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andy99yesterday at 11:54 PM3 repliesview on HN

The big problem with overthinking on a dense model is obviously the speed hit you take. Going from Qwen 35BA3B to 27B for me is about 7-8x slower (should be ~9x?). This makes me a lot less patient for useless thinking tokens.

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.


Replies

simonwtoday at 1:30 AM

Comparing with Muse Glimmer is a good idea. I ran the same exact HTML tool generating prompt against both Glimmer 30B and Qwen 3.8 27B. Results:

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';
bogzztoday at 1:35 AM

I love reading Glimmer's "thoughts". Why use many word when few do trick?

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lostmsutoday at 1:44 AM

Glimmer is stupider than 3.6 27B. You can't compare its speed to 3.8 and be done.