I used Qwen 3.6 27B extensively (>1B tokens) and DeepSeek V4 Flash (the older one also 2B+ tokens).

And I just can't fathom that the new 3.8 beats the new DeepSeek V4 Flash (which, in my eyes, is one of the best everyday coding models).

What an insane release, and convenient size to use every day/locally.

but i will test this model extensivly.

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drob518 3 hours ago | parent | next [-]

I’ve been using v4 Flash 0731 a lot lately and you can’t beat the price performance. That said, it sometimes takes my prompts as more of a suggestion than a directive. I’ve found that introducing a reviewer subagent (even with the same model) helps push it back to what I’ve asked for. But makes every coding session a back and forth: “do X” -> “use a reviewer subagent to analyze whether you really did X as I asked”.

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Saris 3 hours ago | parent [-]

What model do you normally run the subagent on? You mentioned flash as well for that, but I wonder if a more 'strict' model would do a better job at pushing the main back on track.

	▲	drob518 3 hours ago \| parent [-]
		For cost reasons, I’ve been using Flash for the reviewer, too, but I plan on trying to use Pro for that. Thus far, however, Flash has been doing well at reviewing. I’m cheap as I’m paying for all the tokens myself.

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f311a 3 hours ago | parent | prev | next [-]

How is the general knowledge of Qwen 3.6? Do you need to explain things outside of algorithms to it? Since the size is so small, I guess you need more explanations to it. General knowledge helps with coding when your don't specify a lot of details and ask for big changes.

	▲	SwellJoe 11 minutes ago \| parent [-]
		It researches what it doesn't know, just give it a web search tool. It searches unprompted, if it can. It's impressive.

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algo_trader 3 hours ago | parent | prev | next [-]

> I used Qwen 3.6 27B extensively (>1B tokens) and DeepSeek V4 Flash

Were your opinions effected by the harness ?

DS is an amazing combo. It probably could only happen in China, not in current USA or EU (for different reasons)

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ignoramous 3 hours ago | parent [-]

> It probably could only happen in China, not in current USA or EU (for different reasons)

Per Artificial Analysis benchmarks, Meta's Muse Glimmer 30b (open weight) holds its own (for agentic code workloads) against models 5x to 10x its size, too.

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skohan an hour ago | parent | next [-]

Glimmer benchmarks around Qwen 3.6 27B levels no?

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kube-system 2 hours ago | parent | prev [-]

and Muse Glimmer uses a lot fewer tokens than Qwen 3.8. I found it more usable on my hardware because I can get an answer quicker.

	▲	skohan an hour ago \| parent [-]
		I found Glimmer underwhelming in terms of coding - I tried it as a drop-in replacement for 3.6, and the output was noticeably worse. 3.8 has been a significant step up so far from early testing.

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JacobAsmuth 3 hours ago | parent | prev [-]

It has double the active params.

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K0IN 3 hours ago | parent [-]

yeah but using the rule of thumb (I see floating in the Internet) which is sqrt(#params * #active params) which would give sqrt(284B*13B) = 60B, so deepseek should perform better.

	▲	apitman 3 hours ago \| parent [-]
		Seems like that equation really should weight total params and active params differently