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fix(azure_ai): charge the router fee once for any router name and price grok-4-20 cache reads
Direct litellm.cost_per_token callers that name a Model Router deployment as the model get the routing fee again, as they did before this branch, and the fee is still charged exactly once on every completion_cost path. The grok-4-20 entries bill cached prompt tokens at the input rate, since Azure has no cached-input meter for them, and the model_router twin carries the same limits and retirement date as model-router. The catalog test now exercises the cost calculator and map relations instead of pinning map fields.
This commit is contained in:
mateo-berri
parent
c02f2dc0fe
commit
55c10c1983
6 changed files with 125 additions and 174 deletions
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@ -46,7 +46,7 @@ from litellm.llms.azure_ai.cost_calculator import (
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cost_per_token as azure_ai_cost_per_token,
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)
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from litellm.llms.azure_ai.cost_calculator import (
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is_router_fee_entry as azure_ai_is_router_fee_entry,
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is_azure_model_router as azure_ai_is_model_router_name,
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)
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from litellm.llms.base_llm.search.transformation import SearchResponse
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from litellm.llms.bedrock.cost_calculation import (
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@ -1665,7 +1665,7 @@ def completion_cost(
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)
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# Get additional costs from provider (e.g., routing fees, infrastructure costs)
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if custom_llm_provider == "azure_ai" and not azure_ai_is_router_fee_entry(model):
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if custom_llm_provider == "azure_ai" and not azure_ai_is_model_router_name(model):
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model_for_additional_costs = request_model_for_cost
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if completion_response is not None:
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hidden_params = getattr(completion_response, "_hidden_params", None) or {}
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@ -11,7 +11,7 @@ from litellm.types.utils import Usage
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from litellm.utils import get_model_info
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def _is_azure_model_router(model: str) -> bool:
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def is_azure_model_router(model: str) -> bool:
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"""
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Check if the model is Azure AI Foundry Model Router.
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@ -54,7 +54,7 @@ def calculate_azure_model_router_flat_cost(model: str, prompt_tokens: int) -> fl
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Returns:
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float: The flat cost in USD, or 0.0 if not applicable
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"""
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if not _is_azure_model_router(model):
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if not is_azure_model_router(model):
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return 0.0
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model_info: Final = get_model_info(model=_router_fee_entry_name(model), custom_llm_provider="azure_ai")
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router_flat_cost_per_token: Final = model_info.get("input_cost_per_token", 0)
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@ -69,7 +69,7 @@ def _response_model_cost(model: str, usage: Usage, service_tier: str | None) ->
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model=model, usage=usage, custom_llm_provider="azure_ai", service_tier=service_tier
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)
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except Exception as e:
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if not _is_azure_model_router(model):
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if not is_azure_model_router(model):
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raise
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verbose_logger.debug(
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"Azure AI Model Router: model '%s' not in cost map, only the routing fee applies. Error: %s", model, e
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@ -77,6 +77,16 @@ def _response_model_cost(model: str, usage: Usage, service_tier: str | None) ->
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return 0.0, 0.0
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def _router_fee_name(model: str, request_model: str | None) -> str | None:
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if is_router_fee_entry(model):
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return None
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if is_azure_model_router(model):
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return model
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if request_model is not None and is_azure_model_router(request_model):
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return request_model
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return None
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def cost_per_token(
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model: str,
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usage: Usage,
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@ -85,13 +95,15 @@ def cost_per_token(
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service_tier: str | None = None,
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) -> tuple[float, float]:
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"""
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Price the response model's own tokens for Azure AI, plus the Model Router fee when the caller names the
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router as the request model.
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Price the response model's own tokens for Azure AI, plus the Model Router fee exactly once when either the
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priced name or request_model is a Model Router name.
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completion_cost never passes request_model: it charges the fee once through
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A response priced as the router entry itself already carries the fee, so nothing is added on top of it. A
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router deployment name that is missing from the cost map prices at the fee alone.
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completion_cost passes only the priced name: when that name is a routed model it adds the fee itself through
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AzureModelRouterConfig.calculate_additional_costs as the "Azure Model Router Flat Cost" line of the cost
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breakdown. A response priced as the router entry itself already carries the fee, so request_model adds
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nothing on top of it, and a router deployment name that is missing from the cost map prices at zero here.
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breakdown, and when the name is router-shaped the fee is already in the prompt cost returned here.
