Not directly - but latest research advancements, cleaner / richer datasets, etc. still require fresh base models. Not everything can be fixed through post training alone (e.g. why GPT-5.5 "Spud" was such a big jump, and also why GPT-6 "Astra" is now supposedly another big leap). Ofc model size etc also plays a role, but my (admittedly limited) understanding is that new base models _can_ also lead to big jumps even keeping parameter counts constant.

You don't need everything internal, but having some idea of recent events is useful. If you ask it to implement some local AI there's a decent chance it will try to use qwen 2.5 without wondering if anything better came out since

Search grounding is expensive, you can't force the model to do it either. I use Gemini a lot and it often replies with out-dated data. The more detailed the information you're asking, the more likely it is to be wrong.

very important actually. just try to generate code for fresher frameworks/libraries. gemini sucks so bad in real work usage, everything it suggests are outdated and mostly useless.

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