I’d want to compare this to the new Muse 30B model which is super terse and has a whole different way of thinking (no “Wait,”) and in my experiments was way more token efficient to the point that the absolute tok / s didn’t really matter.
Qwen: https://gist.github.com/simonw/121ad098860028b2fab603fa12da1... - 17,576 reasoning tokens, produced this HTML result: https://static.simonwillison.net/static/2026/qwen-over-think...
Glimmer: https://gist.github.com/simonw/51e8ddb2ee597a5005fa63bd4927d... 1,021 reasoning tokens, this HTML: https://static.simonwillison.net/static/2026/glimmer-bbox.ht... - ugly but functional.
In both cases paste in the URL https://static.simonwillison.net/static/2026/two-pelicans-on... to see them work.
Both applications work correctly and fulfill the requirements. The Qwen one (which used the default xhigh reasoning setting) is massively over-engineered. The Glimmer one used whatever their default in LM Studio is and I would argue is a tiny bit under-engineered.
Weirdly the Glimmer one doesn't work with images on other domains like https://static.inaturalist.org/photos/714731804/large.jpg - it fails with a CORS error, but you don't need CORS to load images and detect their width and height, and the Qwen one handles that URL just fine.
That's because Glimmer added this unnecessary line:
img.crossOrigin = 'anonymous';https://gist.github.com/nharziro/aed0c364ce2f295a493494c6f1b...
For instance, it's a step backward, but I put {"reasoning_effort":"none"} and led it by the nose:
User: We're going to make <silly demo>. Please create a plan, but do not write code yet.
Agent: <short and reasonable plan>
User: Now please follow that plan and write the code. No other chat.
Agent: <reasonable code in reasonable time>
You can disable it. It's well known issue in Qwen, previous releases I would disable it by default.
Also xhigh seem a new thing.
the message can be some combination of tool calling, summarizing, etc. It's overthinking often is a bunch of recursion, so simply stopping t and redirecting is all you need to do.
If someones building a harness for llamacpp, you can set this per message, so it's possible to dynamically control it by watching for the expansion of the thinking traces, and redirecting it.
I use the message to tell it to use subagents, add additional logging and to use opencode's dynamic context pruning.
As such, we'll just whisper here _skill issue_.
Probably the better solution if you want it to be quicker but still fairly thorough appears to be to configure reasoning effort instead of thinking budget. It seems to do very well still even on the Low setting; on the Medium setting it can get stuck in loops like 3.6 does.
I think xhigh reasoning effort was an absurd choice for a default, and so was not sorting out the chat template so LM Studio could offer the reasoning effort dropdown.
The only think I could think that'd be better than the --reasoning-budget would bet a budget jitter just in case it really is repeating a pattern and you want to escape it arbitrarily, otherwise yes, it could keep looping if you're always cutting at the wrong time.
Yes, I think I finally have an intuitive sense for that. But surely on a longer prompt it is still better if the thinking has at least brushed past all of the prompt?
One of the things I witnessed with xhigh is that while the thinking trace starts out intending an overview of the prompt, it actually can go fully down a rabbit hole off one of the first two or three bullet points even when it was seemingly intending not to.
It’s basically a lot like me. Gets sidetracked by the interesting bits.
Your strategy would likely help in medium reasoning effort (because there it gets caught up in the very typical Qwen looping).
Not seen looping in the “low” reasoning effort mode.
I've learned that medium effort can improve the outcome relative to higher settings. But I suspect the phenomenon is an artifact of a misguided effort to fix inherent LLM limitations. At least some of its reasoning will miss the target, and more bad reasoning is not the remedy.