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Args:
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model: str, the model name without provider prefix (from response)
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@ -107,6 +119,7 @@ def cost_per_token(
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ValueError: If a model that is not a Model Router name is missing from the cost map
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"""
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prompt_cost, completion_cost = _response_model_cost(model=model, usage=usage, service_tier=service_tier)
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if request_model is None or not _is_azure_model_router(request_model) or is_router_fee_entry(model):
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fee_name: Final = _router_fee_name(model=model, request_model=request_model)
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if fee_name is None:
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return prompt_cost, completion_cost
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return prompt_cost + calculate_azure_model_router_flat_cost(request_model, usage.prompt_tokens), completion_cost
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return prompt_cost + calculate_azure_model_router_flat_cost(fee_name, usage.prompt_tokens), completion_cost
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@ -4060,9 +4060,13 @@
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"supports_minimal_reasoning_effort": false
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},
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"azure_ai/model_router": {
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"deprecation_date": "2027-05-20",
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"input_cost_per_token": 1.4e-07,
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"output_cost_per_token": 0,
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"litellm_provider": "azure_ai",
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"max_input_tokens": 200000,
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"max_output_tokens": 32768,
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"max_tokens": 32768,
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"mode": "chat",
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"source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/aoai/",
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"comment": "Flat cost of $0.14 per M input tokens for Azure AI Foundry Model Router infrastructure. Use pattern: azure_ai/model_router/<deployment-name> where deployment-name is your Azure deployment (e.g., azure-model-router)"
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@ -10754,6 +10758,7 @@
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"supports_web_search": true
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},
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"azure_ai/grok-4-20-reasoning": {
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"cache_read_input_token_cost": 1.25e-06,
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"deprecation_date": "2027-04-06",
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"input_cost_per_token": 1.25e-06,
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"litellm_provider": "azure_ai",
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@ -10771,6 +10776,7 @@
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"supports_reasoning": true
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},
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"azure_ai/grok-4-20-non-reasoning": {
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"cache_read_input_token_cost": 1.25e-06,
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"deprecation_date": "2027-04-06",
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"input_cost_per_token": 1.25e-06,
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"litellm_provider": "azure_ai",
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@ -4060,9 +4060,13 @@
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"supports_minimal_reasoning_effort": false
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},
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"azure_ai/model_router": {
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"deprecation_date": "2027-05-20",
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"input_cost_per_token": 1.4e-07,
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"output_cost_per_token": 0,
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"litellm_provider": "azure_ai",
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"max_input_tokens": 200000,
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"max_output_tokens": 32768,
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"max_tokens": 32768,
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"mode": "chat",
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"source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/aoai/",
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"comment": "Flat cost of $0.14 per M input tokens for Azure AI Foundry Model Router infrastructure. Use pattern: azure_ai/model_router/<deployment-name> where deployment-name is your Azure deployment (e.g., azure-model-router)"
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@ -10754,6 +10758,7 @@
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"supports_web_search": true
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},
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"azure_ai/grok-4-20-reasoning": {
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"cache_read_input_token_cost": 1.25e-06,
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"deprecation_date": "2027-04-06",
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"input_cost_per_token": 1.25e-06,
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"litellm_provider": "azure_ai",
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@ -10771,6 +10776,7 @@
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"supports_reasoning": true
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},
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"azure_ai/grok-4-20-non-reasoning": {
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"cache_read_input_token_cost": 1.25e-06,
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"deprecation_date": "2027-04-06",
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"input_cost_per_token": 1.25e-06,
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"litellm_provider": "azure_ai",
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@ -11,9 +11,9 @@ import litellm
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from litellm.cost_calculator import completion_cost
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from litellm.litellm_core_utils.litellm_logging import Logging
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from litellm.llms.azure_ai.cost_calculator import (
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_is_azure_model_router,
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calculate_azure_model_router_flat_cost,
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cost_per_token,
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is_azure_model_router,
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)
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from litellm.types.utils import Choices, Message, ModelResponse, Usage
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from litellm.utils import get_model_info
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@ -54,7 +54,7 @@ class TestAzureModelRouterDetection:
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)
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def test_is_azure_model_router(self, model: str, expected: bool):
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"""Test Azure Model Router detection."""
