fix(proxy): report /cost/estimate rates from the call that billed them

The estimate looked the reported per-token rates up a second time, with the
provider this endpoint resolved rather than the one completion_cost infers.
The provider decides whether a token tier threshold is inclusive, so an
unrouted xai model sitting exactly on 200k billed at the tier rate and
reported the base rate, half of it.

completion_cost now hands back the rates its own lines were billed at, and
the endpoint reports those.

Claude-Session: https://claude.ai/code/session_01RLKy5DMi3XCBUJ37WzfNi1

litellm/cost_calculator.py

@@ -25,6 +25,7 @@ from litellm.litellm_core_utils.llm_cost_calc.usage_object_transformation import
     TranscriptionUsageObjectTransformation,
 )
 from litellm.litellm_core_utils.llm_cost_calc.utils import (
+    BilledTokenRates,
     CostCalculatorUtils,
     _generic_cost_per_character,
     _get_regional_uplift_multiplier,
@@ -1122,6 +1123,7 @@ def _store_cost_breakdown_in_logging_obj(
     service_tier: str | None = None,
     data_residency: str | None = None,
     vertex_location: str | None = None,
+    billed_token_rates: BilledTokenRates | None = None,
 ) -> None:
     """
     Helper function to store cost breakdown in the logging object.
@@ -1166,6 +1168,7 @@ def _store_cost_breakdown_in_logging_obj(
             service_tier=service_tier,
             data_residency=data_residency,
             vertex_location=vertex_location,
+            billed_token_rates=billed_token_rates,
         )
 
     except Exception as breakdown_error:
@@ -1735,6 +1738,7 @@ def completion_cost(
                     _reasoning_cost: float | None = None
                     _cache_read_cost: float | None = None
                     _cache_creation_cost: float | None = None
+                    _billed_token_rates: BilledTokenRates | None = None
                     if cost_per_token_usage_object is not None and model:
                         _breakdown_provider: str | None = (
                             custom_llm_provider if isinstance(custom_llm_provider, str) else None
@@ -1751,6 +1755,7 @@ def completion_cost(
                         _reasoning_cost = _token_type_breakdown.reasoning_cost
                         _cache_read_cost = _token_type_breakdown.cache_read_cost
                         _cache_creation_cost = _token_type_breakdown.cache_creation_cost
+                        _billed_token_rates = _token_type_breakdown.rates
                     _store_cost_breakdown_in_logging_obj(
                         litellm_logging_obj=litellm_logging_obj,
                         prompt_tokens_cost_usd_dollar=prompt_tokens_cost_usd_dollar,
@@ -1770,6 +1775,7 @@ def completion_cost(
                         service_tier=service_tier,
                         data_residency=data_residency,
                         vertex_location=vertex_location,
+                        billed_token_rates=_billed_token_rates,
                     )
 
                 return _final_cost

litellm/litellm_core_utils/litellm_logging.py

@@ -201,6 +201,7 @@ if TYPE_CHECKING:
     from mcp.types import EmbeddedResource, ImageContent, TextContent
 
     from litellm.integrations.otel.logger import OpenTelemetryV2
+    from litellm.litellm_core_utils.llm_cost_calc.utils import BilledTokenRates
     from litellm.llms.base_llm.passthrough.transformation import BasePassthroughConfig
 try:
     from litellm_enterprise.enterprise_callbacks.callback_controls import (
@@ -581,6 +582,7 @@ class Logging(LiteLLMLoggingBaseClass):
 
         # Initialize cost breakdown field
         self.cost_breakdown: CostBreakdown | None = None
+        self.billed_token_rates: BilledTokenRates | None = None
 
         # Init Caching related details
         self.caching_details: CachingDetails | None = None
@@ -1585,6 +1587,7 @@ class Logging(LiteLLMLoggingBaseClass):
         service_tier: str | None = None,
         data_residency: str | None = None,
         vertex_location: str | None = None,
+        billed_token_rates: "BilledTokenRates | None" = None,
     ) -> None:
         """
         Helper method to store cost breakdown in the logging object.
@@ -1604,8 +1607,10 @@ class Logging(LiteLLMLoggingBaseClass):
             service_tier: Tier the costs above were priced on, already resolved
             data_residency: Region uplift the costs above were priced on, already resolved
             vertex_location: Vertex AI location the costs above were priced on, already resolved
+            billed_token_rates: Per-token rates the costs above were billed at, already resolved
         """
 
