LiteLLM mid-stream fallback fails with HTTP 400 assistant prefill error on Claude models

What this error means

litellm.BadRequestError: AnthropicException - {"type":"error","error":{"type":"invalid_request_error","message":"This model does not support assistant message prefill. The conversation must end with a user message."}} is a LiteLLM failure pattern reported for developers trying to fix litellm streaming fallback error: mid-stream fallback adds unsupported assistant prefill block, causing http 400 on claude sonnet 4.6 / opus 4.7. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub issue BerriAI/litellm#27967 (May 2026): When streaming fails mid-stream, Router.stream_with_fallbacks appends an assistant prefill block with prefix=True. Fallback targets that don’t support prefill (Claude Sonnet 4.6/Opus 4.7) reject with 400. The disable_fallbacks flag is also ignored mid-stream (#19077). Category mapping: LiteLLM (proxy/routing-specific behavior).

Common causes

• GitHub issue BerriAI/litellm#27967 (May 2026): When streaming fails mid-stream, Router.stream_with_fallbacks appends an assistant prefill block with prefix=True. Fallback targets that don’t support prefill (Claude Sonnet 4.6/Opus 4.7) reject with 400. The disable_fallbacks flag is also ignored mid-stream (#19077). Category mapping: LiteLLM (proxy/routing-specific behavior).

Quick fixes

1. Confirm the exact error signature matches litellm.BadRequestError: AnthropicException - {"type":"error","error":{"type":"invalid_request_error","message":"This model does not support assistant message prefill. The conversation must end with a user message."}}.
2. Check the LiteLLM 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.

Sources checked

• https://github.com/BerriAI/litellm/issues/27967

Evidence note: GitHub issue BerriAI/litellm#27967 (May 2026): When streaming fails mid-stream, Router.stream_with_fallbacks appends an assistant prefill block with prefix=True. Fallback targets that don’t support prefill (Claude Sonnet 4.6/Opus 4.7) reject with 400. The disable_fallbacks flag is also ignored mid-stream (#19077). Category mapping: LiteLLM (proxy/routing-specific behavior).

Related errors

• LiteLLM

FAQ

What should I check first?

Start with the exact litellm.BadRequestError: AnthropicException - {"type":"error","error":{"type":"invalid_request_error","message":"This model does not support assistant message prefill. The conversation must end with a user message."}} text and the smallest action that reproduces it.

Can I ignore this error?

No. Treat it as a failed LiteLLM workflow until the root cause is understood.

Is this guaranteed to have one fix?

No. The imported evidence supports the troubleshooting path above, but tool behavior can vary by account, plan, version, provider, and local configuration.

How do I know the fix worked?

Rerun the same command, editor action, or request. The fix is working when that action completes without litellm.BadRequestError: AnthropicException - {"type":"error","error":{"type":"invalid_request_error","message":"This model does not support assistant message prefill. The conversation must end with a user message."}}.