It could've been shortened to: Codex is more hands off, I personally prefer that over claude's more hands-on approach. Neither are bad. I won't bring you proof or examples, this is just my opinion based on my experience.

All this of course can be added using MCPs, but it’s still friction. The Claude Code SDK is also way better than OpenAI Agents, it’s almost no comparison.

Also in general when I experienced bugs with Codex I was always almost sure to find an open GitHub issue with people already asking about a fix for months.

Still I like GPT-5.2 very much for coding and general agent tasks, and there is EveryCode which is a nice fork of Codex that mitigates a lot of shortcomings

I spend most of my engineering time these days not on writing code or even thinking about my product, but on Claude Code configuration (which is portable so should another solution arise I can move it). Whenever Claude Code doesn’t oneshot something, that is an opportunity for improvement.

Right now, as an example, claude code with opus 4.5 is a beast, but before that, with sonnet 4.0, codex was much better.

Gemini-cli, on the other hand, with gemini-flash-3.0 (which is strangely good for the "small and fast" model), it's very good (but the cli and the user experience are not on par with codex or claude yet).

So we need to be in constant observations of those tools. Currently (after gemini-flash-3.0 came out), I tend to submit the same task to claude (with opus) and gemini to understand the behaviour. gemini is surprising me.

What plans are you using, what did you build, what was the output from both on similar inputs, what's an example of a prompt that took you two hours to write, what was the output, etc?

From my personal experience I find cursor to be much more robust because rather than "either / or" its both and can switch depending on the time or the task or whatever the newest model is.

It feels like the same way people often try to avoid "vendor lock in" in software world that Cursor allows freedom for that, but maybe I'm on my own here as I don't see it naturally come up in posts like these as much.

It's important to separate vibes coding from vibes engineering here. For production coding, I create fairly strict plans -- not details, but sequences, step requirements, and documented updating of the plan as it goes. I can run the same plan in both, and it's clear that codex is poor at instruction following because I see it go off plan most of the time. At the same time it can go on its own pretty far in an undirected way.

The result is when I'm doing serious planned work aimed for production PRs, I have to use Claude. When it's experimental and I don't care about quality but speed and distance, such as for prototyping or debugging, codex is great.

Edit: I don't think codex being poor at instruction following is inherent, just where they are today

And I’ll never use OpenAI dev tools because the company insists on a complete absence of ethical standards.

Codex was very context efficient, but also slow (though I used the highest thinking effort), and didn’t adapt do the wider codebase almost at all (even if I pointed it at the files to reference / get inspired by). Lots of defensive programming, hacky implementations, not adapting to the codebase style and patterns.

With Claude Code and starting each conversation by referencing a couple existing files, I am able to get it to write code mostly like I would’ve written it. It adapts to existing patterns, adjusts to the code style, etc. I can steer it very well.

And now with the new cheaper faster Opus it’s also quite an improvement. If you kick off sonnet with a long list of constraints (e.g. 20) it would often ignore many. Opus is much better at “keeping more in mind” while writing the code.

Note: yes, I do also have an agent.md / claude.md. But I also heavily rely on warming the context up with some context dumping at conversation starts.

it takes only one extra line of code in my world(finance) to have catastrophic consequences.

even though i am using these tools like claude/cursor, i make sure to review every small bit it generated to a level, where i ask it create a plan with steps, and then perform each step, ask me for feedback, only when i give approval/feedback, it either proceeds for the next step or iterate on previous step, and on top of that i manually test everything I send for PR.

because there is no value in just sending a PR vs sending a verified/tested PR

with that said, I am not sure how much of your code is getting checked in without supervision, as it's very difficult for people to review weeks worth of work at a time.

just my 2 cents

Looks like Gemini plans have even more generous limits on the equivalently priced plans (Google AI Pro). I'd be interested in the experiences of people who used Google Antigravity/Gemini CLI/Gemini Code Assist for nontrivial tasks.