u/input_a_new_name

I've been using both side by side over this evening working on a project. Basically I'd paste a chunk of creative text into chat and tell it to dismantle it thesis-by-thesis, then I'd see if the criticism is actually sound, and submit the next iteration of the file which incorporates my solutions to bypassing the criticism. Then move on to the next segment, next file, repeat ad infimum.

What I found is that Gemma 4 31B keeps track of the important point very cleanly, maintains unbiased approach over more subsequent turns: GLM basically turns into a yes-man immediately "Woah! Such a genius solution! You really did it! This is so much better omfg, production ready! Poosh-poosh!", Gemma can take at least 3-4 rounds of back and forth and keep a level of constructivism and tell you outright if you just sidestepped the problem instead of actually presenting a valid counterargument. Not as bluntly and unapologetically as it could've, but compared to GLM, ooof, I'll take it man... Along the way it also proposed some suggestions that seemed really efficient, if not out of the box (example, say you got 4 "actors" that need to dynamically interact in a predictable and logical way, instead of creating a 4x4 boolean yes-no-gate matrix where a system can check who-"yes"-who and who-"no"-who, you just condense it into 6 vectors that come with instruction for which type of interaction should play out if the linked pair is called. it's actually a really simple and even obvious optimization, but GLM never even considered this for some reason until I just told it. Okay, don't take this is as proof of some moronic point, it's just my specific example that I experienced.

Gemma sometimes did not even use thinking. It just gave a straight response, and it was still statistically more useful than the average GLM response.
GLM would always think for a thousand or two tokens. Even if the actual response would be like 300, all to say "all good bossmang!"

It also seemed like Gemma was more confident at retrieving/recreating stuff from way earlier in conversation, rewriting whole pages of text exactly one-to-one on demand in chat, or incorporating a bit from one point in chat to a passage from a different point, without a detailed explanation of what exact snippets I mean. I caught GLM just hallucinate certain parts instead. Well, the token meter probably never went above like 30k, so I dunno if that's really impressive by today's standard or not though.

On average I would say that GLM wasted like 60% of my requests by returning useless or worthless output. With Gemma 4 it felt like only 30% of the time it went nowhere. But the amount of "amazing" responses, which is a completely made up metric by me, was roughly the same at like maybe 10%. Anyway, what I'm getting at is, Gemma 4 is far from being a perfect model, that's still a fantasy, but for being literally a 30B bracket model, to feel so much more apparently useful than a GLM flagman, surprised the hell out of me.

I've been using both side by side over this evening working on a project. Basically I'd paste a chunk of creative text into chat and tell it to dismantle it thesis-by-thesis, then I'd see if the criticism is actually sound, and submit the next iteration of the file which incorporates my solutions to bypassing the criticism. Then move on to the next segment, next file, repeat ad infimum.

What I found is that Gemma 4 31B keeps track of the important point very cleanly, maintains unbiased approach over more subsequent turns: GLM basically turns into a yes-man immediately "Woah! Such a genius solution! You really did it! This is so much better omfg, production ready! Poosh-poosh!", Gemma can take at least 3-4 rounds of back and forth and keep a level of constructivism and tell you outright if you just sidestepped the problem instead of actually presenting a valid counterargument. Not as bluntly and unapologetically as it could've, but compared to GLM, ooof, I'll take it man... Along the way it also proposed some suggestions that seemed really efficient, if not out of the box (example, say you got 4 "actors" that need to dynamically interact in a predictable and logical way, instead of creating a 4x4 boolean yes-no-gate matrix where a system can check who-"yes"-who and who-"no"-who, you just condense it into 6 vectors that come with instruction for which type of interaction should play out if the linked pair is called. it's actually a really simple and even obvious optimization, but GLM never even considered this for some reason until I just told it. Okay, don't take this is as proof of some moronic point, it's just my specific example that I experienced.

Gemma sometimes did not even use thinking. It just gave a straight response, and it was still statistically more useful than the average GLM response.
GLM would always think for a thousand or two tokens. Even if the actual response would be like 300, all to say "all good bossmang!"

It also seemed like Gemma was more confident at retrieving/recreating stuff from way earlier in conversation, rewriting whole pages of text exactly one-to-one on demand in chat, or incorporating a bit from one point in chat to a passage from a different point, without a detailed explanation of what exact snippets I mean. I caught GLM just hallucinate certain parts instead. Well, the token meter probably never went above like 30k, so I dunno if that's really impressive by today's standard or not though.

On average I would say that GLM wasted like 60% of my requests by returning useless or worthless output. With Gemma 4 it felt like only 30% of the time it went nowhere. But the amount of "amazing" responses, which is a completely made up metric by me, was roughly the same at like maybe 10%. Anyway, what I'm getting at is, Gemma 4 is far from being a perfect model, that's still a fantasy, but for being literally a 30B bracket model, to feel so much more apparently useful than a GLM flagman, surprised the hell out of me.

A big milestone for local inference.