Have we reached the point where open-source LLMs are “just good enough”?

The question I’m asking myself is whether open-source LLMs are now “**just good enough**” to meet 95% of requirements. I know, of course, that they still need to and will get even better, but where does the added value of the remaining 5% come from? * a) Better answer quality? Okay, but does that justify the extra cost? * b) Cleaner automated loops? Do the extra costs justify the effort of manual interventions to produce the same or similar quality? * c) Reduced risk of facing internal/external criticism for betting on the wrong/slower horse (since the prevailing opinion is that only the first ones are the best) * d) Even greater productivity? Okay, but does this justify the additional costs? * e) General risk management: if errors occur, can we protect ourselves, since we’ve chosen the best (OpenAI, Anthropic, Google, etc.) anyway? * f) ??? As I said, I’m primarily concerned here with **cost-benefit arguments** (**that we want to advance technically goes without saying**) and with other opinions … (to better position ourselves internally) **What do you think?**