+        self.billed_token_rates = billed_token_rates
         self.cost_breakdown = CostBreakdown(
             input_cost=input_cost,
             output_cost=output_cost,

litellm/litellm_core_utils/llm_cost_calc/utils.py

@@ -1302,34 +1302,6 @@ def _coerce_token_count(value: object) -> int:
     return value if isinstance(value, int) and value > 0 else 0
 
 
-@dataclass(frozen=True, slots=True)
-class TokenTypeCostBreakdown:
-    reasoning_cost: float
-    cache_read_cost: float
-    cache_creation_cost: float
-
-
-def _reasoning_token_count(usage: Usage) -> int:
-    parsed: Final = (
-        parse_completion_tokens_details(usage)["reasoning_tokens"] if usage.completion_tokens_details is not None else 0
-    )
-    return parsed or _coerce_token_count(getattr(usage, "reasoning_tokens", 0))
-
-
-def _cache_token_counts(usage: Usage) -> tuple[int, int, CacheCreationTokenDetails | None]:
-    """(cache read tokens, cache creation tokens, cache creation details): read from prompt_tokens_details
-    first, then the private top-level counters the Usage constructor mirrors cache tokens onto for
-    providers/callers that bypass the details."""
-    parsed: Final = parse_prompt_tokens_details(usage) if usage.prompt_tokens_details is not None else None
-    parsed_read: Final = parsed["cache_hit_tokens"] if parsed is not None else 0
-    parsed_creation: Final = parsed["cache_creation_tokens"] if parsed is not None else 0
-    return (
-        parsed_read or _coerce_token_count(getattr(usage, "_cache_read_input_tokens", 0)),
-        parsed_creation or _coerce_token_count(getattr(usage, "_cache_creation_input_tokens", 0)),
-        parsed["cache_creation_token_details"] if parsed is not None else None,
-    )
-
-
 @dataclass(frozen=True, slots=True)
 class BilledTokenRates:
     """Per-token rates one request's usage bills at, after token tiers, off-peak windows and the
@@ -1355,6 +1327,37 @@ class BilledTokenRates:
         )
 
 
+@dataclass(frozen=True, slots=True)
+class TokenTypeCostBreakdown:
+    reasoning_cost: float
+    cache_read_cost: float
+    cache_creation_cost: float
+    rates: BilledTokenRates | None = None
+    """Rates these lines were billed at, so a caller reporting both cannot resolve them a second,
+    differently-argued way. None when the model's pricing could not be resolved."""
+
+
+def _reasoning_token_count(usage: Usage) -> int:
+    parsed: Final = (
+        parse_completion_tokens_details(usage)["reasoning_tokens"] if usage.completion_tokens_details is not None else 0
+    )
+    return parsed or _coerce_token_count(getattr(usage, "reasoning_tokens", 0))
+
+
+def _cache_token_counts(usage: Usage) -> tuple[int, int, CacheCreationTokenDetails | None]:
+    """(cache read tokens, cache creation tokens, cache creation details): read from prompt_tokens_details
+    first, then the private top-level counters the Usage constructor mirrors cache tokens onto for
+    providers/callers that bypass the details."""
+    parsed: Final = parse_prompt_tokens_details(usage) if usage.prompt_tokens_details is not None else None
+    parsed_read: Final = parsed["cache_hit_tokens"] if parsed is not None else 0
+    parsed_creation: Final = parsed["cache_creation_tokens"] if parsed is not None else 0
+    return (
+        parsed_read or _coerce_token_count(getattr(usage, "_cache_read_input_tokens", 0)),
+        parsed_creation or _coerce_token_count(getattr(usage, "_cache_creation_input_tokens", 0)),
+        parsed["cache_creation_token_details"] if parsed is not None else None,
+    )
+
+
 def _custom_pricing_rates(custom_cost_per_token: CostPerToken) -> BilledTokenRates:
     """Flat custom pricing has no tiers, uplifts or reasoning rate: cache tokens bill at the configured
     cache rates (else the input rate) and reasoning at the output rate, as _cost_per_token_custom_pricing_helper does."""
@@ -1497,6 +1500,7 @@ def get_token_type_cost_breakdown(
         reasoning_cost=float(_reasoning_token_count(usage)) * rates.output_cost_per_reasoning_token,
         cache_read_cost=float(cache_read_tokens) * rates.cache_read_input_token_cost,
         cache_creation_cost=cache_creation_cost,
+        rates=rates,
     )
 
