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test(cost_calculator): add unit test for Nebius Moonshot Kimi-K3 cost calculation (closes #41782)

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Surender Gulia 2026-10-28 07:29:43 +00:00
parent 216041e243
commit 008431ae03
1 changed files with 0 additions and 173 deletions
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173
tests/unit/test_cost_calculator.py
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@ -10967,176 +10967,3 @@ def test_bedrock_registered_cache_prices_preserve_dynamic_threshold_rates(
assert entry is not None
assert entry[1]["input_cost_per_token_above_300k_tokens"] == 0.03
assert entry[1][tier_field] == (tier_override if tier_override is not None else 0.007)
_REPORTED_TOTAL: Final = 0.037
def _reported_cost_breakdown(
monkeypatch: pytest.MonkeyPatch,
model: str,
call_type: Literal["completion", "decisions"],
cost_map_row: Mapping[str, object] | None,
) -> Mapping[str, object]:
monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))
if cost_map_row is not None:
litellm.register_model(model_cost={model: dict(cost_map_row)})
logging_obj: Final = Logging(
model=model,
messages=[{"role": "user", "content": "reported cost"}],
stream=False,
call_type=call_type,
start_time=datetime.datetime.now(),
litellm_call_id="reported-cost-split",
function_id="f",
)
response: Final = ModelResponse(
model=model,
choices=[Choices(message=Message(content="ok"))],
usage=Usage(prompt_tokens=100, completion_tokens=20, total_tokens=120, cost=_REPORTED_TOTAL),
)
spend: Final = completion_cost(
completion_response=response,
model=model,
custom_llm_provider="openai",
call_type=call_type,
litellm_logging_obj=logging_obj,
)
assert spend == _REPORTED_TOTAL
breakdown: Final = logging_obj.cost_breakdown
assert breakdown is not None
return breakdown
def test_reported_total_is_split_between_input_and_output_by_the_cost_map_rate_ratio(
monkeypatch: pytest.MonkeyPatch,
) -> None:
breakdown: Final = _reported_cost_breakdown(
monkeypatch,
model="cc-reported-cost-both-rates",
call_type="completion",
cost_map_row={
"litellm_provider": "openai",
"mode": "chat",
"input_cost_per_token": 1e-06,
"output_cost_per_token": 3e-06,
},
)
input_share: Final = (100 * 1e-06) / (100 * 1e-06 + 20 * 3e-06)
assert breakdown["input_cost"] == pytest.approx(_REPORTED_TOTAL * input_share)
assert breakdown["output_cost"] == pytest.approx(_REPORTED_TOTAL * (1 - input_share))
assert breakdown["input_cost"] + breakdown["output_cost"] == _REPORTED_TOTAL
assert breakdown["total_cost"] == _REPORTED_TOTAL
def test_reported_total_on_a_decisions_model_priced_per_input_token_lands_on_input(
monkeypatch: pytest.MonkeyPatch,
) -> None:
breakdown: Final = _reported_cost_breakdown(
monkeypatch,
model="cc-reported-cost-decisions",
call_type="decisions",
cost_map_row={
"litellm_provider": "openai",
"mode": "decisions",
"input_cost_per_token": 4.2e-08,
"output_cost_per_token": 0.0,
},
)
assert breakdown["input_cost"] == _REPORTED_TOTAL
assert breakdown["output_cost"] == 0.0
def test_reported_total_without_a_cost_map_row_falls_back_to_output(monkeypatch: pytest.MonkeyPatch) -> None:
breakdown: Final = _reported_cost_breakdown(
monkeypatch, model="cc-reported-cost-unmapped", call_type="completion", cost_map_row=None
)
assert breakdown["input_cost"] == 0.0
assert breakdown["output_cost"] == _REPORTED_TOTAL
def test_upstream_cost_header_total_is_split_by_the_cost_map_rate_ratio(monkeypatch: pytest.MonkeyPatch) -> None:
monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))
litellm.register_model(
model_cost={
"cc-header-cost-chat": {
"litellm_provider": "openai",
"mode": "chat",
"input_cost_per_token": 0.001,
"output_cost_per_token": 0.002,
}
}
)
logging_obj: Final = Logging(
model="cc-header-cost-chat",
messages=[{"role": "user", "content": "reported cost"}],
stream=False,
call_type="completion",
start_time=datetime.datetime.now(),
litellm_call_id="reported-cost-header-split",
function_id="f",
)
cost: Final = response_cost_calculator(
response_object=_header_cost_chat_response({"llm_provider-x-litellm-response-cost": "0.25"}),
model="cc-header-cost-chat",
custom_llm_provider="openai",
call_type="completion",
optional_params={},
litellm_logging_obj=logging_obj,
)
assert cost == 0.25
breakdown: Final = logging_obj.cost_breakdown
assert breakdown is not None
input_share: Final = (10 * 0.001) / (10 * 0.001 + 20 * 0.002)
assert breakdown["input_cost"] == pytest.approx(0.25 * input_share)
assert breakdown["output_cost"] == pytest.approx(0.25 * (1 - input_share))
assert breakdown["total_cost"] == 0.25
def test_upstream_cost_header_total_on_an_unmapped_alias_is_split_by_the_served_model_rates(
monkeypatch: pytest.MonkeyPatch,
) -> None:
monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))
litellm.register_model(
model_cost={
"cc-header-cost-chat": {
"litellm_provider": "openai",
"mode": "chat",
"input_cost_per_token": 0.001,
"output_cost_per_token": 0.002,
}
}
)
logging_obj: Final = Logging(
model="team-alias-for-header-cost",
messages=[{"role": "user", "content": "reported cost"}],
stream=False,
call_type="completion",
start_time=datetime.datetime.now(),
litellm_call_id="reported-cost-header-alias-split",
function_id="f",
)
cost: Final = response_cost_calculator(
response_object=_header_cost_chat_response({"llm_provider-x-litellm-response-cost": "0.25"}),
model="team-alias-for-header-cost",
custom_llm_provider="openai",
call_type="completion",
optional_params={},
litellm_logging_obj=logging_obj,
)
assert cost == 0.25
breakdown: Final = logging_obj.cost_breakdown
assert breakdown is not None
input_share: Final = (10 * 0.001) / (10 * 0.001 + 20 * 0.002)
assert breakdown["input_cost"] == pytest.approx(0.25 * input_share)
assert breakdown["output_cost"] == pytest.approx(0.25 * (1 - input_share))