If the models are designed around it, and not resorting to compression to get to higher input token lengths, they don't 'fall off' as they get near the context window limit. When working with large codebases, exhausting or compressing the context actually causes more issues since the agent forgets what was in the other libraries and files. Google has realized this internally and were among the first to get to 2M token context length (internally then later released publicly).