What this error means

Error transforming OpenRouter image generation response: 2 validation errors for ImageUsage output_tokens: Input should be a valid integer, got a number with a fractional part [type=int_from_float, input_value=14417.92] is a LiteLLM failure pattern reported for developers trying to fix litellm openrouter image generation failure with imageusage pydantic validation error. 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#28001 (May 15, 2026): When generating images via OpenRouter through dockerized LiteLLM, the response contains fractional token counts (float) but ImageUsage Pydantic model expects integers. Causes APIError for all OpenRouter image generation requests. Category: LiteLLM (proxy/model routing layer).

Common causes

  • GitHub issue BerriAI/litellm#28001 (May 15, 2026): When generating images via OpenRouter through dockerized LiteLLM, the response contains fractional token counts (float) but ImageUsage Pydantic model expects integers. Causes APIError for all OpenRouter image generation requests. Category: LiteLLM (proxy/model routing layer).

Quick fixes

  1. Confirm the exact error signature matches Error transforming OpenRouter image generation response: 2 validation errors for ImageUsage output_tokens: Input should be a valid integer, got a number with a fractional part [type=int_from_float, input_value=14417.92].
  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.