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assert _is_azure_model_router(model) == expected
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assert is_azure_model_router(model) == expected
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class TestAzureModelRouterPrefix:
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@ -130,11 +130,21 @@ def _routed_model_cost() -> tuple[float, float]:
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@pytest.mark.usefixtures("local_model_cost_map")
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class TestAzureModelRouterFlatCost:
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"""cost_per_token prices the response model only; the router fee is the cost breakdown's own line item."""
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"""cost_per_token charges the router fee once, for whichever router name the caller gives it."""
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def test_unmapped_router_deployment_name_prices_at_zero(self) -> None:
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def test_unmapped_router_deployment_name_prices_the_fee(self) -> None:
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usage = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500)
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assert cost_per_token(model="azure-model-router", usage=usage) == (0.0, 0.0)
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prompt_cost, completion_cost_usd = cost_per_token(model="azure-model-router", usage=usage)
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assert prompt_cost == pytest.approx(1000 * ROUTER_FEE_PER_TOKEN, rel=1e-9)
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assert completion_cost_usd == 0.0
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def test_router_deployment_name_as_both_names_charges_the_fee_once(self) -> None:
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usage = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500)
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prompt_cost, completion_cost_usd = cost_per_token(
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model="model_router/my-deployment", usage=usage, request_model="azure_ai/model_router/my-deployment"
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)
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assert prompt_cost == pytest.approx(1000 * ROUTER_FEE_PER_TOKEN, rel=1e-9)
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assert completion_cost_usd == 0.0
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@pytest.mark.parametrize("router_entry_name", ["model_router", "model-router"])
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def test_router_entry_prices_its_own_fee(self, router_entry_name: str) -> None:
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@ -209,7 +219,8 @@ class TestAzureModelRouterFlatCost:
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@pytest.mark.usefixtures("local_model_cost_map")
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class TestAzureModelRouterCostBreakdown:
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"""completion_cost charges the router fee exactly once, as the cost breakdown's additional cost line."""
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"""completion_cost charges the router fee exactly once: as the breakdown's additional cost line when a routed
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model is priced as itself, inside the input cost when the priced name is the router."""
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def test_unmapped_router_deployment_name_costs_only_the_fee(self) -> None:
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cost = completion_cost(
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@ -219,7 +230,7 @@ class TestAzureModelRouterCostBreakdown:
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)
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assert cost == pytest.approx(ROUTED_FEE, rel=1e-9)
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def test_fee_is_the_breakdown_line_item_for_an_unmapped_router_name(self) -> None:
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def test_unmapped_router_name_carries_the_fee_as_its_input_cost(self) -> None:
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logging_obj = _router_logging("azure-model-router")
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cost = completion_cost(
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completion_response=_azure_ai_response("azure-model-router"),
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@ -229,10 +240,8 @@ class TestAzureModelRouterCostBreakdown:
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)
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breakdown = logging_obj.cost_breakdown
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assert breakdown is not None
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assert breakdown["input_cost"] == 0.0
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assert breakdown.get("additional_costs") == pytest.approx(
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{"Azure Model Router Flat Cost": ROUTED_FEE}, rel=1e-9
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)
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assert breakdown["input_cost"] == pytest.approx(ROUTED_FEE, rel=1e-9)
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assert "additional_costs" not in breakdown
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assert cost == pytest.approx(ROUTED_FEE, rel=1e-9)
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def test_router_request_with_routed_response_charges_the_fee_once(self) -> None:
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@ -5,131 +5,34 @@ from typing import Final
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import pytest
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from pydantic import TypeAdapter
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from litellm import cost_per_token, get_model_info
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from litellm import completion_cost, cost_per_token
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from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
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from litellm.types.utils import TranscriptionResponse
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REPO_ROOT: Final = Path(__file__).parents[4]
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MAIN_COST_MAP: Final = REPO_ROOT / "model_prices_and_context_window.json"
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BACKUP_COST_MAP: Final = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json"
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COST_MAP_ADAPTER: Final = TypeAdapter(dict[str, dict[str, object]])
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AZURE_OPENAI_PRICING: Final = "https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/"
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FOUNDRY_AOAI_PRICING: Final = "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/aoai/"
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FOUNDRY_COHERE_PRICING: Final = "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/cohere/"
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FOUNDRY_GROK_PRICING: Final = "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/grok/"
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AZURE_PRICING_PREFIX: Final = "https://azure.microsoft.com/en-us/pricing/details/"
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A_MILLION: Final = 1_000_000
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@dataclass(frozen=True, slots=True)
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class TokenPricedCatalogModel:
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catalog_name: str
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mode: str
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source: str
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input_cost_per_token: float
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output_cost_per_token: float
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max_input_tokens: int
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max_output_tokens: int
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cache_read_input_token_cost: float | None
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deprecation_date: str | None
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supported_flags: tuple[str, ...]
