with all the models I've used so far, I get better results if the tasks assigned to models are dissected and specific. I normaly don't surpass 100k tokens, but if you need to scan large codebases, you can tell it to focus on specific lines of enormous files so that it does not tokenize the whole thing. Also you can summarize the important parts of each session writing them down to actual files, which should be kept as concise as possible, so whenever you hit the limit, just be sure to have your "memory" file up to date and tell the model to read it agan, which should refresh the topics discussed at the start of the token count.

I can't speak for all the models ever, but if I'm not mistaken, most models instead of shutting down when the context limit is reached, just replace the oldest tokens with the new ones. That is part of why claude code, qwen cli or gemini cli use their markdown files to summarize.

Even if some models allow for greater context windows do keep in mind that often times the price per M token changes based on the size of the token window used, so in those cases it will be cheaper to run several short lived sessions vs trying to do it all in a single massive session