What this error means

tokens burning very fast following extension update is a GitHub Copilot failure pattern reported for developers trying to users report that the openai codex desktop extension rapidly depletes token usage after an update — just 1–2 prompts drop usage by 1%. users want to understand why tokens are consumed excessively and how to fix/prevent it. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub Issue #14593 on openai/codex (opened 2026-03-13). Reported by cy-ooi88 with 586 comments — extremely high engagement. Labels: bug, rate-limits. Directly affects paying GitHub Copilot/Codex subscribers. Maps to GitHub Copilot category.

Common causes

  • GitHub Issue #14593 on openai/codex (opened 2026-03-13). Reported by cy-ooi88 with 586 comments — extremely high engagement. Labels: bug, rate-limits. Directly affects paying GitHub Copilot/Codex subscribers. Maps to GitHub Copilot category.

Quick fixes

  1. Confirm the exact error signature matches tokens burning very fast following extension update.
  2. Check the GitHub Copilot account, local tool state, and provider configuration involved in the failing workflow.
  3. Compare the failing environment with a known working setup, then change one configuration value at a time.

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.