 

litellm/proxy/management_endpoints/cost_tracking_settings.py

@@ -21,7 +21,6 @@ import litellm
 from litellm._internal_context import current_billing_time, pinned_billing_time
 from litellm._logging import verbose_proxy_logger
 from litellm.cost_calculator import completion_cost
-from litellm.litellm_core_utils.llm_cost_calc.utils import get_billed_token_rates
 from litellm.proxy._types import (
     CommonProxyErrors,
     CostEstimateRequest,
@@ -85,9 +84,9 @@ def _extract_custom_pricing(
     )
 
 
-def _lookup_model_info(model: str) -> ModelInfo | None:
+def _lookup_model_info(model: str, custom_llm_provider: str | None = None) -> ModelInfo | None:
     try:
-        return litellm.get_model_info(model=model)
+        return litellm.get_model_info(model=model, custom_llm_provider=custom_llm_provider)
     except Exception:
         return None
 
@@ -122,7 +121,7 @@ def _resolve_model_for_cost_lookup(model: str) -> ResolvedCostModel:
                 if resolved_model:
                     verbose_proxy_logger.debug("Resolved model '%s' to '%s' from router", model, resolved_model)
                     custom_cost_per_token: Final = _extract_custom_pricing(
-                        litellm_params, model_info, _lookup_model_info(str(resolved_model))
+                        litellm_params, model_info, _lookup_model_info(str(resolved_model), provider)
                     )
                     return ResolvedCostModel(str(resolved_model), provider, custom_cost_per_token)
         except Exception as e:
@@ -632,10 +631,9 @@ async def estimate_cost(
         function_id="cost-estimate",
     )
 
-    # The totals, the per-token-type lines and the reported rates each resolve pricing on their
-    # own path. Pinning one moment keeps an off-peak window that opens mid-quote from splitting them.
-    billed_at: Final = current_billing_time()
-    with pinned_billing_time(billed_at):
+    # Pinning one moment keeps an off-peak window that opens mid-quote from pricing the totals on
+    # one side of it and the reported rates on the other.
+    with pinned_billing_time(current_billing_time()):
         # Use completion_cost which handles all the logic including margins/discounts
         try:
             cost_per_request: Final = completion_cost(
@@ -653,19 +651,16 @@ async def estimate_cost(
                 },
             )
 
-        rates: Final = get_billed_token_rates(
-            model=resolved_model,
-            custom_llm_provider=resolved_provider,
-            usage=usage,
-            custom_cost_per_token=resolved.custom_cost_per_token,
-            current_time=billed_at,
-        )
-
+    # The rates come back from the pricing call itself rather than a second lookup, so they are the
+    # ones the cost lines above billed at even when completion_cost infers a provider this endpoint
+    # never resolved (an unrouted "xai/grok-4" prices on xai's inclusive tier thresholds; a lookup
+    # here without that provider would report the sub-200k rate for a line billed above it).
+    rates: Final = litellm_logging_obj.billed_token_rates
     per_request: Final = _cost_lines(cost_per_request, litellm_logging_obj.cost_breakdown)
     daily: Final = per_request.times(request.num_requests_per_day)
     monthly: Final = per_request.times(request.num_requests_per_month)
 
-    model_info: Final = _lookup_model_info(resolved_model)
+    model_info: Final = _lookup_model_info(resolved_model, resolved_provider)
     mapped_provider: Final = model_info.get("litellm_provider") if model_info is not None else None
     custom_llm_provider: Final = mapped_provider if mapped_provider is not None else resolved_provider
 

tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py

@@ -33,7 +33,6 @@ from litellm.litellm_core_utils.llm_cost_calc.utils import (
     CostCalculatorUtils,
     PromptTokensDetailsResult,
     TokenRates,
-    TokenTypeCostBreakdown,
     _calculate_input_cost,
     _get_token_base_cost,
     _is_off_peak,
@@ -4023,6 +4022,49 @@ def test_a_pinned_billing_time_prices_the_totals_and_the_reported_rates_at_one_m
     assert peak_completion_cost == pytest.approx(500 * peak_rates.output_cost_per_token)
 
