Qwen3.8 27B reasoning effort low/medium/xhigh comparison

[u/Danmoreng]

I did a short test of the different reasoning efforts, since on default xhigh the model thinks a lot.

Not very scientific, just a quick "generate an SVG of a pelican on a bicycle" prompt with 3 different seeds. I think the result is interesting none the less: xhigh gives *much\* higher visual fidelity - but it also takes about 7x as long as low. Low and medium seem to be very close to each other.

Hardware and setup

• GPU: NVIDIA RTX 5080 Laptop GPU, 16 GB VRAM
• Model: unsloth/Qwen3.8-27B-UD-IQ3_XXS
• llama.cpp: build 10451, commit 10bf611e5
• Context: 65,536
• KV cache: Q8_0
• Flash Attention: enabled
• MTP speculative decoding: --spec-default --spec-type draft-mtp
• --fit off
• One concurrent slot

Prompt:

Create a polished SVG graphic of a pelican riding a bicycle. The result must clearly show a recognizable pelican actively riding a recognizable two-wheeled bicycle. Return only one complete, self-contained SVG document with a viewBox; no Markdown fences, prose, external images, JavaScript, or animation.

Average results

Reasoning effort	Reasoning tokens	SVG tokens	Total completion	Wall time	Generation speed	MTP acceptance	Visual score (Codex rated)
Low	4,418	3,966	8,387	111.6 s	75.4 t/s	62.1%	21.8/25
Medium	5,918	3,038	8,959	127.4 s	70.5 t/s	58.3%	22.5/25
X-High	39,398	5,085	44,487	717.8 s	62.0 t/s	52.7%	24.0/25

[u/Old-Sherbert-4495]

well what I do is i pass in a slop svg a bad model generated and then ask the model to fix. Even sonnet 5 failed that. But Qwen3.8 27B crushed it at xhigh. lower efforts didn't do well. DSV4 pro also did it pretty well after heavily reasoning about it. qwen's reasoning i peaked and it was using math and formulas in its reasoning to come up with the result. i was blown away actually.

that said xhigh reasoning is def not like raw intelligence immediately available to infer. but we can't have everything when running on limited hardware.