Do you know how it compares to Qwen 3.8 27B? I really want to compare the distilled ones with harness versus the full MoE versions.

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incognito124 2 hours ago | parent | next [-]

Qwen 3.8 flash next is way better than 27B. It's so good I dont even use claude anymore

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mickeyp 2 hours ago | parent | next [-]

I have not tried Flash Next yet; but 27B is a cracking, little model. It is the first small model that I, as someone with 30 years of experience, can finally say is good enough to hand off small and mid-sized tasks and expect a pretty good result.

It is also a competent tool caller when quantised to NVFP4 for use with ninfer; my own harness only reports the occasional hiccup and it is only because the model will sometimes emit tool calling tokens in its reasoning loop.

	▲	snehesht an hour ago \| parent [-]
		This is interesting, thanks. - https://github.com/Neroued/ninfer

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snehesht 2 hours ago | parent | prev [-]

Yeah I agree, I'm running it with Pi didn't notice much difference compared to lower tier models and the speed, of course.

	▲	nicce an hour ago \| parent [-]
		I am running 27B with Deepseek Harness these days and somehow just by using it, without any parameter changes, the model feels even more intelligent.

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thatsabadlook an hour ago | parent | prev [-]

Significantly better for both performance and real world use case. 3.8 27b is a good small model. This is a good model.

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geye1234 an hour ago | parent [-]

I find 27B more accurate -- maybe because I'm running at FP8 instead of NVFP4? Flash Next starts making spelling mistakes when I get to 150K context or so. Also it sometimes ignores .md file instructions. Not sure if others have found that.

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PcChip 44 minutes ago | parent [-]

Spelling mistakes?

What inference engine are you using for flash next?

	▲	anon373839 14 minutes ago \| parent [-]
		Yep, can confirm that is NOT normal. Are you using Nvidia’s NVFP4 quant? There are other NVFP4s floating around but they are not as good. The quality of the calibration data really matters. Qwen Flash Next is just excellent, all the way to the very end of the native 262k context. (I haven’t tried YaRN scaling to 1M, so I don’t know about that.)