Comment by stymaar
17 hours ago
I just tried to reproduce in Llama-cpp's webui: with the following (reverse-engineered) prompt:
“Write a complete short story scene of 2000-3000 words. Use a third-person limited perspective focused on a character named Rowan. The scene must include the following plot elements: a raccoon, Milo returns, a storm power failure, an elderly customer knocks, and a thermos somewhere. Make sure the scene ends feeling finished. Output only the story; do not include any outline, preamble, analysis, or meta-commentary.”
And here's Qwen's thinking trace:
> The user is asking for a short story scene of 2000–3000 words. Third-person limited POV, Rowan. Plot elements: a raccoon, Milo returns, a storm causing a power outage, an elderly customer knocks on the door, a thermos. End with a sense of closure. Output only the story.
> Let me plan carefully. I need to count words in my head—roughly 2400–2600 words would be safely in range. Let me draft.
> Setting: a small-town shop or a gas station/diner where Rowan works the night shift. Milo returns—Milo is probably Rowan's partner, brother, or ex. Let me make Milo Rowan's brother who's been away (military? a road trip? rehab?). Better: Milo is the younger brother who disappeared/went away after a fight. He comes back in the middle of a storm.
While at the very beginning it rephrases the prompt in very short sentence, it's nowhere near close to the above GPT thinking trace and it quickly moves to plain sentences.
Yeah, I dunno. For me it does "talk normally" for the most part when used in an actual coding harness.
One thing though, the actual prompt I used was pretty long (844 words), and ... generated by GPT-5.6 Sol (lol), with the intent of "benchmarking" model performance in being able to write stories where the model avoids explicitly stating every detail in the prompt.
I wonder if the GPT-produced stream could steer the generation into GPT-think territory. That's all I've got, though.
Then there's the actual geometry problem from the stolen thoughts paper:
It doesn’t use the caveman speak unless reasoning is set to xhigh, in my experience. But I don’t know if it has always been coincidental.
> unless reasoning is set to xhigh
That's the default and I'm sure almost everyone else is also using it because other reasoning efforts yield subpar results from what I've seen.
It is the default, which is insane.
I think it is clear that medium reasoning has more 'loopy' results like the older Qwens, but I actually think the low effort results are usually more appropriate.
If you plan to one-shot and vibe code AI slop to meet benchmarks, maybe xhigh makes sense. But if you want a responsive agentic coding assistant it is, to me, quite evidently the wrong choice, especially on modest hardware.
I have seen xhigh radically distract itself with rabbitholes and write considerably worse code than low.
It is my own opinion only, but I think much of the fuss about squeezing Qwen 3.8 27B into small local hardware setups, Macs etc., is a bit misguided.
There's too much focus on its benchmark scores, its one-shot capability, canned demos etc.
For my own needs Muse Glimmer (again on reasoning strength: low) is shaping up to being the more practical agentic tool. It is considerably faster than Qwen at solving real coding tasks.
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The person evaluating and noticing similar reasoning traces to gpt is because they are using a coding harness which probably has a different system prompt to llama webui which primarly serves as a chat interface
They said literally the opposite in their message above. In their experience, the caveman speech occurs in chat ui, not in coding harness.