diff --git a/helm/litellm-helm/Chart.yaml b/helm/litellm-helm/Chart.yaml
index 3959d85edf3..a3cb388ffc6 100644
--- a/helm/litellm-helm/Chart.yaml
+++ b/helm/litellm-helm/Chart.yaml
@@ -18,7 +18,7 @@ type: application
 # This is the chart version. This version number should be incremented each time you make changes
 # to the chart and its templates, including the app version.
 # Versions are expected to follow Semantic Versioning (https://semver.org/)
-version: 1.1.2
+version: 1.1.3
 
 # This is the version number of the application being deployed. This version number should be
 # incremented each time you make changes to the application. Versions are not expected to
diff --git a/helm/litellm-helm/tests/hpa_tests.yaml b/helm/litellm-helm/tests/hpa_tests.yaml
index ec18c3591d3..cd062dd5971 100644
--- a/helm/litellm-helm/tests/hpa_tests.yaml
+++ b/helm/litellm-helm/tests/hpa_tests.yaml
@@ -1,4 +1,4 @@
-suite: "hpa with behavior"
+suite: "hpa"
 templates:
   - hpa.yaml
 tests:
@@ -23,14 +23,44 @@ tests:
       - equal: { path: spec.behavior.scaleUp.stabilizationWindowSeconds, value: 60 }
       - equal: { path: spec.behavior.scaleDown.stabilizationWindowSeconds, value: 90 }
 
----
-suite: "hpa without behavior"
-templates:
-  - hpa.yaml
-tests:
   - it: "does not render behavior when not set"
     set:
       autoscaling.enabled: true
     asserts:
       - isKind: { of: HorizontalPodAutoscaler }
       - isNull: { path: spec.behavior }
+
+  - it: "scales on cpu at the documented 60 percent by default"
+    set:
+      autoscaling.enabled: true
+    asserts:
+      - isKind: { of: HorizontalPodAutoscaler }
+      - equal: { path: "spec.metrics[0].resource.name", value: cpu }
+      - equal: { path: "spec.metrics[0].resource.target.type", value: Utilization }
+      - equal: { path: "spec.metrics[0].resource.target.averageUtilization", value: 60 }
+
+  - it: "does not scale on memory by default"
+    set:
+      autoscaling.enabled: true
+    asserts:
+      - lengthEqual: { path: spec.metrics, count: 1 }
+
+  - it: "honours an explicit cpu target override"
+    set:
+      autoscaling.enabled: true
+      autoscaling.targetCPUUtilizationPercentage: 75
+    asserts:
+      - equal: { path: "spec.metrics[0].resource.target.averageUtilization", value: 75 }
+
+  - it: "renders a memory metric only when a memory target is set"
+    set:
+      autoscaling.enabled: true
+      autoscaling.targetMemoryUtilizationPercentage: 80
+    asserts:
+      - lengthEqual: { path: spec.metrics, count: 2 }
+      - equal: { path: "spec.metrics[1].resource.name", value: memory }
+      - equal: { path: "spec.metrics[1].resource.target.averageUtilization", value: 80 }
+
+  - it: "renders no hpa when autoscaling is disabled"
+    asserts:
+      - hasDocuments: { count: 0 }
diff --git a/helm/litellm-helm/values.yaml b/helm/litellm-helm/values.yaml
index f8df98de102..637be2322e3 100644
--- a/helm/litellm-helm/values.yaml
+++ b/helm/litellm-helm/values.yaml
@@ -200,7 +200,16 @@ autoscaling:
   enabled: false
   minReplicas: 1
   maxReplicas: 100
-  targetCPUUtilizationPercentage: 80
+  # 60 is the documented recommendation. See "Recommended Machine Specifications"
+  # in https://docs.litellm.ai/docs/proxy/prod. A new replica clears the startupProbe
+  # above only after up to failureThreshold x periodSeconds = 300 seconds, so a target
+  # high enough to trip near saturation adds capacity minutes after it was needed.
+  targetCPUUtilizationPercentage: 60
+  # Deliberately left unset rather than given a value. The prisma query engine's
+  # resident memory is a high-water mark that ratchets to the pod's worst-ever write
+  # and is never returned, so a memory target reads the largest write a pod ever did
+  # rather than what it is doing now, and replicas ratchet up without scaling back in.
+  # Memory is a floor to provision under 'resources', not a signal to scale on.
   # targetMemoryUtilizationPercentage: 80
   # behavior: {}
 
diff --git a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py
index 3978a01a5db..0e01577b20e 100644
--- a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py
+++ b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py
@@ -36,6 +36,8 @@ from litellm.types.utils import (
 from litellm.utils import print_verbose, token_counter
 
 if TYPE_CHECKING:
+    from openai.types.completion_usage import CompletionUsage
+
     from litellm.litellm_core_utils.litellm_logging import Logging
     from litellm.types.litellm_core_utils.streaming_chunk_builder_utils import (
         UsagePerChunk,
@@ -794,7 +796,7 @@ class ChunkProcessor:
 
