The catch is that "does as good as X" is pretty much never representative of real world performance when it comes to LLMs. In general, all those scores are mos

The catch is that "does as good as X" is pretty much never representative of real world performance when it comes to LLMs. In general, all those scores are mostly useful to filter out the models that are blatantly and obviously bad. But to determine whether the model is actually good at any specific thing that you need, you'll have to evaluate them yourself to find out.