[NitpickLawyer]

Just some anecdata++ here but I found 5.2 to be really good at code review. So I can have something crunched by cheaper models, reviewed async by codex and then re-prompt with the findings from the review. It finds good things, doesn't flag nits (if prompted not to) and the overall flow is worth it for me. Speed loss doesn't impact this flow that much.

[kilroy123]

Personally, I have Claude do the coding. Then 5.2-high do the reviewing.

[mmaunder]

I might flip that given how hard it's been for Claude to deal with longer context tasks like a coding session with iterations vs a single top down diff review.

[seunosewa]

Then I pass the review back to Claude Opus to implement it.

[VladVladikoff]

Just curious is this a manual process or you guys have automated these steps?

[ricketycricket]

I have a `codex-review` skill with a shell script that uses the Codex CLI with a prompt. It tells Claude to use Codex as a review partner and to push back if it disagrees. They will go through 3 or 4 back-and-forth iterations some times before they find consensus. It's not perfect, but it does help because Claude will point out the things Codex found and give it credit.

[bryanlarsen]

Mind sharing the skill/prompt?

[dror]

Not the OP, but I use the same approach.

https://gist.github.com/drorm/7851e6ee84a263c8bad743b037fb7a...

I typically use github issues as the unit of work, so that's part of my instruction.

[_zoltan_]

zen-mcp (now called pal-mcp I think) and then claude code can actually just pass things to gemini (or any other model)

[kilroy123]

Sometimes, depends on how big of a task. I just find 5.2 so slow.

[_zoltan_]

I have Opus 4.5 do everything then review it with Gemini 3.

[StephenHerlihyy]

I don’t use OpenAI too much, but I follow a similar work flow. Use Opus for design/architecture work. Move it to Sonnet for implementation and build out. Then finally over to Gemini for review, QC and standards check. There is an absolute gain in using different models. Each has their own style and way of solving the problem just like a human team. It’s kind of awesome and crazy and a bit scary all at once.

[readyforbrunch]

How do you orchestrate this workflow? Do you define different skills that all use different models, or something else?

[nitroedge]

You should check out the PAL MCP and then also use this process, its super solid: https://github.com/glittercowboy/get-shit-done

The way "Phases" are handled is incredible with research then planning, then execution and no context rot because behind the scenes everything is being saved in a State.md file...

I'm on Phase 41 of my own project and the reliability and almost absence of any error is amazing. Investigate and see if its a fit for you. The PAL MCP you can setup to have Gemini with its large context review what Claude codes.