31–40 of 246 posts
Didn't expect it to beat 3.8 27B so cleanly. Opus 4.6 Max self-hosted at 30 tok/s on a 5k Macbook in Aug 2026. The LLM timelines are crazy.
For comparison with hosted models, GPT 5.6 Luna scores 67% on DeepSWE, compared to 59% here for Qwen. Luna is $0.20 / $1.20 vs $0.16 / $0.47 with Qwen.
It looks like this also undercuts the already absurdly inexpensive Deepseek Flash in pricing. Wild.
DSV4 Flash 304B params, 167 GB download (at full size)
Qwen3.8 Flash Next 180B params, 360 GB download (at full size)
It looks like this also undercuts the already absurdly inexpensive Deepseek Flash in pricing. Wild.
Qwen 3.8 flash: $0.16 / $0.47
Compared to
Deepseek 0723: $0.03 / $0.075
(units in USD/m tok)
Adding to my homelab stack, hopefully it doesn't overthink like the little model. Actually, hoping it thinks a bit less. Wait actually I'm really praying it reasons a bit more directly. But wait, I'm really sure that it must be a bit better.
Also, heating my home during the winter is nice.
Oh, also, I use llamacpp with --reasoning-budget; very simple way to move on.
6B active params helps around the memory bandwidth constraints, but a 128GB box can probably run the Q3/Q4 quants fairly easily with a decent context size. This might actually be better for strix users than 27B, which was already very good.
FYI: nothing seems to be able to run this (easily) yet. llama.cpp, vllm etc I couldn't get working because of no support in the mainline version.
How is input token efficiency/verbosity on this model? Has anyone tried? GLM 5.2 was doing lot of turns and thinking piling up input tokens in the context (compared to Claude and GPT models). Then Qwen3.8-27B was 2x of that. Both delivered good output results but those cumulative input token costs were not cheap. Note this is on our specific business workloads. Genuinely interested in other people's experience (if yo…
It's new arch demo for future Qwen 4 family, but (as I understand) training recipe/data is same as any other 3.8 model.
Earlier quoted context omitted.
For comparison with hosted models, GPT 5.6 Luna scores 67% on DeepSWE, compared to 59% here for Qwen. Luna is $0.20 / $1.20 vs $0.16 / $0.47 with Qwen.
This is a good counter argument. But you have to note that this is after OpenAI cut Luna costs by 80%. If you compare launch pricing, Qwen probably comes out ahead on a cost-performance basis.
It looks like this also undercuts the already absurdly inexpensive Deepseek Flash in pricing. Wild.
Where are you seeing that? At the bottom of this post from Qwen I see: Qwen 3.8 flash: $0.16 / $0.47 Compared to Deepseek 0723: $0.03 / $0.075 (units in USD/m tok)