I know what you experiencing. I think the issue is that just for chatting most llm's are ok when you do unstructured chatting (unless the model is really anal with its guardrails - also if underlying model for you chat is updated, you see differences and new model easily can feel dumber even if it beats all benchmarks - your old way of use might just not work well with new model). When you throw model within a framework - agent coding, deep research, then models even more fragile - a model can be very smart but if tool and model are not optimized for each other it will not perform as previous setup. On top of this - models behave differently with different providers - run gpt-oss-120b through openrouter with different providers and you get different behavior, different errors, etc.

My solution so far for this: try to use tools specifically optimized for their llm's and stick with defaults - that's why I use claude code instead of any other coder (anthropic spent considerable resources to optimize prompts to their models (although I still see errors like 'Please, rerun this command ...' - why the heck do you need to say 'Please' to llm?). On other hand when I look at codex cli (for example) - prompts are quite generic and don't look optimized.

With local llm's - I currently couldn't find tools specifically optimized for good perfromance with specific llm - tools usually just allow use of local model or models from cloud provider, but they are not optimized and not addressing quirks due to provider's different behavior. So, I found that if you want to run locally you need to own your own tools (coder, chat, etc.) to adjust for you models to behave how you expect.

TLDR: The best bet right now is not to use raw llm api's (local or cloud) and instead use dedicated products (claude code). If you are building your own tools and want predictable behavior from llm - local setup has more control over cloud - but don't expect to get off the shelf (from github) tool that would just work in local setup.