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dollars_per_million_input: float
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dollars_per_million_output: float
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TOKEN_PRICED_MODELS: Final = (
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TokenPricedCatalogModel(
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catalog_name="gpt-chat-latest",
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mode="chat",
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source=AZURE_OPENAI_PRICING,
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input_cost_per_token=5e-06,
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output_cost_per_token=3e-05,
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max_input_tokens=272000,
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max_output_tokens=128000,
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cache_read_input_token_cost=5e-07,
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deprecation_date="2026-12-02",
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supported_flags=(
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"supports_function_calling",
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"supports_prompt_caching",
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"supports_reasoning",
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"supports_response_schema",
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"supports_tool_choice",
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"supports_vision",
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"supports_web_search",
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),
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),
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TokenPricedCatalogModel(
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catalog_name="codex-mini",
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mode="responses",
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source=AZURE_OPENAI_PRICING,
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input_cost_per_token=1.5e-06,
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output_cost_per_token=6e-06,
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max_input_tokens=200000,
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max_output_tokens=100000,
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cache_read_input_token_cost=3.75e-07,
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deprecation_date="2026-11-15",
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supported_flags=(
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"supports_function_calling",
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"supports_prompt_caching",
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"supports_reasoning",
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"supports_vision",
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),
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),
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TokenPricedCatalogModel(
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catalog_name="model-router",
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mode="chat",
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source=FOUNDRY_AOAI_PRICING,
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input_cost_per_token=1.4e-07,
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output_cost_per_token=0.0,
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max_input_tokens=200000,
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max_output_tokens=32768,
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cache_read_input_token_cost=None,
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deprecation_date="2027-05-20",
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supported_flags=(),
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),
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TokenPricedCatalogModel(
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catalog_name="cohere-command-a",
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mode="chat",
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source=FOUNDRY_COHERE_PRICING,
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input_cost_per_token=2.5e-06,
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output_cost_per_token=1e-05,
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max_input_tokens=131072,
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max_output_tokens=8182,
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cache_read_input_token_cost=None,
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deprecation_date=None,
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supported_flags=("supports_function_calling", "supports_tool_choice"),
