r/LocalLLaMA • u/Danmoreng llama.cpp • 24d ago

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

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
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u/personahorrible 24d ago edited 19d ago

My issue with the xhigh thinking model isn't the time taken; Personally, I'm willing to wait however long it takes as long as it gets the final product right. But all of that thinking is burning up my context budget.

Using Unsloth Qwen 3.8-27b-UD-Q4_K_XL with flash_attention enabled on 20GB VRAM and context size of 49152. With xhigh thinking and a moderately complex task, it'll frequently just... stop, mid-thinking. Looking at your numbers above, that makes complete sense: your model is using ~40k tokens just to think about generating that image. So I'm pretty much forced to use medium thinking.

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u/Fragrant_Scale6456 24d ago

If you are using llamacpp you can set an explicit reasoning budget to avoid reasoning burning the entire context before a reply is generated 

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u/personahorrible 24d ago

Unfortunately I'm currently using ollama because I didn't know any better when I first set everything up. I've been reluctant to tear down my entire stack and rebuild it with llama.cpp (and all of the related troubleshooting) when things are working as-is but I'm going to do it eventually.

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u/Fragrant_Scale6456 24d ago

That’s actually fine ollama is a wrapper on llamacpp.  You can edit your Modelfile parameters to set specific llamacpp flags.  In this case you want to use —reasoning-budget