> ## Documentation Index
> Fetch the complete documentation index at: https://braintrust.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Context length error caused by wrong AI provider routing

export const plans_0 = "Any"

export const deployments_0 = "Any"

export const data_plane_version_0 = undefined

export const use_case_0 = "Use case - Debugging unexpected context length errors when using a model that should support a larger context window, caused by Braintrust routing to a different AI provider"

<Note>
  **Applies to:**

  * Plan - {plans_0}
  * Deployment - {deployments_0}
  * {data_plane_version_0}
  * {use_case_0}
</Note>

## Summary

**Issue:** Requests to a model with a large context window return a `context_length_exceeded` error with a smaller token limit than expected.

**Cause:** Braintrust load balances across all providers that support a matching model name. If there are multiple providers with matching model names, requests can silently use secondary provider that may have a different underlying model with smaller limits.

**Resolution:** Disable/remove secondary providers that don't have the correct model deployed or have different limits.

## Resolution steps

### Step 1: Confirm which provider handled the request

Open the failed run's trace in Braintrust and check:

* The purple chat completion span (not the root span)
* `metadata.model` — confirms which model ID was sent to the provider

If the model ID matches what you expected but the error shows a smaller context limit, the request may have been routed to a provider with a different underlying deployment.

### Step 2: Fix provider configuration

Go to **Settings > AI providers** and do one of the following:

**Option A — Fix the primary provider:** Update the configuration for your providers to match actual limits.

**Option B — Remove/disable additional providers with matching models:** Remove or disable any providers that don't have the correct model deployed, or that map to older/smaller versions of the model.

**Option C — Check custom model mappings:** If using custom endpoints or Azure deployments, verify that the deployment name actually corresponds to the model you expect.

> **Note:** Braintrust automatically load balances across all providers where a model name matches. Any provider with a matching model name will receive requests, even if the underlying deployment differs.

### Step 4: Verify the fix

Re-run your prompt with cache disabled to confirm requests now route to the correct provider and the context length error is resolved.