     @staticmethod
     def _extract_usage_chunk(chunk: "_UsageBearingChunk | ModelResponse | ModelResponseStream") -> Usage | None:
-        usage_chunk: Usage | None = None
+        usage_chunk: Usage | CompletionUsage | None = None
         if hasattr(chunk, "usage") and chunk.usage is not None:
             usage_chunk = chunk.usage
         elif "usage" in chunk:
@@ -806,7 +808,9 @@ class ChunkProcessor:
 
         if isinstance(usage_chunk, dict):
             return Usage(**usage_chunk)
-        return usage_chunk
+        if usage_chunk is None or isinstance(usage_chunk, Usage):
+            return usage_chunk
+        return Usage(**usage_chunk.model_dump())
 
     def _calculate_usage_per_chunk(
         self,
diff --git a/litellm/llms/anthropic/common_utils.py b/litellm/llms/anthropic/common_utils.py
index c60ebd844ba..d23690976ad 100644
--- a/litellm/llms/anthropic/common_utils.py
+++ b/litellm/llms/anthropic/common_utils.py
@@ -1378,31 +1378,38 @@ def process_anthropic_headers(headers: httpx.Headers | dict) -> dict:
     return additional_headers
 
 
-def _anthropic_model_entry(model: ModelInfoResponse, created_at: str) -> Mapping[str, object]:
+def _anthropic_model_entry(
+    model: ModelInfoResponse, created_at: str, display_names: Mapping[str, str]
+) -> Mapping[str, object]:
     return {  # mutable-ok: JSON response body, serialized by the route and never mutated
         "type": "model",
         "id": model["id"],
-        "display_name": model["id"],
+        "display_name": display_names.get(model["id"], model["id"]),
         "created_at": created_at,
         "max_input_tokens": model.get("max_input_tokens"),
         "max_tokens": model.get("max_output_tokens"),
     }
 
 
-def create_anthropic_model_list_response(models: Sequence[ModelInfoResponse]) -> Mapping[str, object]:
+def create_anthropic_model_list_response(
+    models: Sequence[ModelInfoResponse],
+    display_names: Mapping[str, str] = MappingProxyType({}),
+) -> Mapping[str, object]:
     """Build the Anthropic-native /v1/models envelope.
 
     Clients that send an anthropic-version header parse the Anthropic Models API
     shape (type/display_name/created_at plus has_more/first_id/last_id) and filter
     the list themselves, so every model is returned here. The token limits carry
     over from the OpenAI-shaped listing, named as the Messages API names them, and
-    are always present because the vendor shape declares them nullable, not optional
+    are always present because the vendor shape declares them nullable, not optional.
+    display_names maps a listed model id to a configured human-readable name; ids
+    without an entry fall back to the id itself, matching the vendor behavior
     """
     created_at: Final = (
         datetime.fromtimestamp(DEFAULT_MODEL_CREATED_AT_TIME, tz=timezone.utc).isoformat().replace("+00:00", "Z")
     )
     data: Final = [  # mutable-ok: JSON response body, serialized by the route and never mutated
-        _anthropic_model_entry(model, created_at) for model in models
+        _anthropic_model_entry(model, created_at, display_names) for model in models
     ]
     return {  # mutable-ok: JSON response body, serialized by the route and never mutated
         "data": data,
diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
index d8b1e7ba17c..69fe5678de9 100644
--- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
+++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
@@ -949,7 +949,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
         # For Gemini 3+ models, use thinkingLevel instead of thinkingBudget
         if model and VertexGeminiConfig._is_gemini_3_or_newer(model):
             if thinking_enabled:
-                if thinking_budget is None or thinking_budget == 0:
+                if thinking_budget == 0:
                     params["includeThoughts"] = False
                 else:
                     params["includeThoughts"] = True
diff --git a/litellm/llms/vertex_ai/ocr/deepseek_transformation.py b/litellm/llms/vertex_ai/ocr/deepseek_transformation.py
index 2603552152d..b57a87c3325 100644
--- a/litellm/llms/vertex_ai/ocr/deepseek_transformation.py
+++ b/litellm/llms/vertex_ai/ocr/deepseek_transformation.py
@@ -177,8 +177,9 @@ class VertexAIDeepSeekOCRConfig(BaseOCRConfig):
             content_item = {"type": "image_url", "image_url": document_url}
 
