What this error means
400 Bad Request — unsupported parameter passed to model API is a LiteLLM failure pattern reported for developers trying to fix 400 error when using litellm with codex cli and chinese/domestic llm models. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.
Why this happens
OpenAI Codex CLI uses Responses API protocol; domestic Chinese models only support Chat Completions API. LiteLLM's protocol translation passes unsupported fields (client_metadata, reasoning_effort, reasoning, coding_plan, parallel_tool_calls, stream_options, modalities, prediction, audio, store, include, prompt_cache_key), causing 400 errors. Fix requires parameter filtering per model type.
Common causes
- Developers using OpenAI Codex CLI (v0.130.0) with LiteLLM proxy and Chinese/domestic LLM models get 400 errors because LiteLLM's protocol translation layer passes Codex-specific parameters (client_metadata, reasoning_effort, coding_plan, parallel_tool_calls) that domestic models don't support.
- OpenAI Codex CLI uses Responses API protocol; domestic Chinese models only support Chat Completions API. LiteLLM's protocol translation passes unsupported fields (client_metadata, reasoning_effort, reasoning, coding_plan, parallel_tool_calls, stream_options, modalities, prediction, audio, store, include, prompt_cache_key), causing 400 errors. Fix requires parameter filtering per model type.
Quick fixes
- Confirm the exact error signature matches
400 Bad Request — unsupported parameter passed to model API. - Check the LiteLLM 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.