Context is often not the only issue. Really the issue is attention - context is a factor in how well the LLM handles attention to the broad scope of a task, but one can anecdotally easily observe the thing forget or go off the rails when only a fraction of the context window is being used. Oftentimes it’s effective to just say “don’t ever go above 20% of the max”

Some of that is, or at least was, down to the training: extending the context window but not training on sufficiently long data or using weak evaluation metrics caused issues. More recent models have been getting better, though long context performance is still not as good as short context performance, even if the definition of "short context" has been greatly extended.

RoPE is great and all, but doesn't magically give 100% performance over the lengthened context; that takes more work.