What this error means

Tool choices other than 'auto' are not supported with model on GPT5 is a OpenAI API failure pattern reported for developers trying to fix error when specifying explicit tool_choice (e.g., 'required', 'none', or function name) with gpt-5 models, forcing fallback to auto mode. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub Issue #2537 on openai/openai-python (opened Aug 9, 2025, by hayescode). GPT-5 models reject non-auto tool choices, breaking function calling patterns for developers who need deterministic tool invocation. 17 comments indicate active community engagement. High value: function calling is core to paid API usage.

Common causes

  • GitHub Issue #2537 on openai/openai-python (opened Aug 9, 2025, by hayescode). GPT-5 models reject non-auto tool choices, breaking function calling patterns for developers who need deterministic tool invocation. 17 comments indicate active community engagement. High value: function calling is core to paid API usage.

Quick fixes

  1. Confirm the exact error signature matches Tool choices other than 'auto' are not supported with model on GPT5.
  2. Check the OpenAI API 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.