Repeated "Selected model is at capacity" errors interrupting Codex tasks on ChatGPT Pro

What issue are you seeing?

I am a ChatGPT Pro user using the latest Codex app.

Starting today, August 31, 2026, Codex has repeatedly shown the following error:

"Selected model is at capacity. Please try a different model."

This is happening very frequently during normal agent tasks even though I still have plenty of usage remaining.

In one single task lasting about 15 minutes, I encountered this capacity error 3 times. Codex entered retry backoff (for example, a 117-second retry), resumed briefly, and then hit the same capacity error again.

This severely disrupts long-running Codex workflows. The task was actively editing files and running commands when the model capacity errors occurred.

I have been using ChatGPT Pro during my current subscription period and had essentially never encountered this problem before today. Today it suddenly started happening repeatedly.

This does not appear to be a usage-limit issue. My Pro usage quota is still available.

Please investigate model serving capacity / routing for ChatGPT Pro Codex users.

Screenshot attached showing three "Selected model is at capacity" errors during the same ~15-minute task.

What steps can reproduce the bug?

1. Open the latest Codex app while signed in with a ChatGPT Pro subscription.
2. Select the affected model.
3. Start a normal long-running coding task that edits files and runs commands.
4. Allow Codex to continue working for several minutes.
5. During the task, Codex displays:
   "Selected model is at capacity. Please try a different model."
6. Wait for the automatic retry/backoff.
7. Codex resumes, but the same capacity error occurs again shortly afterward.

In my latest reproduction, the error occurred 3 times within approximately 15 minutes.

What is the expected behavior?

No response

Additional information

No response

Start by reproducing the repeated capacity error in the latest Codex app with a ChatGPT Pro account during a long-running task. Investigate the model-serving or routing path responsible for the error and retry behavior; done means the same workflow no longer encounters repeated capacity failures, or the cause is documented.