 
+def test_the_token_type_breakdown_carries_the_rates_it_billed_at(monkeypatch):
+    """Callers that report both the lines and the rates read the rates off the breakdown rather than
+    resolving them a second time, so the breakdown has to hand back exactly what it billed at."""
+    monkeypatch.setitem(
+        litellm.model_cost,
+        "xai/tiered-model",
+        {
+            "input_cost_per_token": 3e-6,
+            "output_cost_per_token": 15e-6,
+            "cache_read_input_token_cost": 3e-7,
+            "input_cost_per_token_above_200k_tokens": 6e-6,
+            "output_cost_per_token_above_200k_tokens": 3e-5,
+            "cache_read_input_token_cost_above_200k_tokens": 6e-7,
+            "litellm_provider": "xai",
+            "mode": "chat",
+        },
+    )
+    usage = Usage(
+        prompt_tokens=200_000,
+        completion_tokens=1_000,
+        total_tokens=201_000,
+        prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=100_000),
+    )
+
+    breakdown = get_token_type_cost_breakdown(model="xai/tiered-model", custom_llm_provider="xai", usage=usage)
+
+    assert breakdown.rates == get_billed_token_rates(
+        model="xai/tiered-model", custom_llm_provider="xai", usage=usage
+    )
+    assert breakdown.rates.cache_read_input_token_cost == pytest.approx(6e-7)
+    assert breakdown.cache_read_cost == pytest.approx(100_000 * breakdown.rates.cache_read_input_token_cost)
+
+
+def test_the_token_type_breakdown_reports_no_rates_for_an_unpriced_model():
+    usage = Usage(prompt_tokens=10, completion_tokens=5, total_tokens=15)
+
+    breakdown = get_token_type_cost_breakdown(
+        model="no-such-model-anywhere", custom_llm_provider="openai", usage=usage
+    )
+
+    assert breakdown.rates is None
+
+
 def test_billed_token_rates_are_none_for_an_unpriced_model():
     usage = Usage(prompt_tokens=10, completion_tokens=5, total_tokens=15)
 
@@ -4036,9 +4078,7 @@ def test_token_type_cost_breakdown_zero_without_special_tokens(_local_model_cost
         model="gpt-4o", custom_llm_provider="openai", usage=usage
     )
 
-    assert breakdown == TokenTypeCostBreakdown(
-        reasoning_cost=0.0, cache_read_cost=0.0, cache_creation_cost=0.0
-    )
+    assert (breakdown.reasoning_cost, breakdown.cache_read_cost, breakdown.cache_creation_cost) == (0.0, 0.0, 0.0)
 
 
 @pytest.mark.parametrize(
@@ -4110,9 +4150,7 @@ def test_token_type_cost_breakdown_handles_unknown_model_gracefully():
             completion_tokens_details=CompletionTokensDetailsWrapper(reasoning_tokens=5),
         ),
     )
-    assert breakdown == TokenTypeCostBreakdown(
-        reasoning_cost=0.0, cache_read_cost=0.0, cache_creation_cost=0.0
-    )
+    assert (breakdown.reasoning_cost, breakdown.cache_read_cost, breakdown.cache_creation_cost) == (0.0, 0.0, 0.0)
 
 
 def test_token_type_cost_breakdown_applies_regional_uplift(_local_model_cost_map):

tests/test_litellm/proxy/management_endpoints/test_cost_tracking_settings.py

@@ -1139,6 +1139,45 @@ class TestEstimateCostCacheAndReasoningTokens:
         assert response.output_cost_per_request == pytest.approx(OUTPUT_TOKENS * response.output_cost_per_token)
 
