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
- Confirm the exact error signature matches
Tool choices other than 'auto' are not supported with model on GPT5. - Check the OpenAI API account, local tool state, and provider configuration involved in the failing workflow.
- 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
- Capture the exact error message and the command, editor action, or request that triggered it.
- Check whether the failure is account/auth, quota/rate, model/provider, local runtime, or deployment configuration.
- Review the source evidence below and compare it with your environment.
- Apply one change at a time and rerun the smallest failing action.
- 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.