The benchmarks are cool and all but 1M context on an Opus-class model is the real headline here imo. Has anyone actually pushed it to the limit yet? Long context has historically been one of those "works great in the demo" situations.
Did they solve the "lost in the middle" problem? Proof will be in the pudding, I suppose. But that number alone isn't all that meaningful for many (most?) practical uses. Claude 4.5 often starts reverting bug fixes ~50k tokens back, which isn't a context window length problem.
Things fall apart much sooner than the context window length for all of my use cases (which are more reasoning related). What is a good use case? Do those use cases require strong verification to combat the "lost in the middle" problems?