LiteLLM / LiteLLM

cost_per_token returns silently wrong values for unrecognized models in LiteLLM

Detect when LiteLLM proxy routes requests to models it doesn't recognize pricing for — silent zero-cost masking real token costs impacts billing accuracy Includes evidence for LiteLLM troubleshooting demand.

Source-backed Last updated May 23, 2026 1 source Needs local verification

Category: LiteLLM
Error signature: cost_per_token() in cost_calculator.py returns (0.0, 0.0) instead of raising error for unknown/unrecognized models
Quick fix: Compare the failing environment with a known working setup, then change one configuration value at a time.
Updated: May 23, 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

cost_per_token() in cost_calculator.py returns (0.0, 0.0) instead of raising error for unknown/unrecognized models is a LiteLLM failure pattern reported for developers trying to detect when litellm proxy routes requests to models it doesn't recognize pricing for — silent zero-cost masking real token costs impacts billing accuracy. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

Found on BerriAI/litellm#27581 (opened May 10, 2026). When LiteLLM encounters an unrecognized model, cost_per_token returns (0.0, 0.0) silently rather than raising an error. This can cause production deployments to underbill or misreport API costs. Category: LiteLLM SDK behavior bug.

Common causes

Found on BerriAI/litellm#27581 (opened May 10, 2026). When LiteLLM encounters an unrecognized model, cost_per_token returns (0.0, 0.0) silently rather than raising an error. This can cause production deployments to underbill or misreport API costs. Category: LiteLLM SDK behavior bug.

Quick fixes

Confirm the exact error signature matches cost_per_token() in cost_calculator.py returns (0.0, 0.0) instead of raising error for unknown/unrecognized models.
Check the LiteLLM account, local tool state, and provider configuration involved in the failing workflow.
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

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 cost_per_token() in cost_calculator.py returns (0.0, 0.0) instead of raising error for unknown/unrecognized 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 cost_per_token() in cost_calculator.py returns (0.0, 0.0) instead of raising error for unknown/unrecognized models mean in LiteLLM?

cost_per_token() in cost_calculator.py returns (0.0, 0.0) instead of raising error for unknown/unrecognized models indicates the workflow described by "cost_per_token returns silently wrong values for unrecognized models in LiteLLM" failed in a LiteLLM context. Start by confirming the exact signature and the component that emitted it.

What should I check first for cost_per_token() in cost_calculator.py returns (0.0, 0.0) instead of raising error for unknown/unrecognized models?

Check whether this cause matches your environment: Found on BerriAI/litellm#27581 (opened May 10, 2026). When LiteLLM encounters an unrecognized model, cost_per_token returns (0.0, 0.0) silently rather than raising an error. This can cause production deployments to underbill or misreport API costs. Category: LiteLLM SDK behavior bug.

Is the cost_per_token() in cost_calculator.py returns (0.0, 0.0) instead of raising error for unknown/unrecognized models guidance source-backed?

The "cost_per_token returns silently wrong values for unrecognized models in LiteLLM" 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 cost_per_token() in cost_calculator.py returns (0.0, 0.0) instead of raising error for unknown/unrecognized models?

For "cost_per_token returns silently wrong values for unrecognized models in LiteLLM", avoid applying "Compare the failing environment with a known working setup, then change one configuration value at a time." 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 cost_per_token() in cost_calculator.py returns (0.0, 0.0) instead of raising error for unknown/unrecognized models?

For "cost_per_token returns silently wrong values for unrecognized models in LiteLLM", 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.