Selected Model Is at Capacity: What It Means and How to Fix It

Key Takeaways

• Selected model is at capacity means the requested model temporarily cannot accept more work.
• It is usually a service-capacity issue, not an account suspension or exhausted usage allowance.
• Retrying the request or switching to another model is normally the fastest solution.
• Check your usage page only when the interface also displays a limit or reset message.

What Does “Selected Model Is at Capacity” Mean?

The warning appears when the selected AI model has more active demand than its available computing capacity can handle.

Your request may be valid, your account may still have available usage, and the application itself may continue working. The problem is usually limited to the selected model or the infrastructure serving it.

This can affect Codex CLI, IDE extensions, cloud tasks, ChatGPT, and other tools that depend on shared model capacity.

Why Does This Error Happen?

Common causes include:

• A sudden increase in user demand
• Temporary model or cluster maintenance
• Regional infrastructure congestion
• A newly released model receiving unusually high traffic
• Large coding tasks requiring more compute than immediately available

Capacity can also vary between models. One model may reject new tasks while another remains available.

How to Fix the Capacity Error

1. Retry the Request

Submit the request again after a brief pause. Temporary capacity problems often clear without requiring configuration changes.

Avoid repeatedly submitting the same task within a few seconds. Controlled retries are more reliable than rapid retry loops.

2. Switch to Another Model

Changing models is usually the fastest workaround. Choose a model that supports the same coding or reasoning workflow, even if it is slightly less powerful.

For routine tasks such as file editing, test generation, documentation, and small bug fixes, a lighter model may complete the work with little practical difference.

3. Reduce the Task Size

Large repository-wide requests can require substantial compute. Break the task into smaller operations, such as:

• Analyze one directory at a time
• Modify a limited set of files
• Run tests separately from implementation
• Split research, planning, and coding into separate prompts

This does not guarantee access when a model is completely unavailable, but it can improve reliability during periods of heavy demand.

4. Check the Service Status

Review the OpenAI status page when the error affects multiple models, continues across new sessions, or appears alongside failed cloud tasks.

A confirmed service incident means local troubleshooting is unlikely to help. Switching models or retrying later is more effective.

5. Confirm It Is Not a Usage Limit

Capacity errors and account limits are different problems.

A usage-limit warning normally includes allowance or reset information. A capacity warning usually does not.

What Should You Avoid Doing?

Do not immediately reinstall the CLI, remove the IDE extension, delete project configuration, or regenerate authentication credentials. These actions rarely solve server-side capacity problems and can introduce unrelated setup issues.

Clearing the conversation may also remove useful context without improving model availability. Preserve the current session unless there is evidence that the session itself is corrupted.

Conclusion

The Selected model is at capacity warning is generally temporary and does not mean the account has been blocked or its allowance has been exhausted.

Retry the task once, switch to another compatible model, and check the service status if the problem continues. For time-sensitive coding work, keeping a reliable secondary model configured is the most practical way to avoid disruption.