> The distillations of Qwen 3.8 down to a 27B model are good, but they're not on a par with frontier models.

Does it have to be? There are plenty of coding tasks, where it's good enough.

Practically, no, the distill is great. It's fine to use it.

However, if you're having a discussion about access to frontier models, and using Qwen 3.8 as an example of how open weights is a solution, then you should be honest and accurate about what you're talking about. Making an argument like "People can run Qwen 3.8 at home. That shows open weights are great." is a bit disingenuous if you're not also making it clear that you're not talking about Qwen 3.8 Max (or that you have a beast of a PC at home :D ).

	▲	woadwarrior01 3 hours ago \| parent [-]
		I agree 100%. IMO, it all started with ollama misrepresenting the Deepseek R1 distills as Deepseek R1, all for hype and marketing. I've had so many ostensibly technical people telling me: "I tried DeepSeek R1 and it was terrible", and every time when I probe further they'd tried the tiny 1.5B Qwen2.5 distill model that was further brain damaged by ollama's naive RTN quantization[1]. DeepSeek themselves were very forthright about it by naming it DeepSeek-R1-Distill-Qwen-1.5B[2]. I suppose the road to technical hell is paved with marketers and grifters. :) [1]: https://ollama.com/library/deepseek-r1:1.5b [2]: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-...

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Pavilion2095 2 hours ago | parent | prev [-]

> coding tasks

Exactly. There are common coding tasks that these models can adequately do. They are absolute trash for anything that isn't coding. And even with coding, they are so, so far behind frontier models.