 
+    @pytest.mark.asyncio
+    async def test_an_unrouted_model_reports_the_rates_of_the_provider_the_calculator_inferred(self, monkeypatch):
+        """The cost calculator infers a provider this endpoint never resolved, and the provider decides
+        whether a tier threshold is inclusive. xai bills a request sitting exactly on the 200k threshold
+        at the tier rate, so the reported rates have to be the tier's rather than the sub-tier base."""
+        an_xai_model = "xai/tiered-model"
+        monkeypatch.setitem(
+            litellm.model_cost,
+            an_xai_model,
+            {
+                "input_cost_per_token": 3e-6,
+                "output_cost_per_token": 15e-6,
+                "cache_read_input_token_cost": 3e-7,
+                "input_cost_per_token_above_200k_tokens": 6e-6,
+                "output_cost_per_token_above_200k_tokens": 3e-5,
+                "cache_read_input_token_cost_above_200k_tokens": 6e-7,
+                "litellm_provider": "xai",
+                "mode": "chat",
+            },
+        )
+
+        response = await _estimate(
+            None,
+            model=an_xai_model,
+            input_tokens=200_000,
+            cache_read_input_tokens=100_000,
+            output_tokens=1_000,
+        )
+
+        assert response.input_cost_per_token == pytest.approx(6e-6)
+        assert response.output_cost_per_token == pytest.approx(3e-5)
+        assert response.cache_read_input_token_cost == pytest.approx(6e-7)
+        assert response.cache_read_cost_per_request == pytest.approx(100_000 * response.cache_read_input_token_cost)
+        assert response.input_cost_per_request == pytest.approx(
+            100_000 * response.input_cost_per_token + response.cache_read_cost_per_request
+        )
+        assert response.output_cost_per_request == pytest.approx(1_000 * response.output_cost_per_token)
+
+
 class TestCostEstimateRequestTokenSubsets:
     def test_cache_tokens_beyond_the_input_tokens_are_rejected(self):
         with pytest.raises(ValidationError, match="cannot exceed input_tokens"):

tests/test_litellm/test_cost_calculator.py

@@ -3736,6 +3736,62 @@ def test_completion_cost_logs_reasoning_and_cache_breakdown(_local_model_cost_ma
     assert logging_obj.cost_breakdown["cache_read_cost"] == pytest.approx(100 * 3e-08)
 
 
+def test_completion_cost_logs_the_rates_it_billed_at(monkeypatch):
+    """A caller reporting the cost lines beside their per-token rates reads both off this one call.
+    completion_cost infers the provider, and xai's inclusive tier thresholds put a request sitting
+    exactly on 200k at the tier rate, which a lookup made without that inferred provider would miss.
+    """
+    from datetime import datetime
+
+    from litellm.litellm_core_utils.litellm_logging import Logging
+
+    monkeypatch.setitem(
+        litellm.model_cost,
+        "xai/tiered-model",
+        {
+            "input_cost_per_token": 3e-6,
+            "output_cost_per_token": 15e-6,
+            "cache_read_input_token_cost": 3e-7,
+            "input_cost_per_token_above_200k_tokens": 6e-6,
+            "output_cost_per_token_above_200k_tokens": 3e-5,
+            "cache_read_input_token_cost_above_200k_tokens": 6e-7,
+            "litellm_provider": "xai",
+            "mode": "chat",
+        },
+    )
+    logging_obj = Logging(
+        model="xai/tiered-model",
+        messages=[{"role": "user", "content": "Hello"}],
+        stream=False,
+        call_type="completion",
+        start_time=datetime.now(),
+        litellm_call_id="billed-rates",
+        function_id="f",
+    )
+    usage = Usage(
+        prompt_tokens=200_000,
+        completion_tokens=1_000,
+        total_tokens=201_000,
+        prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=100_000),
+    )
+
+    litellm.completion_cost(
+        completion_response=ModelResponse(model="xai/tiered-model", usage=usage),
+        model="xai/tiered-model",
+        custom_llm_provider=None,
+        litellm_logging_obj=logging_obj,
+    )
+
+    rates = logging_obj.billed_token_rates
+    assert rates is not None
+    assert rates.input_cost_per_token == pytest.approx(6e-6)
+    assert rates.cache_read_input_token_cost == pytest.approx(6e-7)
+    assert logging_obj.cost_breakdown["cache_read_cost"] == pytest.approx(
+        100_000 * rates.cache_read_input_token_cost
+    )
+    assert logging_obj.cost_breakdown["output_cost"] == pytest.approx(1_000 * rates.output_cost_per_token)
+
+
 def test_completion_cost_logs_cache_and_reasoning_breakdown_for_custom_pricing():
     """
     A custom-priced deployment bills cache tokens at its custom cache rates, but the