I stumbled into this same workflow idea. I hadn't really used anything other that Chat GPT Sol (medium) and when I wanted to start using code agents, I just asked GPT how to start. This evolved into having GPT do the brainstorming and writing the initial prompt for a project feature/change to hand over to Codex working in Sol light mode. These prompts where often a couple pages long as they had a lot of the architecture details worked out already. Codex would then come up with a plan and I'd take that back to GPT to review. GPT would make some suggestions, which I'd throw back to the agent and off it went.

I got super great results working this way. Maybe there is a better way to integrate the two modes though. But my first real project was end-to-end 100% working correctly from the first run - about 10,000 lines (including tests) of greenfield code. I did have it work in manageable chunks that I could easily review - not one-shotting the whole thing.

Now I'm thinking about plugging Deepseek into Codex to be the coding model and see how it goes.

Pairing non-APIs with APIs tend to be a hassle, and risky, as usually that breaks the ToC. I guess easiest for you to test if it's worth using the OpenAI API, is to manually copy-paste responses between wherever you run V4-Flash and the ChatGPT UI. What I've done in the past is basically .zip up the entire project directory, ignoring files from .gitignore, then asking ChatGPT Pro to inspect that and come up with a plan, then you paste that to where you have V4-Flash. Basically how we did "vibe pair programming" before the TUI agent harnesses appeared in the ecosystem :)

https://github.com/obra/superpowers

I use Opus/Sol with for /brainstorming, deepseek (on Pi) for /subagent-driven-development

I love it, the docs are easily editable and when I'm ready Deepseek is faster/cheaper then everything on my coding plans.