Luna is the main exception to this. It's such a cheap model and the tokens come so fast.
I use CC and Codex about equally. CC by far has the best harness but I prefer OAI's models.
- CC is pretty effortless in sleeping, monitoring, or waiting. If I ask CC to get CI green, it will correctly wait for the GitHub status check to resolve, check BuildKite logs, and iterate. Codex will usually just do one cycle usually because it gets confused on how to wait. I think Claude Code has a built in 'monitor' primitive which might help.
- With CC I can easily say here's a huge piece of work that will likely be 100k-200k LOC. Divide up the work and have cheaper models do the impl. CC will _usually_ do this right. With Codex it almost never works well for me. I have been able to get GPT 6 Astra to do this more successfully.
It seems weird to me that just using a ton of output tokens manages to produce a decent result in the end.
It seems to work well though. Sometimes I fear that it might be more likely to eg. run an incorrect, destructive command, but maybe that concern is not justified.