Earlier quoted context omitted.
You're kind of contradicting yourself. If a high token count tells you all you need to know about how smart a model is Ultra 1.5 would be just as good as Pro 1.5. The fact of the matter is it remains to be seen how smart either model will be.
You can make up for a lot of smartness by having a smarter model generate all the text you put in that giant context window.
I just asked mistral 7b to provide 5 sentences that end with Apple. It couldn't do it. I then provided 5 examples generated from ChatGPT 4 and asked it to generate 5 more. It still couldn't do it. 10 ChatGPT examples- still couldn't do it.
You seem to be saying the models can generalize on the entire context size, that I should keep provided examples up to the token limit because this will make the model smarter. Is there evidence of that?