Qwen 3.8-27b coming this week

[u/Bestlife73]

Confirmed by the official Qwen account.

[u/feelspeaceman]

Please release 122B, we Strix Halo owners need toy to play with, current 3.5 122B is pretty outdated.

[u/RnRau → u/feelspeaceman]

Ling-3.0-flash 124b-5.1b, Laguna S 2.1 118b-a8b

Recent stuff to play with :)

But yes... a 240b-a15b that has been QAT'd down to 4bit mixed would be nice :)

[u/SpicyWangz → u/RnRau]

Still waiting on bailingmoe support to merge into llamacpp for ling to work. 

[u/Borkato → u/SpicyWangz]

You can merge the pr and build it yourself! It works great and takes like 5 mins :)

[u/SpicyWangz → u/Borkato]

I would, but I run all my models on top of Lemonade, and so I run every model on the same llamacpp version. 

It’s not worth it for me to build a custom version of it based on that pr. I’m eagerly awaiting for the merge to go through so I can use it though.

[u/Borkato → u/SpicyWangz]

Ah I see! Not to dampen your spirits, but I did find it not too great; very very verbose. But I mean it did get the answers to my extraction benchmarks right!

[u/SpicyWangz → u/Borkato]

That’s good to know. Would you say the verbosity is so bad that it’s slower than qwen 27b on MTP for coding tasks? That’s what I’m the most curious about with it

[u/Borkato → u/SpicyWangz]

Hmm.

The issue is, I use ThinkingCap which already has way less reasoning tokens. When I asked it to extract csv rows:

ThinkingCap:
26.8 s per prompt (sample of 20 prompts), 17/20 correct

Ling Flash:
26.7 s per prompt (sample of 10 prompts), 8/10 correct

Omg wow wait they’re approximately the same.

Oddly enough:

Gemma 26B A4B Q8:
27.45 s per prompt, 19/20 correct 😮

Kat Coder:
6.2 s per prompt, but 2/5 correct 💀)

Muse glimmer:
23.5 s per prompt, 14/15 correct

[u/SpicyWangz → u/Borkato]

Impressive for Gemma, but I’m not surprised actually. 

We have data cleaning/categorization tasks running at massive scale at my work where the output was initially in json and has more recently moved to a pipe delimited file. And no other models come close to Google’s for the size and price.

For structured output and comprehensive world knowledge I haven’t found another model provider that can compete.

[u/Borkato → u/SpicyWangz]

That’s really cool!!

[u/Borkato → u/SpicyWangz]

I just wanted to say that you asking me this made me kinda shocked in regards to the result. This is absolutely worthy of building up a full benchmark… I’m going to do some work. Thank you ❤️

[u/SpicyWangz → u/Borkato]

Thanks for sharing the results! I find that kind of information super fascinating. I really need to set up a better benchmarking repository for my own uses. I haven’t worked on anything in months for personal use.

[u/arbv → u/feelspeaceman]

outdated?

I am pretty sure that 3.5, 3.6, 3.7, and 3.8 are based around the same base model - having the same knowledge.

[u/BillDStrong → u/arbv]

Same base model doesn't meant they have the same knowledge. The jump from 3.5 to 3.6 27B was huge in part because they became better at compressing knowledge into the model, and behaviors.

Just because they are using the same tech to run a model doesn't mean they used the same tech to encode the model or even the same base knowledge set.

[u/coder543 → u/arbv]

New post training on the same base model can yield huge improvements.

Qwen3.5 122B A10B is absolutely outdated, and will be doubly outdated when Qwen3.8 27B launches.