test: retarget reasoning and search assertions for mirrored catalog

tests/test_litellm/llms/bedrock/search/test_agentcore_search_transformation.py

@@ -634,4 +634,8 @@ class TestAgentCoreSearchEdgeCases:
         from litellm.search.cost_calculator import search_provider_cost_per_query
 
         monkeypatch.setattr(litellm, "model_cost", GetModelCostMap.load_local_model_cost_map())
-        assert search_provider_cost_per_query(model="agentcore/search", custom_llm_provider="agentcore") == (0.0, 0.0)
+        price_per_query = litellm.model_cost["agentcore/search"]["input_cost_per_query"]
+        assert price_per_query > 0
+        assert search_provider_cost_per_query(
+            model="agentcore/search", custom_llm_provider="agentcore", number_of_queries=3
+        ) == (3 * price_per_query, 0.0)

tests/test_litellm/llms/openrouter/chat/test_openrouter_chat_transformation.py

@@ -1,5 +1,8 @@
 
 import httpx
+from typing import Final
+
+import litellm
 import pytest
 
 
@@ -523,14 +526,14 @@ def test_openrouter_cost_tracking_streaming():
     assert result2.usage.cost == 0.0001
 
 
-def test_openrouter_reasoning_models_allow_reasoning_effort_param():
+def test_openrouter_reasoning_models_allow_reasoning_effort_param(reasoning_model: str):
     """
     OpenRouter reasoning-capable models should accept the reasoning_effort param.
     """
     config = OpenrouterConfig()
 
     supported_params = config.get_supported_openai_params(
-        model="openrouter/deepseek/deepseek-v3.2"
+        model=reasoning_model
     )
 
     assert "reasoning_effort" in supported_params
@@ -550,7 +553,7 @@ def test_openrouter_non_reasoning_models_do_not_add_reasoning_effort():
     assert "reasoning_effort" not in supported_params
 
 
-def test_openrouter_reasoning_effort_max_maps_to_xhigh():
+def test_openrouter_reasoning_effort_max_maps_to_xhigh(reasoning_model: str):
     """
     OpenRouter expects 'xhigh' instead of 'max' for reasoning_effort.
     """
@@ -559,14 +562,14 @@ def test_openrouter_reasoning_effort_max_maps_to_xhigh():
     result = config.map_openai_params(
         non_default_params={"reasoning_effort": "max"},
         optional_params={},
-        model="openrouter/deepseek/deepseek-r1",
+        model=reasoning_model,
         drop_params=False,
     )
 
     assert result["reasoning_effort"] == "xhigh"
 
 
-def test_openrouter_reasoning_effort_max_does_not_mutate_caller_dict():
+def test_openrouter_reasoning_effort_max_does_not_mutate_caller_dict(reasoning_model: str):
     """
     map_openai_params must not mutate the caller-supplied non_default_params dict.
     """
@@ -576,14 +579,14 @@ def test_openrouter_reasoning_effort_max_does_not_mutate_caller_dict():
     config.map_openai_params(
         non_default_params=original_params,
         optional_params={},
-        model="openrouter/deepseek/deepseek-r1",
+        model=reasoning_model,
         drop_params=False,
     )
 
     assert original_params["reasoning_effort"] == "max"
 
 
-def test_openrouter_reasoning_effort_xhigh_passes_through():
+def test_openrouter_reasoning_effort_xhigh_passes_through(reasoning_model: str):
     """
     reasoning_effort='xhigh' should be forwarded unchanged.
     """
@@ -592,14 +595,14 @@ def test_openrouter_reasoning_effort_xhigh_passes_through():
     result = config.map_openai_params(
         non_default_params={"reasoning_effort": "xhigh"},
         optional_params={},
-        model="openrouter/deepseek/deepseek-r1",
+        model=reasoning_model,
         drop_params=False,
     )
 
     assert result["reasoning_effort"] == "xhigh"
 
 
-def test_openrouter_reasoning_effort_high_passes_through():
+def test_openrouter_reasoning_effort_high_passes_through(reasoning_model: str):
     """
     Non-max reasoning_effort values should be forwarded unchanged.
     """
@@ -608,8 +611,15 @@ def test_openrouter_reasoning_effort_high_passes_through():
     result = config.map_openai_params(
         non_default_params={"reasoning_effort": "high"},
         optional_params={},
-        model="openrouter/deepseek/deepseek-r1",
+        model=reasoning_model,
         drop_params=False,
     )
 
     assert result["reasoning_effort"] == "high"
+
+
+@pytest.fixture
+def reasoning_model(monkeypatch: pytest.MonkeyPatch) -> str:
+    model: Final = "openrouter/test-reasoning-model"
+    monkeypatch.setitem(litellm.model_cost, model, {"litellm_provider": "openrouter", "mode": "chat", "supports_reasoning": True})
+    return model

tests/test_litellm/llms/test_cache_control_and_reasoning.py

@@ -8,6 +8,9 @@ This test file verifies the fixes for Issue #19923:
 """
 
 
+from typing import Final
+
+import litellm
 import pytest
 
 
@@ -256,20 +259,14 @@ def test_openrouter_glm_transform_moves_cache_control_to_content():
     assert user_message["content"][0]["cache_control"] == {"type": "ephemeral"}
 
 
-def test_openrouter_supports_thinking_param_for_reasoning_models():
-    """OpenRouter should support thinking parameter for reasoning-capable models."""
-    config = OpenrouterConfig()
+def test_openrouter_supports_thinking_param_for_reasoning_models(reasoning_model: str):
+    supported_params = OpenrouterConfig().get_supported_openai_params(model=reasoning_model)
+    assert "thinking" in supported_params
+    assert "reasoning_effort" in supported_params
 
-    # Test MiniMax (supports reasoning)
-    supported_params_minimax = config.get_supported_openai_params(
-        model="openrouter/minimax/minimax-m2"
-    )
-    assert "thinking" in supported_params_minimax
-    assert "reasoning_effort" in supported_params_minimax
 
-    # Test GLM (supports reasoning)
-    supported_params_glm = config.get_supported_openai_params(
-        model="openrouter/z-ai/glm-4.6"
-    )
-    assert "thinking" in supported_params_glm
-    assert "reasoning_effort" in supported_params_glm
+@pytest.fixture
+def reasoning_model(monkeypatch: pytest.MonkeyPatch) -> str:
+    model: Final = "openrouter/test-reasoning-model"
+    monkeypatch.setitem(litellm.model_cost, model, {"litellm_provider": "openrouter", "mode": "chat", "supports_reasoning": True})
+    return model