What this error means

litellm.CallFailed: Error code: 404 — OpenRouter model IDs stopped working after LiteLLM upgrade to v1.82.1 is a LiteLLM failure pattern reported for developers trying to after upgrading to litellm 1.82.1, previously working openrouter model ids throw 404 errors. users proxying through litellm proxy lose access to openrouter models and need quick workaround to restore routing to healthy deployments.. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub issue #22667 on BerriAI/litellm: 'LiteLLM 1.82.1 OpenRouter Model IDs no longer working'. Regression introduced in v1.82.1 breaking proxy routes to OpenRouter. Related: issue #24795 'OpenRouter model selected from Admin UI list fails Test Connect with is not a valid model ID'. Also issue #27362 'APIConnectionError hardcoded in cooldown_handlers.py prevents failover to healthy deployments'. These are all regression-style bugs affecting paying users who route production traffic through LiteLLM. Category: LiteLLM.

Common causes

  • GitHub issue #22667 on BerriAI/litellm: 'LiteLLM 1.82.1 OpenRouter Model IDs no longer working'. Regression introduced in v1.82.1 breaking proxy routes to OpenRouter. Related: issue #24795 'OpenRouter model selected from Admin UI list fails Test Connect with is not a valid model ID'. Also issue #27362 'APIConnectionError hardcoded in cooldown_handlers.py prevents failover to healthy deployments'. These are all regression-style bugs affecting paying users who route production traffic through LiteLLM. Category: LiteLLM.

Quick fixes

  1. Confirm the exact error signature matches litellm.CallFailed: Error code: 404 — OpenRouter model IDs stopped working after LiteLLM upgrade to v1.82.1.
  2. Check the LiteLLM account, local tool state, and provider configuration involved in the failing workflow.
  3. Check the build output, project root, and deployment platform configuration before redeploying.

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.