LiteLLM / LiteLLM

LiteLLM gpt-4o-transcribe-diarize chunking_strategy Required Error

Fix LiteLLM 400 error requiring chunking_strategy for gpt-4o-transcribe-diarize model Includes evidence for LiteLLM troubleshooting demand.

Source-backed Last updated May 11, 2026 1 source Needs local verification

Category: LiteLLM
Error signature: 400 litellm.BadRequestError: OpenAIException - chunking_strategy is required for diarization models
Quick fix: Reduce request pressure, check quota or plan limits, and retry with backoff instead of immediate repeated requests.
Updated: May 11, 2026
Verification status: Source-backed
Evidence: 1 public source URL

Before you change production

This page includes public source URLs in the imported troubleshooting record. Compare those references with your version and environment before applying changes.

Reproduce the smallest failing action and save non-secret logs before changing configuration.
Check versions for LiteLLM, related SDKs, package managers, CI runners, and hosting providers.
Change one setting or dependency at a time, then rerun the same failing command or request.
Avoid destructive commands, credential rotation, billing changes, or security relaxations without a rollback plan.

What this error means

400 litellm.BadRequestError: OpenAIException - chunking_strategy is required for diarization models is a LiteLLM failure pattern reported for developers trying to fix litellm 400 error requiring chunking_strategy for gpt-4o-transcribe-diarize model. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub issue #18125 in BerriAI/litellm with exact error message and model group context. Affects OpenAI diarization models via LiteLLM proxy.

Common causes

Using OpenAI's gpt-4o-transcribe-diarize model through LiteLLM returns a 400 BadRequestError stating 'chunking_strategy is required for diarization models'. Developers need to know how to pass this parameter correctly through LiteLLM.
GitHub issue #18125 in BerriAI/litellm with exact error message and model group context. Affects OpenAI diarization models via LiteLLM proxy.

Quick fixes

Confirm the exact error signature matches 400 litellm.BadRequestError: OpenAIException - chunking_strategy is required for diarization models.
Check the LiteLLM account, local tool state, and provider configuration involved in the failing workflow.
Reduce request pressure, check quota or plan limits, and retry with backoff instead of immediate repeated requests.

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.

Diagnostic flow for this page

Match 400 litellm.BadRequestError: OpenAIException - chunking_strategy is required for diarization models exactly before applying the quick fix.
Compare the failing environment with LiteLLM versions, account scope, provider settings, and deployment context.
Check the listed common causes in order, starting with the cause that best matches your logs.
Use the evidence status below to decide whether to confirm against public sources or official documentation.
Apply one reversible change, rerun the smallest failing action, and keep rollback notes.

FAQ

What does 400 litellm.BadRequestError: OpenAIException - chunking_strategy is required for diarization models mean in LiteLLM?

400 litellm.BadRequestError: OpenAIException - chunking_strategy is required for diarization models indicates the workflow described by "LiteLLM gpt-4o-transcribe-diarize chunking_strategy Required Error" failed in a LiteLLM context. Start by confirming the exact signature and the component that emitted it.

What should I check first for 400 litellm.BadRequestError: OpenAIException - chunking_strategy is required for diarization models?

Check whether this cause matches your environment: Using OpenAI's gpt-4o-transcribe-diarize model through LiteLLM returns a 400 BadRequestError stating 'chunking_strategy is required for diarization models'. Developers need to know how to pass this parameter correctly through LiteLLM.

Is the 400 litellm.BadRequestError: OpenAIException - chunking_strategy is required for diarization models guidance source-backed?

The "LiteLLM gpt-4o-transcribe-diarize chunking_strategy Required Error" page is labeled source-backed. It has 1 public source URL from github.com, so use that evidence status when deciding how much additional verification you need.

When should I avoid the quick fix for 400 litellm.BadRequestError: OpenAIException - chunking_strategy is required for diarization models?

For "LiteLLM gpt-4o-transcribe-diarize chunking_strategy Required Error", avoid applying "Reduce request pressure, check quota or plan limits, and retry with backoff instead of immediate repeated requests." when the error text differs, the affected version or provider is different, or the change would touch credentials, billing, DNS, production deploy settings, or irreversible data.

What should I collect before debugging 400 litellm.BadRequestError: OpenAIException - chunking_strategy is required for diarization models?

For "LiteLLM gpt-4o-transcribe-diarize chunking_strategy Required Error", collect the failing LiteLLM command or request, non-secret configuration, versions, timestamps, request IDs when available, and the smallest reproduction that still triggers the signature.