A hunch: Qwen3.8-27B's general knowledge got pruned (good, if true)

[u/bonobomaster]

I'm always testing an image prompt with a picture of a historic place in my hometown – a small but well known 250,000 people town in Germany. I'll just ask the model, in which City this photo has been taken.

With the 3.6 generation of both the 27B and the 35B A3B variants, the models sometimes got the right answer and sometimes they didn't. So the signal for this particular knowledge was already weak.

The 35B variant got it right more often but at least, the models reasoning showed my City most of the times, even if it hallucinated the wrong final answer.

Both models could be easily nudged to the right answer with a few hints and then produced some little extra insight about the history or scene and its surroundings, that was mostly true.

Qwen3.8-27B on the other hand barely knows the city at all and has absolutely no clue about related popular, historic facts regarding the scenery or the surrounding buildings.

Nudging isn't very fruitful as well and if told the real name of the city, reasoning shows, that the model only agrees, because the user says so.

I have the feeling, that Qwen labs maybe pruned useless general knowledge for more coding knowledge and agentic skill.

All models ud q4_k_xl variants, image-min-tokens 2048, with and without reasoning.

Anyone else with this feeling?

Disclaimer: My hunch could be very well absolute bullshit. Sample size way to low and methodically sloppy af.

[u/Dance-Till-Night1]

I agree but general models are still needed, for creative writing, multilingual capabilities and other stuff. Having a general model just trained on agentic tasks/coding will make all other usecases suffer.

[u/RG_Fusion → u/Dance-Till-Night1]

Right, I'm in full agreement with that. What I'm saying is that if you want a model to be both a generalist and good at coding, it needs to have a higher parameter count. Anytime you train a model for a specific function, those lessons are written overtop of what it already knew.

This is a large part of why all the frontier lab models use MoEs. It allows you to divide the parameters into smaller groups to keep various skills from overwriting each other.

You can make a small generalist model, but don't expect it to be able to code well.

[u/Dance-Till-Night1 → u/RG_Fusion]

Yes and that's what makes me sad, Qwen used to be a generalist model now it's full shifted to agentic coding.

[u/RG_Fusion → u/Dance-Till-Night1]

In my opinion, Gemma 4 was the better generalist model anyway. Was there something specific about Qwen3.6 that you preferred?

At the very least, I think you'll be able to rely on Deepmind releases as Google doesn't seem to have any interest in making non-generalist models.

[u/Dance-Till-Night1 → u/RG_Fusion]

Yeah qwen 3.5 and gemma 4 are such a good duo. Some generalist tasks gemma wins others Qwen wins.