litellm/tests/test_litellm/router_strategy/test_savings_baseline.py
tin-berri 1e4d358f3f
fix(router): drop a tier param the routed target cannot take (#38622)
* fix(router): drop a tier param the routed target cannot take

A complexity tier's litellm_params are an operator override applied to every request that tier
routes, and they were written into the request kwargs unconditionally. When the tier set a param
the target does not declare, get_optional_params raised UnsupportedParamsError before the request
left the proxy, so the whole tier answered 400. The bundled Lite preset sets reasoning_effort on
its complex tier, and four of the thirteen kimi-k3 map entries reject that param, so a router
built from a first-party template failed on every complex prompt

Filter the tier params at both store sites against what the group's deployments declare. The
candidate set is asked of the module that raises rather than derived from a second list, so
credentials, endpoint and transport controls are never at risk: base_url, timeout,
default_headers, organization and deployment_id are not chat completion params and never reach
that comparison. A param survives if any deployment could take it, since routing has not picked
one yet, and it survives an unresolvable provider or an empty group, since a best-effort filter
must not narrow what the request already did

The skip list _check_valid_arg applies before rejecting a param now has one owner both it and the
router read, so the two cannot drift

* fix(router): honor allowed_openai_params when gating tier params

* test(router): cover _declared_param_allowlist malformed declarations

* fix(router): never ask an authenticating provider whether it takes a tier param

Resolving github_copilot or chatgpt runs their OAuth device flow, so the
capability question _deployment_accepts_param asks would freeze the event
loop for minutes inside async_get_available_deployment. Promote
register_model's local skip set to
constants.PROVIDERS_THAT_AUTHENTICATE_ON_PROVIDER_INFO and fail open on
those providers before any lookup

* fix(utils): adopt a declared authenticating prefix instead of resolving it

The tier-param guard alone was not enough: the savings baseline and the
model-info funnels also resolve deployments during routing, and each
resolution of github_copilot or chatgpt runs their OAuth device flow.
declared_authenticating_provider gives every metadata funnel
(get_supported_openai_params, _get_potential_model_names,
_supports_factory, canonical_model) the resolver's answer by string, so
the whole routing path answers without authenticating. A through-test
drives async_get_available_deployment with a copilot deployment and
records that no copilot resolution happens
2026-08-28 14:17:49 -07:00

