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.
replyI 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 :)
replyreminds me back in the day when we used to do version control with google drive. you generally don't need to do this anymore i don't think. it should be fairly trivial to open the codex cli in the project directory, ask it to do some analysis and planning -> save to md. then opencode with deepseek to read that md and start implementing. consider that some cross-model subagent workflows already exist: i use claude/fable to plan and there are openai plugins to directly spin up codex subagents. this latter approach gives claude/fable more directly control to babysit the codex agents whereas the former is more of a clearcut handoff
replyThat approach won't give you access to the "Pro Mode" nor "Deep Research" though, which if you're paying for a Pro subscription already, "Pro Mode" is probably the top-top when it comes to "extensively researched answers & plans" across the entire ecosystem. That said, I do ask it to produce a .md plan that later is used with agents :)
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replyhttps://github.com/obra/superpowers
replyI 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.
Source reads like Tony Robbins for LLMs:
reply Excuse
"Too simple to test"
Reality
Simple code breaks. Test takes 30 seconds.