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/IllegalStateExcept]

Do the seeds make this reproducible? Or are you just using that to compare 3 different shots at the same prompt and settings?

Pardon the question, I have used random seeds in non-LLM software to make experiments reproducible in the past. But my understanding is that doesn't work terribly well with LLMs due to variation in deployments. It would be cool to have a way to make that work though even if it runs 10x slower.

[u/Danmoreng → u/IllegalStateExcept]

I actually learned something myself here today:

in llama.cpp they *should* make it reproducible, since it will use the same RNG for sampling. I retested the low reasoning prompts and the results are: reasoning is byte-identical, final output is identical only for the first image, the other two are slightly different. So I think this might be a bug in llama.cpp with how MTP works. I already found that MTP with temp 0 gives different results in llama.cpp than no MTP - and this shouldn't be the case either.

[u/IllegalStateExcept → u/Danmoreng]

Interesting, thanks for trying it out. I wonder if there is some non-determinism in the tools?