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),
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TokenPricedCatalogModel(
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catalog_name="grok-4-20-reasoning",
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mode="chat",
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source=FOUNDRY_GROK_PRICING,
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input_cost_per_token=1.25e-06,
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output_cost_per_token=2.5e-06,
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max_input_tokens=262000,
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max_output_tokens=8192,
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cache_read_input_token_cost=None,
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deprecation_date="2027-04-06",
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supported_flags=(
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"supports_function_calling",
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"supports_reasoning",
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"supports_response_schema",
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"supports_tool_choice",
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"supports_vision",
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"supports_web_search",
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),
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),
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TokenPricedCatalogModel(
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catalog_name="grok-4-20-non-reasoning",
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mode="chat",
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source=FOUNDRY_GROK_PRICING,
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input_cost_per_token=1.25e-06,
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output_cost_per_token=2.5e-06,
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max_input_tokens=262000,
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max_output_tokens=8192,
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cache_read_input_token_cost=None,
|
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deprecation_date="2027-04-06",
|
||||
supported_flags=(
|
||||
"supports_function_calling",
|
||||
"supports_response_schema",
|
||||
"supports_tool_choice",
|
||||
"supports_vision",
|
||||
"supports_web_search",
|
||||
),
|
||||
),
|
||||
TokenPricedCatalogModel("gpt-chat-latest", 5.0, 30.0),
|
||||
TokenPricedCatalogModel("codex-mini", 1.5, 6.0),
|
||||
TokenPricedCatalogModel("model-router", 0.14, 0.0),
|
||||
TokenPricedCatalogModel("cohere-command-a", 2.5, 10.0),
|
||||
TokenPricedCatalogModel("grok-4-20-reasoning", 1.25, 2.5),
|
||||
TokenPricedCatalogModel("grok-4-20-non-reasoning", 1.25, 2.5),
|
||||
)
|
||||
GROK_4_20_NAMES: Final = ("grok-4-20-reasoning", "grok-4-20-non-reasoning")
|
||||
CATALOG_NAMES: Final = tuple(spec.catalog_name for spec in TOKEN_PRICED_MODELS) + ("whisper",)
|
||||
|
||||
|
||||
|
|
@ -137,60 +40,74 @@ def _cost_map_entry(path: Path, catalog_name: str) -> dict[str, object]:
|
|||
return COST_MAP_ADAPTER.validate_json(path.read_bytes())[f"azure_ai/{catalog_name}"]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("catalog_name", CATALOG_NAMES)
|
||||
def test_azure_ai_catalog_name_routes_to_azure_ai(catalog_name: str) -> None:
|
||||
routed_model, provider, _, _ = get_llm_provider(model=f"azure_ai/{catalog_name}")
|
||||
assert (routed_model, provider) == (catalog_name, "azure_ai")
|
||||
|
||||
|
||||
@pytest.mark.usefixtures("local_model_cost_map")
|
||||
@pytest.mark.parametrize("spec", TOKEN_PRICED_MODELS, ids=lambda spec: spec.catalog_name)
|
||||
def test_azure_ai_catalog_name_is_priced_and_routed(spec: TokenPricedCatalogModel) -> None:
|
||||
routed_model, provider, _, _ = get_llm_provider(model=f"azure_ai/{spec.catalog_name}")
|
||||
assert (routed_model, provider) == (spec.catalog_name, "azure_ai")
|
||||
|
||||
info = get_model_info(model=routed_model, custom_llm_provider=provider)
|
||||
assert info["litellm_provider"] == "azure_ai"
|
||||
assert info["mode"] == spec.mode
|
||||
assert info["input_cost_per_token"] == spec.input_cost_per_token
|
||||
assert info["output_cost_per_token"] == spec.output_cost_per_token
|
||||
assert info["cache_read_input_token_cost"] == spec.cache_read_input_token_cost
|
||||
assert info["max_input_tokens"] == spec.max_input_tokens
|
||||
assert info["max_output_tokens"] == spec.max_output_tokens
|
||||
assert info["max_tokens"] == spec.max_output_tokens
|
||||
for flag in spec.supported_flags:
|
||||
assert info[flag] is True, flag
|
||||
def test_azure_ai_catalog_name_costs_a_million_tokens_at_list_price(spec: TokenPricedCatalogModel) -> None:
