What this error means

"usage limit reached" error from OpenAI Codex provider — Confusion between subscription-based usage caps and API rate limits leads to failed completions when free-tier quota is exhausted is a OpenAI API failure pattern reported for developers trying to resolve "usage limit reached" error in cursor/openai codex provider; switch to standard openai api provider or increase plan to continue using models. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

cline/cline#8910 reports real users hitting usage limit errors in Cursor's built-in Codex provider. Distinguishing this from standard API key errors is valuable because many users conflate subscription usage limits with API rate limits.

Common causes

  • cline/cline#8910 reports real users hitting usage limit errors in Cursor's built-in Codex provider. Distinguishing this from standard API key errors is valuable because many users conflate subscription usage limits with API rate limits.

Quick fixes

  1. Confirm the exact error signature matches "usage limit reached" error from OpenAI Codex provider — Confusion between subscription-based usage caps and API rate limits leads to failed completions when free-tier quota is exhausted.
  2. Check the OpenAI API account, local tool state, and provider configuration involved in the failing workflow.
  3. Reduce request pressure, check quota or plan limits, and retry with backoff instead of immediate repeated requests.

Platform/tool-specific checks

  • Verify the command, editor, extension, or API client that produced the error.
  • Compare local settings with CI, deployment, or editor-level settings when the error appears in only one environment.
  • Avoid deleting credentials, local model data, or project settings until the failing scope is clear.

Step-by-step troubleshooting

  1. Capture the exact error message and the command, editor action, or request that triggered it.
  2. Check whether the failure is account/auth, quota/rate, model/provider, local runtime, or deployment configuration.
  3. Review the source evidence below and compare it with your environment.
  4. Apply one change at a time and rerun the smallest failing action.
  5. Keep the working fix documented for the team or deployment environment.

How to prevent it

  • Keep provider/tool configuration documented.
  • Record non-secret diagnostics such as tool version, provider name, model name, and command path.
  • Add a lightweight check before CI or production workflows depend on the tool.