Benchmarks only give you the roughest idea of how models compare in real world use. They're essentially useless beyond maybe classifying models into a few buckets. The only way you gain an understanding of something as complex as how an LLM integrates with your workflow is by doing it and measuring across many trials. I've been running Opus 4.7 in Claude Code and Gemma 4 31b in parallel on projects for hours a day this past week, Opus 4.7 is definitely better, but for many things they are roughly equivalent, there are some things on the edge that are just up to chance, and either model may stumble across the solution, and there are some areas of my work that reliably trip up both models and I get better mileage out of writing code the old fashioned way. I understand that I'm just one data point, but I'm not writing CRUD apps here, I'm doing DSPs and weird color math in shaders, I don't think any of it is hard, and the stuff that I think is hard none of the models are good at yet, but idk, they just don't seem that extremely disparate from one another.

FWIW I think Gemma 4 31b is more likely to be of use to me than Sonnet, idfk, maybe it's a skill issue but I love Opus 4.7, undisputed king, but Sonnet seems borderline useless and I basically think of it as on the same level as Qwen 35b MoE.

I’m building a pipeline and testing against gemma4 and Gemini’s 3-1 flash. Both are very good on certain tasks and even n-way clustering works almost perfect almost always.

But they diverge greatly on other particular ones whenever the ViT tower and the apriori knowledge of the world is crucial. I wish Gemma was on par but both me and Google know they not.

You do need to ask whether or not Sonnet or Opus are overkill for a lot of work though. If Gemma4 with some human effort can achieve the same result as Sonnet then it's arguably a lot more cost effective as you're paying for the person to operate each one regardless.

No, exactly the opposite actually. Qwen3.6 is too imprecise for long running agentic tasks. It doesn't have the same ability to check itself as Gemma does in my testing. I keep Qwen MoE in vram by default because there are tons of tasks i trust it to oneshot and it's 90tok/sec is unparalleled, anything where I don't want to have to intervene too much it can't be trusted.

Overall using screentime as the metric, derived from some imperfect logging and vibes it's about 50% OpenCode 15% Continue 15% my homebrew bullshit 13% Claude Code and 7% Cline. I've been deep on agentic stuff lately (1.3wks aka 3 months of AI time), there are only so many hours in the day to duplicate work and AB test, but in the past I've sworn by Qwen Coder + llama.vim and I still enjoy that workflow for deep work far more than I like prompting agents, but there's a lot of dross I'm learning to delegate.

False. The absolute capability is irrelevant, with the proper harness 31b is more than adequate for a very large portion of the tasks I ask AI to do. The metric isn't how good the model is at Erdos Problems, it's how reliably it can remove drudgery in my life. It just autonomously reverse engineered a bluetooth protocol with minimal intervention, it's ability to react to data and ground itself is constantly impressive to me. I do a ton of testing with these models, today I had Gemma answer a physics problem that Opus 4.7 gave up on. With a decent harness and context the set of tasks where their capabilities are both good enough is very surprising. The tasks I have that stump Gemma often also stump Opus 4.7.

It would be true, if model providers did not throttle their models. I do not have definitive proof they do but the rumors are abundant.

I think you are missing the point here. what matters is for that user the local models are good enough for their use case.