test: retarget tests that pinned prices the mirrored cost map now carries

agentcore/search and vertex_ai/semantic-ranker-default-004 are priced in main's map, so these tests follow main: the search test asserts the mapped per-query price and the custom-pricing rerank test uses an unmapped deployment name.

ci_cd/generate_model_prices_schema.py

@@ -62,6 +62,17 @@ OFF_PEAK_WINDOW: Final[JsonSchema] = {
 }
 
 OBJECT_KEYS: dict[str, JsonSchema] = {
+    "search_free_requests": {
+        "type": "object",
+        "description": "Free Google Search grounding requests per period, shared across models in the same shared_group.",
+        "required": ["count", "period", "shared_group"],
+        "additionalProperties": False,
+        "properties": {
+            "count": {"type": "integer", "minimum": 0},
+            "period": {"type": "string", "enum": ["day", "month"]},
+            "shared_group": {"type": "string", "pattern": "\\S"},
+        },
+    },
     "off_peak_pricing": {
         "type": "object",
         "description": "Rates that replace the same-named base fields while the request falls inside the stated UTC windows.",
@@ -111,8 +122,9 @@ OBJECT_KEYS: dict[str, JsonSchema] = {
         "properties": {
             "model": NON_BLANK_STRING,
             "region": NON_BLANK_STRING,
+            "litellm_provider": {"type": "string", "enum": ["bedrock", "bedrock_mantle"]},
         },
-        "description": "Model that counts tokens for this one, and the AWS region its count API runs in.",
+        "description": "Model that counts tokens for this one, its counting provider, and the AWS region its count API runs in.",
     },
 }
 

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

@@ -636,7 +636,11 @@ 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)
 
 
 def _transform_text(text: str) -> SearchResponse:

tests/unit/test_cost_calculator.py

@@ -1027,7 +1027,7 @@ def test_per_query_priced_rerank_deployment_completion_cost_is_nonzero():
             {
                 "model_name": "semantic-ranker-default-004",
                 "litellm_params": {
-                    "model": "vertex_ai/semantic-ranker-default-004",
+                    "model": "vertex_ai/semantic-ranker-custom-test",
                     "vertex_project": "test-project",
                     "vertex_location": "us-east5",
                 },
@@ -1036,7 +1036,7 @@ def test_per_query_priced_rerank_deployment_completion_cost_is_nonzero():
         ]
     )
     router_model_id: Final = router.model_list[0]["model_info"]["id"]
-    assert litellm.model_cost["vertex_ai/semantic-ranker-default-004"].get("input_cost_per_query") is None
+    assert litellm.model_cost.get("vertex_ai/semantic-ranker-custom-test", {}).get("input_cost_per_query") is None
 
     response: Final = RerankResponse(
         id="vertex_ai_rerank_test",
@@ -1046,7 +1046,7 @@ def test_per_query_priced_rerank_deployment_completion_cost_is_nonzero():
 
     cost: Final = completion_cost(
         completion_response=response,
-        model="vertex_ai/semantic-ranker-default-004",
+        model="vertex_ai/semantic-ranker-custom-test",
         custom_llm_provider="vertex_ai",
         call_type="arerank",
         custom_pricing=True,

tests/unit/test_model_prices_schema.py

@@ -559,7 +559,17 @@ def test_deepseek_rows_bill_half_rate_outside_weekday_peak_hours(path: Path):
     assert all(name in rows for name in DEEPSEEK_PRICED_ROWS)
 
 
-PROVIDER_LABELS_WITHOUT_A_MODEL_SET: Final = frozenset({"sagemaker", "bedrock_converse"})
+PROVIDER_LABELS_WITHOUT_A_MODEL_SET: Final = frozenset(
+    {
+        "sagemaker",
+        "bedrock_converse",
+        "coralbricks",
+        "inferenceapis",
+        "microsoft_365_copilot",
+        "openinfer",
+        "scaledown",
+    }
+)
 MODES_SERVED_OUTSIDE_THE_LLM_PROVIDER_REGISTRY: Final = frozenset({"search", "decisions", "evaluation"})
 VERTEX_FAMILIES_A_VERTEX_WILDCARD_GRANT_DOES_NOT_LIST: Final = frozenset(
     {

tests/unit/test_utils.py

@@ -833,6 +833,18 @@ def test_aaamodel_prices_and_context_window_json_is_valid():
             "type": "object",
             "properties": {
                 "supports_computer_use": {"type": "boolean"},
+                "cache_creation_input_token_cost_above_272k_tokens_decisions": {"type": "number"},
+                "cache_creation_input_token_cost_decisions": {"type": "number"},
+                "cache_read_input_token_cost_above_272k_tokens_decisions": {"type": "number"},
+                "cache_read_input_token_cost_decisions": {"type": "number"},
+                "explicit_cache_read_input_token_cost": {"type": "number"},
+                "input_cost_per_text_message": {"type": "number"},
+                "input_cost_per_token_above_272k_tokens_decisions": {"type": "number"},
+                "input_cost_per_token_decisions": {"type": "number"},
+                "output_cost_per_token_above_272k_tokens_decisions": {"type": "number"},
+                "output_cost_per_token_decisions": {"type": "number"},
+                "supports_bedrock_converse_tool_result_images": {"type": "boolean"},
+                "supports_bedrock_native_structured_output": {"type": "boolean"},
                 "cache_creation_input_audio_token_cost": {"type": "number"},
                 "cache_creation_input_token_cost": {"type": "number"},
                 "cache_creation_input_token_cost_above_1hr": {"type": "number"},
@@ -959,6 +971,7 @@ def test_aaamodel_prices_and_context_window_json_is_valid():
                         "image_edit",
                         "embedding",
                         "evaluation",
+                        "extraction",
                         "guardrail",
                         "image_generation",
                         "video_generation",
@@ -1132,6 +1145,16 @@ def test_aaamodel_prices_and_context_window_json_is_valid():
                     "type": "object",
                     "additionalProperties": {"type": "number"},
                 },
+                "search_free_requests": {
+                    "type": "object",
+                    "properties": {
+                        "count": {"type": "integer", "minimum": 1},
+                        "period": {"type": "string", "enum": ["day", "month"]},
+                        "shared_group": {"type": "string"},
+                    },
+                    "required": ["count", "period", "shared_group"],
+                    "additionalProperties": False,
+                },
                 "search_context_cost_per_query": {
                     "type": "object",
                     "properties": {
@@ -8187,12 +8210,17 @@ def test_models_by_provider():
             continue
         elif k == "sample_spec":
             continue
-        elif (
-            v["litellm_provider"] == "sagemaker"
-            or v["litellm_provider"] == "bedrock_converse"
+        elif v["litellm_provider"] in (
+            "sagemaker",
+            "bedrock_converse",
+            "coralbricks",
+            "inferenceapis",
+            "microsoft_365_copilot",
+            "openinfer",
+            "scaledown",
         ):
             continue
-        elif v.get("mode") in ("search", "decisions", "evaluation"):
+        elif v.get("mode") in ("search", "decisions", "evaluation", "extraction"):
             continue
         else:
             providers.add(v["litellm_provider"])