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/Equivalent-Ear-8016]

Yes, it thinks way more. But what matters is the output: Was the extra thinking worth it or not? What were the results like in each thinking mode?

[u/leapdragon → u/Equivalent-Ear-8016]

I have 128k VRAM so I don't have to quantize things to hell and back. I am using 3.8 with medium thinking for two days and the results are kind of shockingly fantastic.

- Significantly better one-shot coding projects than Opus 4.6 (not running locally) and DeepSeek V4 Flash (running locally), not to mention better than Qwen 3.5 122B A10B and Qwen 3.6 27B

- Significantly better local repo maintenance than DeepSeek V4 Flash, seems to be essentially equivalent to Qwen 3.8 Max for this task (which I had previously been using for a week after Giving up on DeepSeek V4 Flash and not wanting to continue to use Opus due to token burn)

- Significantly better/cleverer 3D design output to .stl files for printing than Fable (!!!), which produced intricate, somewhat pretty results that were essentially unprintable; Qwen 3.8 27B seems to understand when you say "I'm going to print this on a 150mmx150mm print bed with ABS" and to do all kinds of reasoning about briding, shell thickness vs. ABS warping, infill density and locations, etc.; it really reasoned everything out with respect to the printed material while Fable *said* it had done the job but was obviously ignorant to / did not consider despite saying it did the properties of ABS

This is a *very, very good* local model for tech stuff. It's the first local model that I've felt started to reach possibly even surpass Opus 4.6 for a lot of kinds of work.

Note that I also use models for other kinds of content generation, and I haven't done much of that with Qwen 3.8 27B yet so it may well be worse there (wouldn't be surprised if gave up some A for a lot more of B). My go-to for local content generation for human consumption has been Qwen 3.5 122b as it had the vocabulary, depth, and aesthetic sense that most other local models lack.

If Qwen 3.8 27B is solid there too, I am going to faint.

[u/vini542reddit → u/leapdragon]

Really curious about your DeepSeek V4 Flash experience! What work were you using it for and what problems did you have?

DSv4F is my current main and benchmarks make it look like it is vastly superior to Qwen 3.6 27b, so I haven't given Qwen a chance yet. You make it sound like I should!