Codex model can't be used for reviews

Resolved 💬 4 comments Opened Nov 22, 2025 by matssk Closed Dec 1, 2025
💡 Likely answer: A maintainer (github-actions[bot], contributor) responded on this thread — see the highlighted reply below.

What version of Codex is running?

0.63.0

What subscription do you have?

Pro

Which model were you using?

gpt-5.1-codex-max

What platform is your computer?

_No response_

What issue are you seeing?

Trying to use the GPT 5.1 codex max for reviews using the /review command, it does not find bugs.

After repeatedly running /review with GPT 5.1 Codex Max I tried the same with GPT-5.1 and each time it found a new bug, while GPT 5.1 Codex Max was unable to find any of them.

What steps can reproduce the bug?

Type /review when you have code with bugs using the GPT 5.1 Codex Max model

It does not find any bugs, even when run many times.

What is the expected behavior?

It should find bugs, like the GPT-5 or 5.1

Additional information

_No response_

View original on GitHub ↗

4 Comments

github-actions[bot] contributor · 9 months ago

Potential duplicates detected. Please review them and close your issue if it is a duplicate.

  • #5547
  • #5375

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theBucky · 9 months ago

/review function always use gpt-5.1-codex with medium reasoning effort if review_model is not set in config.toml

matssk · 9 months ago
/review function always use gpt-5.1-codex with medium reasoning effort if review_model is not set in config.toml

Hm. If that's so this might just have been a coincidence then. I tried running /review in multiple session with changing model using /model and I ran it many times this way with model set to gpt-5.1 and gpt-5.1-codex-max. When I set the model to codex max it didn't find any of the bugs, but when it was on gpt-5.1 it found them. Now if /model doesn't actually change the model used for the review though, it must have just been the random seeds for when I had set model to gpt-5.1, just happened to be randomly "better" for finding the bugs.

etraut-openai contributor · 9 months ago

Thanks for the bug report. We'll share the feedback with the team responsible for training our codex models.