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Comment by PaulKeeble | Hacker News Reader

I have tried the Thinking cap and the Swift model for QWEN-3.8 27B and my experience was its not as smart and capable, the results on the same task were less impressive. For the amount of tokens saved it might very well be worth it but I wanted to run the xhigh precisely because I wanted the peak result. I haven't tested this but I suspect it ends up much the same trade off, in theory it does well in the benchmarks but in practice on your own tasks its not as capable.

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Yeah. I didn't find these any good either.

However, Gemma 4 26B is a reasonably good general chat inquiry model (it's also not too bad at commonplace code questions, if not as good for agentic code).

So this could be a good, fast model for that, in some situations; I may test it.

I still broadly just prefer working with Muse Glimmer, to be honest; we get along OK. I am trying out the Nemotron Bolt 74B/A9B MoE and I really do think it could be quite good, but it's still slow on Metal.

> I still broadly just prefer working with Muse Glimmer

Welcome to our very exclusive club of now two people! Ever since it came out I’ve been trying to find a better model, but just can’t.

It is hard to explain why it is better. Benchmarks etc., context length, it should be worse than Qwen 3.8 27B and I am sure for long context work it is.

But on my old M1 Max, with "Reasoning strength: low" in the system prompt, it's just kind of nice to use. It's not really slow (despite being a dense model it is faster to actual solutions than the Qwen 3.6 35B MoE), it writes well, etc.

So I use it as a developer-friend and researcher at the moment. Though it is actually the only model I've deployed code from! In general I am still writing my own (because I'm stuck in a bespoke framework on my main project) but it wrote me a plugin I needed for another app without any difficulty.

It's the first local LLM I've considered building hardware for.

This is my experience with Fireworks Ember-1 (reduced thinking Kimi K3)

It hallucinated and claimed task completion almost every session. Zero trust for this concept of fine tuning out excessive thinking, seems to do more harm than good. Eventually, as token vendors continue to commoditize, we will not care.

But Qwen 3.8 27b is unbearable slow, you get 10minutes of "let me think" over any trivial stuff.

Qwen 3.8 27B defaults to "xhigh" (its maximum level) for thinking, which makes it diabolically slow. Setting it to "medium" or "low" speeds it up a lot.

It does. It's quite good with the low setting. I prefer Muse Glimmer for, I dunno, hard to explain comfort reasons.

I had to hack the template of Qwen 3.8-27B to get it to go into low reasoning mode. Not sure if that is fixed.

Have you tried Nemotron 74B-9B? (Slow on Mac with llama.cpp due to a missing tensor type)

that nemotron model is such a dog, like all the other nemotrons

I have found it to be OK on sort of general purpose overview code stuff. At the moment it's too slow in llama.cpp on Mac for me to bother testing further.

There is a trade-off, but on Gemma we reduced thinking tokens by 39% on average for just a 0.15 percentage-point drop in average accuracy across 12 benchmarks. We'd encourage you to give this release a try.

For most folks in this thread running old M1 Max like me, look into MTPLX models (and the namesake inference engine).

I’ve built something custom that’s pretty darn fast but not ready for prime time yet. Might share it eventually.

Please share it. Fellow M1 (Ultra) user here

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