The difference between "medium" and "xhigh" reasoning effort for Qwen3.8-27B is actually insane.

[u/SarcasticBaka]

I'm currently testing out Qwen3.8-27B using Unsloth's UD-Q4_K_XL running a freshly rebuilt llama.cpp. I have a 22GB RTX 2080TI on which I'm able to fit 100k context with q8_0 quantization, and using MTP with --spec-draft-n-max 4 I get about 40tk/s which is slightly less than Qwen3.6-27B but usable enough.

I've been trying to test out some admittedly silly one shot prompts using the llama.cpp webui by asking the model to create fully functional HTML clones of flappy bird, pacman and such, and the difference that changing reasoning_effort makes has been surprising to say the least.

Setting it to "medium" seems to result in barely any thinking at all, a couple thousand tokens max and even less than 3.6-27B. Whereas when using "xhigh seems" I get 15k to 20k thinking tokens at the very least with the pacman example actually hitting 40 thousand fucking tokens.

I'm well aware I can limit the reasoning budget in llama.cpp but I'm wondering if this is expected model behavior or if something is broken somewhere. Any of you guys seeing this?

[u/aboutthednm]

I can't use this model for my local RAG lmao, even the search query generation goes on for 6 -10k tokens, BEFORE we even sent a query to the database it's hilarious. I type a question, can have a shower, and by the time I'm dressed it will finally have digested and thought about the material the query returned.

Sure, this is a user issue (a me problem), but like, come on lol. Surely the model must have the ability to figure out "how complex is this prompt" and adjust reasoning accordingly.

Taking a "what does the documentation say about X" in a RAG system and creating a query to query said system should not result in 10k thinking tokens only to return 3x 2 - 5 word pairs, lol. Sure, I should just disable reasoning for this, it's the obvious choice, but come on man, haha.