What this error means

OpenAI Batch API returns 404 with GPT-5.x models: model_not_found gpt-5-mini-2025-08-07-batch is a OpenAI API failure pattern reported for developers trying to fix openai batch api 404 error when using gpt-5.x models. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

OpenAI Batch API returns 404 with model_not_found for gpt-5-mini-2025-08-07-batch. Users confirm token has access to GPT-5-mini. /v1/responses works but /v1/chat/completions batch fails. OpenAI docs list GPT-5.x as supporting batch API.

Common causes

  • The Batch API automatically appends -batch suffix to model names, but GPT-5.x batch variants do not exist. This breaks batch processing workflows for developers who upgraded from GPT-4.x to GPT-5.x. The /v1/responses endpoint works fine, only /v1/chat/completions batch is affected.
  • OpenAI Batch API returns 404 with model_not_found for gpt-5-mini-2025-08-07-batch. Users confirm token has access to GPT-5-mini. /v1/responses works but /v1/chat/completions batch fails. OpenAI docs list GPT-5.x as supporting batch API.

Quick fixes

  1. Confirm the exact error signature matches OpenAI Batch API returns 404 with GPT-5.x models: model_not_found gpt-5-mini-2025-08-07-batch.
  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.