211 lines
8.9 KiB
Python

import pytest
from litellm.router import Router
from litellm.router_strategy.savings_baseline import (
Baseline,
canonical_model,
_models_in,
_most_expensive,
resolve_baseline,
)
@pytest.fixture
def parent() -> Router:
return Router(
model_list=[
{"model_name": "cheap", "litellm_params": {"model": "anthropic/claude-haiku-4-5"}},
{"model_name": "top", "litellm_params": {"model": "anthropic/claude-opus-5"}},
{"model_name": "pool", "litellm_params": {"model": "anthropic/claude-haiku-4-5"}},
{"model_name": "pool", "litellm_params": {"model": "anthropic/claude-opus-5"}},
]
)
class TestCanonicalModel:
def test_qualifies_a_bare_name_with_the_provider_that_owns_it(self):
assert canonical_model("claude-opus-5") == "anthropic/claude-opus-5"
def test_keeps_an_already_qualified_name_qualified(self):
assert canonical_model("anthropic/claude-opus-5") == "anthropic/claude-opus-5"
def test_honours_a_separately_declared_provider(self):
assert canonical_model("claude-opus-5", "openai") == "openai/claude-opus-5"
def test_returns_none_for_a_name_no_provider_claims(self):
assert canonical_model("") is None
@pytest.mark.parametrize(
"model, provider, expected",
[
("github_copilot/gpt-4o", None, "github_copilot/gpt-4o"),
("chatgpt/gpt-5", None, "chatgpt/gpt-5"),
("gpt-4o", "github_copilot", "github_copilot/gpt-4o"),
],
)
def test_never_resolves_a_provider_whose_lookup_authenticates(self, model, provider, expected, monkeypatch):
"""Resolving github_copilot or chatgpt runs their OAuth device flow, so the baseline must
qualify these by string alone. A raising sentinel cannot prove the lookup was skipped,
because canonical_model swallows resolver errors into None."""
import litellm
lookups: list = []
def _record(*args, **kwargs):
lookups.append((args, kwargs))
raise RuntimeError("provider resolution must not run for an authenticating provider")
monkeypatch.setattr(litellm, "get_llm_provider", _record)
assert canonical_model(model, provider) == expected
assert lookups == []
class TestModelsForGroup:
def test_resolves_a_group_to_the_models_its_deployments_call(self, parent):
assert [c.model for c in _models_in(parent, "cheap")] == ["anthropic/claude-haiku-4-5"]
def test_returns_every_deployment_in_a_pooled_group(self, parent):
assert sorted(c.model for c in _models_in(parent, "pool")) == [
"anthropic/claude-haiku-4-5",
"anthropic/claude-opus-5",
]
def test_treats_an_unknown_group_as_a_model_name(self, parent):
"""A tier can point straight at a provider model rather than a configured group."""
assert [c.model for c in _models_in(parent, "claude-opus-5")] == ["anthropic/claude-opus-5"]
class TestMostExpensive:
"""Ranking runs through the router, because what a deployment costs is the
router's answer to give: it merges configured prices over the built-in map."""
def test_picks_by_output_rate(self, parent):
picked = _most_expensive(parent, [Baseline("anthropic/claude-haiku-4-5"), Baseline("anthropic/claude-opus-5")])
assert picked.model == "anthropic/claude-opus-5"
def test_ignores_models_with_no_per_token_price(self, parent):
"""A free model as baseline would report the whole real spend as a loss."""
picked = _most_expensive(
parent, [Baseline("not-a-real-model-anywhere"), Baseline("anthropic/claude-haiku-4-5")]
)
assert picked.model == "anthropic/claude-haiku-4-5"
def test_returns_none_when_nothing_can_be_priced(self, parent):
assert _most_expensive(parent, [Baseline("not-a-real-model-anywhere")]) is None
def test_returns_none_for_an_empty_candidate_set(self, parent):
assert _most_expensive(parent, []) is None
class TestResolveBaseline:
def test_derives_the_priciest_candidate(self, parent):
assert resolve_baseline(parent, ["cheap", "top"]).model == "anthropic/claude-opus-5"
def test_never_raises_so_a_metric_cannot_fail_a_live_request(self):
"""Read on the routing path while decorating a request that is about to be
served; a dashboard counterfactual must not be able to take routing down."""
class Exploding:
@property
def model_name_to_deployment_indices(self):
raise RuntimeError("router is mid-reload")
assert resolve_baseline(Exploding(), ["anything"]) is None
def test_an_empty_candidate_set_zeroes_the_driver_rather_than_inventing_one(self, parent):
assert resolve_baseline(parent, []) is None
class TestDeploymentsPricedByBaseModel:
"""`litellm_params.model` is not always a model.
On Azure it is the deployment name, which is absent from the cost map, so pricing it
directly drops the candidate. If that candidate was the priciest, the baseline quietly
becomes the second priciest and every saving is understated; if the whole pool is
Azure, nothing prices and the driver reports zero with nothing at default log level
saying why. `model_info.base_model` is what names the real model, which is the chain
router.py already resolves pricing through.
"""
@staticmethod
def _router(*deployments: dict) -> Router:
return Router(model_list=list(deployments))
def test_model_info_base_model_is_preferred_over_the_deployment_name(self):
router = self._router(
{
"model_name": "big",
"litellm_params": {"model": "azure/my-gpt5-deployment"},
"model_info": {"base_model": "azure/gpt-4.1"},
},
)
assert [c.model for c in _models_in(router, "big")] == ["azure/gpt-4.1"]
def test_litellm_params_base_model_is_the_other_accepted_spelling(self):
router = self._router(
{
"model_name": "big",
"litellm_params": {"model": "azure/my-gpt5-deployment", "base_model": "azure/gpt-4.1"},
},
)
assert [c.model for c in _models_in(router, "big")] == ["azure/gpt-4.1"]
def test_a_deployment_without_a_base_model_still_prices_by_its_model(self):
router = self._router({"model_name": "big", "litellm_params": {"model": "anthropic/claude-opus-5"}})
assert [c.model for c in _models_in(router, "big")] == ["anthropic/claude-opus-5"]
def test_an_azure_deployment_can_win_the_priciest_candidate(self):
"""Without the base_model hop the Azure candidate never prices, so the cheaper
model wins by default and the reported saving shrinks."""
router = self._router(
{"model_name": "cheap", "litellm_params": {"model": "anthropic/claude-haiku-4-5"}},
{
"model_name": "big",
"litellm_params": {"model": "azure/my-gpt5-deployment"},
"model_info": {"base_model": "azure/gpt-4.1"},
},
)
assert resolve_baseline(router, ["cheap", "big"]).model == "azure/gpt-4.1"
def test_an_all_azure_pool_still_has_a_baseline(self):
"""Otherwise nothing prices, the driver is disabled and the card reads $0.00."""
router = self._router(
{
"model_name": "big",
"litellm_params": {"model": "azure/my-gpt5-deployment"},
"model_info": {"base_model": "azure/gpt-4.1"},
},
)
assert resolve_baseline(router, ["big"]).model == "azure/gpt-4.1"
class TestDeploymentPricingOverrides:
"""A deployment may not be charged the public rate for the model it names."""
def test_a_configured_price_decides_the_baseline_not_the_public_rate(self):
"""A deployment configured far above its public rate is what the traffic would
really have cost. Ranking on the public rate picks the wrong counterfactual and
then prices it at a rate nobody pays."""
router = Router(
model_list=[
{"model_name": "cheap", "litellm_params": {"model": "anthropic/claude-haiku-4-5"}},
{"model_name": "top", "litellm_params": {"model": "anthropic/claude-opus-5"}},
]
)
assert resolve_baseline(router, ["cheap", "top"]).model == "anthropic/claude-opus-5"
overridden = Router(
model_list=[
{
"model_name": "cheap",
"litellm_params": {
"model": "anthropic/claude-haiku-4-5",
"input_cost_per_token": 0.001,
"output_cost_per_token": 0.002,
},
},
{"model_name": "top", "litellm_params": {"model": "anthropic/claude-opus-5"}},
]
)
assert resolve_baseline(overridden, ["cheap", "top"]).model == "anthropic/claude-haiku-4-5"