         # Build DeepSeek OCR request
+        provider_model: Final = model if model.startswith("deepseek-ai/") else f"deepseek-ai/{model}"
         data: Final = {
-            "model": "deepseek-ai/" + model,
+            "model": provider_model,
             "messages": [{"role": "user", "content": [content_item]}],
         }
 
diff --git a/litellm/main.py b/litellm/main.py
index c4c5bbefc4f..01c106adc7c 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -8637,6 +8637,16 @@ def _set_stream_builder_response_cost(response: ModelResponse, logging_obj: Opti
     hidden_params["response_cost"] = response_cost
 
 
+def _stamp_streaming_usage_cost(usage: Usage, response: ModelResponse, logging_obj: Optional["Logging"]) -> None:
+    if logging_obj is None:
+        return
+    if isinstance(getattr(usage, "cost", None), (int, float)):
+        return
+    computed_cost: Final = logging_obj._response_cost_calculator(result=response)
+    if isinstance(computed_cost, (int, float)) and computed_cost > 0:
+        setattr(usage, "cost", computed_cost)
+
+
 def stream_chunk_builder(
     chunks: list,
     messages: list | None = None,
@@ -8731,12 +8741,7 @@ def stream_chunk_builder(
                     )
                     break
 
-            if litellm.include_cost_in_streaming_usage and logging_obj is not None:
-                setattr(
-                    usage,
-                    "cost",
-                    logging_obj._response_cost_calculator(result=response),
-                )
+            _stamp_streaming_usage_cost(usage, response, logging_obj)
             _set_stream_builder_response_cost(response, logging_obj)
 
             processor.apply_provider_assembled_streaming_metadata(response, chunks, logging_obj)
@@ -8915,10 +8920,7 @@ def stream_chunk_builder(
                 )
                 break
 
-        # Add cost to usage object if include_cost_in_streaming_usage is True
-        if litellm.include_cost_in_streaming_usage and logging_obj is not None:
-            setattr(usage, "cost", logging_obj._response_cost_calculator(result=response))
-
+        _stamp_streaming_usage_cost(usage, response, logging_obj)
         _set_stream_builder_response_cost(response, logging_obj)
 