|
||||
prompt_cost, completion_cost_usd = cost_per_token(
|
||||
model=f"azure_ai/{spec.catalog_name}", prompt_tokens=A_MILLION, completion_tokens=A_MILLION
|
||||
)
|
||||
assert prompt_cost == pytest.approx(spec.dollars_per_million_input)
|
||||
assert completion_cost_usd == pytest.approx(spec.dollars_per_million_output)
|
||||
|
||||
|
||||
@pytest.mark.usefixtures("local_model_cost_map")
|
||||
@pytest.mark.parametrize(
|
||||
"spec",
|
||||
[spec for spec in TOKEN_PRICED_MODELS if spec.catalog_name != "model-router"],
|
||||
ids=lambda spec: spec.catalog_name,
|
||||
)
|
||||
def test_azure_ai_catalog_name_costs_a_million_tokens_at_list_price(spec: TokenPricedCatalogModel) -> None:
|
||||
prompt_cost, completion_cost = cost_per_token(
|
||||
model=f"azure_ai/{spec.catalog_name}", prompt_tokens=1_000_000, completion_tokens=1_000_000
|
||||
@pytest.mark.parametrize("spec", TOKEN_PRICED_MODELS, ids=lambda spec: spec.catalog_name)
|
||||
def test_azure_ai_catalog_name_prices_the_same_in_any_casing(spec: TokenPricedCatalogModel) -> None:
|
||||
lowercase_cost = cost_per_token(model=f"azure_ai/{spec.catalog_name}", prompt_tokens=A_MILLION, completion_tokens=0)
|
||||
upper_cost = cost_per_token(model=f"azure_ai/{spec.catalog_name.upper()}", prompt_tokens=A_MILLION, completion_tokens=0)
|
||||
assert upper_cost == lowercase_cost
|
||||
|
||||
|
||||
@pytest.mark.usefixtures("local_model_cost_map")
|
||||
@pytest.mark.parametrize("catalog_name", GROK_4_20_NAMES)
|
||||
def test_azure_ai_grok_4_20_bills_cached_prompt_tokens_at_the_input_price(catalog_name: str) -> None:
|
||||
uncached_prompt_cost, _ = cost_per_token(model=f"azure_ai/{catalog_name}", prompt_tokens=A_MILLION, completion_tokens=0)
|
||||
cached_prompt_cost, _ = cost_per_token(
|
||||
model=f"azure_ai/{catalog_name}",
|
||||
prompt_tokens=A_MILLION,
|
||||
completion_tokens=0,
|
||||
cache_read_input_tokens=A_MILLION,
|
||||
)
|
||||
assert prompt_cost == pytest.approx(spec.input_cost_per_token * 1_000_000)
|
||||
assert completion_cost == pytest.approx(spec.output_cost_per_token * 1_000_000)
|
||||
assert uncached_prompt_cost > 0
|
||||
assert cached_prompt_cost == pytest.approx(uncached_prompt_cost)
|
||||
|
||||
|
||||
@pytest.mark.usefixtures("local_model_cost_map")
|
||||
def test_azure_ai_whisper_catalog_name_is_priced_per_second() -> None:
|
||||
routed_model, provider, _, _ = get_llm_provider(model="azure_ai/whisper")
|
||||
assert (routed_model, provider) == ("whisper", "azure_ai")
|
||||
|
||||
info = get_model_info(model=routed_model, custom_llm_provider=provider)
|
||||
assert info["mode"] == "audio_transcription"
|
||||
assert info["input_cost_per_second"] == 0.0001
|
||||
assert info["output_cost_per_second"] == 0.0001
|
||||
transcription: Final = TranscriptionResponse(text="hello")
|
||||
transcription._hidden_params = { # pyright: ignore[reportPrivateUsage] # TranscriptionResponse exposes no public hidden-params setter
|
||||
"custom_llm_provider": "azure_ai",
|
||||
"model": "azure_ai/whisper",
|
||||
"audio_transcription_duration": 3600,
|
||||
}
|
||||
cost = completion_cost(
|
||||
completion_response=transcription,
|
||||
model="azure_ai/whisper",
|
||||
custom_llm_provider="azure_ai",
|
||||
call_type="atranscription",
|
||||
)
|
||||
assert cost == pytest.approx(0.36)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("catalog_name", CATALOG_NAMES)
|
||||
def test_azure_ai_catalog_entry_source_and_backup_match(catalog_name: str) -> None:
|
||||
main_entry = _cost_map_entry(REPO_ROOT / "model_prices_and_context_window.json", catalog_name)
|
||||
backup_entry = _cost_map_entry(REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json", catalog_name)
|
||||
main_entry = _cost_map_entry(MAIN_COST_MAP, catalog_name)
|
||||
backup_entry = _cost_map_entry(BACKUP_COST_MAP, catalog_name)
|
||||
|
||||
assert str(main_entry["source"]).startswith("https://azure.microsoft.com/en-us/pricing/details/")
|
||||
assert str(main_entry["source"]).startswith(AZURE_PRICING_PREFIX)
|
||||
assert backup_entry == main_entry
|
||||
|
||||
|
||||
@pytest.mark.parametrize("spec", TOKEN_PRICED_MODELS, ids=lambda spec: spec.catalog_name)
|
||||
def test_azure_ai_catalog_entry_carries_its_retirement_date(spec: TokenPricedCatalogModel) -> None:
|
||||
entry = _cost_map_entry(REPO_ROOT / "model_prices_and_context_window.json", spec.catalog_name)
|
||||
assert entry.get("deprecation_date") == spec.deprecation_date
|
||||
def test_azure_ai_model_router_spellings_share_one_entry() -> None:
|
||||
underscore_entry = _cost_map_entry(MAIN_COST_MAP, "model_router")
|
||||
hyphen_entry = _cost_map_entry(MAIN_COST_MAP, "model-router")
|
||||
|
||||
assert {k: v for k, v in underscore_entry.items() if k != "comment"} == {
|
||||
k: v for k, v in hyphen_entry.items() if k != "comment"
|
||||
}
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue