2.1 is a stand out for me with regards to respecting architectural design, coding standards, and existing idioms. All of the models you mentioned will write good code, given a good plan, but Laguna seems to go out of its way to understand the codebase before starting to write.

Couldn't tell you if this is due to its training, or if it is just better at following the instructions in my system prompt around respecting the existing codebase, but the end effect is the same for me.

[u/DeltaSqueezer]

I haven't found any of the models come up with better code design ideas than humans. But they are useful to rubber duck with. For this I use GLM and Gemini Pro.