Context window is only 275k or something. And honestly compaction is not that bad in Codex. I often don't even notice I went through 5 compactions in a session.
Apparently OpenAI makes you manually setup their 1 Million context window, and it seems to be only documented on X:
https://x.com/thsottiaux/status/2089082893804896524
There's at least a forum thread about it here:
https://community.openai.com/t/why-does-codex-report-a-258-4...
I barely compact at work in a very complex monorepo (neither with Fable 5.1 nor Opus 5.5), and yet in my personal greenfield project Astra keeps compacting all the time, to the point of it being unusable.
yes, exactly
These are often my best sessions - they're unattended overnight, because by then we have the specification figured out, and I can just leave Claude to build out the rest, making good choices if it does find gaps in the spec. I regularly go to sleep & wake up to an entirely new application completed. Claude never uses compacting in my sessions.
I haven't used GPT as much as I should have, so I'm prepared to be incorrect & out of date. It just intuitively feels like I wouldn't get the same from a 275K context window - maybe it uses lots of subagents? Even Deepseek & GLM have 1 Million context windows now, so it "feels" strange for people to actually prefer the 275K window. But that's just my intuition.
if you talk about them (in which you lean on an LLM as a sort-of independent employee) and conservative, chunk-based usage (in which you use the LLM as more of an extension of yourself), you're comparing apples to oranges
a predefined spec obviously reduces that gap but how much is highly dependent on the level of detail
~/.codex/config.toml
model = "gpt-6.1-sol"
model_context_window = 700000
model_auto_compact_token_limit = 630000I’ve also found compaction not to be a problem when it does happen.
It also presumably means it's regularly not able to get everything it wants to have to make decisions in context, which means it's going to perform poorly...