For comparison, I also used GLM-5.3, Muse-Spark etc, with no significant improvement for ordinary/BAU coding tasks.
I did had it forget some stuff over long runs that went through several context compactions and i'd expect a larger model to be able to handle more vague instructions or have wider breadth of knowledge (if you're trying to make Qwen 3.8 27B do stuff outside the mainstream you need to provide it with some reference material to use) but i never had a problem with it not following instructions.
The harness might matter though but i'm using plain Pi (which is a very barebones harness with a very small system message) without any addons.
Would something like LMCache help here?
Local LLM can't get even closer to frontier ones. It's just bragging. There's a reason why models like Astra have 2 orders of magnitude more parameters (and run on specialized clusters).
Local models like Qwen (flash) are much slower, they have much less room for context, and most importantly (people forget to mention) they produce much buggier code. Ah, Qwen also goes into loops sometimes.
Having said, no doubt that Qwen-flash-next is considerably better than Qwen-3.8-27b - but on the other hand, it's several times bigger. Deepseek Flash also keeps getting bigger.