What this error means

Error code: 401 - Access denied due to invalid subscription key or wrong API endpoint — AzureOpenAI passes AAD bearer token via api_key parameter which now sends api-key header instead of Bearer token is a OpenAI API failure pattern reported for developers trying to fix 401 unauthorized when upgrading from openai==2.33.0 to 2.34.0 with azure openai + azure ad authentication via apim proxy. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub issue #3282 (opened May 20, 2026) on openai/openai-python. Regression in v2.34.0 where azure_ad_token/api_key handling changed — api_key now consistently sends Authorization: api-key header while azure_ad_token sends Bearer, breaking existing Azure AD workflow. PR #3283 already linked. Strong commercial value: blocks enterprise production deployment with Azure + OpenAI integration.

Common causes

  • GitHub issue #3282 (opened May 20, 2026) on openai/openai-python. Regression in v2.34.0 where azure_ad_token/api_key handling changed — api_key now consistently sends Authorization: api-key header while azure_ad_token sends Bearer, breaking existing Azure AD workflow. PR #3283 already linked. Strong commercial value: blocks enterprise production deployment with Azure + OpenAI integration.

Quick fixes

  1. Confirm the exact error signature matches Error code: 401 - Access denied due to invalid subscription key or wrong API endpoint — AzureOpenAI passes AAD bearer token via api_key parameter which now sends api-key header instead of Bearer token.
  2. Check the OpenAI API account, local tool state, and provider configuration involved in the failing workflow.
  3. Verify the account session, API key, provider settings, and environment where the failing tool is running.

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.