         processor.apply_provider_assembled_streaming_metadata(response, chunks, logging_obj)
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index a3cfb300ea6..10e8b56d925 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -9643,7 +9643,8 @@
         "supported_endpoints": [
             "/v1/images/generations",
             "/v1/images/edits"
-        ]
+        ],
+        "deprecation_date": "2026-10-01"
     },
     "azure_ai/MAI-Image-2.5-Flash": {
         "input_cost_per_image_token": 1.75e-06,
@@ -9656,7 +9657,8 @@
         "supported_endpoints": [
             "/v1/images/generations",
             "/v1/images/edits"
-        ]
+        ],
+        "deprecation_date": "2026-10-01"
     },
     "azure_ai/MAI-Image-2e": {
         "deprecation_date": "2026-08-15",
@@ -10155,7 +10157,9 @@
         "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/",
         "supports_function_calling": true,
         "supports_reasoning": true,
-        "supports_tool_choice": true
+        "supports_tool_choice": true,
+        "cache_read_input_token_cost": 1.45e-07,
+        "supports_prompt_caching": true
     },
     "azure_ai/deepseek-v4-flash": {
         "deprecation_date": "2028-02-20",
@@ -10169,18 +10173,20 @@
         "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/",
         "supports_function_calling": true,
         "supports_reasoning": true,
-        "supports_tool_choice": true
+        "supports_tool_choice": true,
+        "cache_read_input_token_cost": 2.8e-08,
+        "supports_prompt_caching": true
     },
     "azure_ai/deepseek-v4-flash-0731": {
-        "cache_read_input_token_cost": 2.8e-08,
+        "cache_read_input_token_cost": 1.4e-08,
         "deprecation_date": "2026-12-03",
-        "input_cost_per_token": 1.9e-07,
+        "input_cost_per_token": 4.4e-07,
         "litellm_provider": "azure_ai",
         "max_input_tokens": 1000000,
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
-        "output_cost_per_token": 5.1e-07,
+        "output_cost_per_token": 1.32e-06,
         "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/",
         "supports_function_calling": true,
         "supports_prompt_caching": true,
@@ -10400,11 +10406,13 @@
         "max_tokens": 262144,
         "mode": "chat",
         "output_cost_per_token": 3e-06,
-        "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/kimi-k2-5-now-in-microsoft-foundry/4492321",
+        "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/kimi/",
         "supports_function_calling": true,
         "supports_tool_choice": true,
         "supports_video_input": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 1e-07,
+        "supports_prompt_caching": true
     },
     "azure_ai/kimi-k2.6": {
         "deprecation_date": "2027-04-16",
@@ -10415,7 +10423,7 @@
         "max_tokens": 262144,
         "mode": "chat",
         "output_cost_per_token": 4e-06,
-        "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/introducing-kimi-k2-6-in-microsoft-foundry/4513125",
+        "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/kimi/",
         "supported_modalities": [
             "text",
             "image"
@@ -10426,7 +10434,9 @@
         "supports_function_calling": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 1.6e-07,
+        "supports_prompt_caching": true
     },
     "azure_ai/ministral-3b": {
         "input_cost_per_token": 4e-08,
@@ -12110,7 +12120,7 @@
         "max_output_tokens": 2048,
         "max_tokens": 2048,
         "mode": "chat",
-        "output_cost_per_token": 2.65e-06,
+        "output_cost_per_token": 6e-07,
         "supports_pdf_input": true
     },
     "bedrock/us-west-1/meta.llama3-70b-instruct-v1:0": {
@@ -23514,6 +23524,63 @@
         "web_search_billing_unit": "per_query",
         "google_maps_grounding_cost_per_query": 0.014
     },
+    "vertex_ai/gemini-3.8-flash": {
+        "prompt_cache_min_tokens": 4096,
+        "cache_read_input_token_cost": 7.5e-08,
+        "cache_read_input_token_cost_flex": 3.75e-08,
+        "input_cost_per_token": 7.5e-07,
+        "input_cost_per_token_batches": 3.75e-07,
+        "input_cost_per_token_flex": 3.75e-07,
+        "litellm_provider": "vertex_ai",
+        "max_input_tokens": 1048576,
+        "max_output_tokens": 65536,
+        "max_tokens": 65536,
+        "mode": "chat",
+        "output_cost_per_reasoning_token": 3.75e-06,
+        "output_cost_per_token": 3.75e-06,
+        "output_cost_per_token_batches": 1.875e-06,
+        "output_cost_per_token_flex": 1.875e-06,
+        "regional_endpoint_uplift_multiplier": 1.1,
+        "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing",
+        "supported_endpoints": [
+            "/v1/chat/completions",
+            "/v1/completions",
+            "/v1/batch"
+        ],
+        "supported_modalities": [
+            "text",
+            "image",
+            "audio",
+            "video"
+        ],
+        "supported_output_modalities": [
+            "text"
+        ],
+        "supports_audio_input": true,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_system_messages": true,
+        "supports_tool_choice": true,
+        "supports_url_context": true,
+        "supports_video_input": true,
+        "supports_vision": true,
+        "supports_web_search": true,
+        "supports_native_streaming": true,
+        "input_cost_per_token_priority": 1.35e-06,
+        "output_cost_per_token_priority": 6.75e-06,
+        "cache_read_input_token_cost_priority": 1.35e-07,
+        "search_context_cost_per_query": {
+            "search_context_size_low": 0.014,
+            "search_context_size_medium": 0.014,
+            "search_context_size_high": 0.014
+        },
+        "web_search_billing_unit": "per_query",
+        "google_maps_grounding_cost_per_query": 0.014
+    },
     "vertex_ai/gemini-3.1-pro-preview": {
         "prompt_cache_min_tokens": 4096,
         "cache_read_input_token_cost": 2e-07,
@@ -25351,6 +25418,65 @@
         "web_search_billing_unit": "per_query",
         "google_maps_grounding_cost_per_query": 0.014
     },
+    "gemini/gemini-3.8-flash": {
+        "prompt_cache_min_tokens": 4096,
+        "cache_read_input_token_cost": 7.5e-08,
+        "cache_read_input_token_cost_flex": 3.75e-08,
+        "input_cost_per_token": 7.5e-07,
+        "input_cost_per_token_batches": 3.75e-07,
+        "input_cost_per_token_flex": 3.75e-07,
+        "litellm_provider": "gemini",
+        "max_input_tokens": 1048576,
+        "max_output_tokens": 65536,
+        "max_tokens": 65536,
+        "mode": "chat",
+        "output_cost_per_reasoning_token": 3.75e-06,
+        "output_cost_per_token": 3.75e-06,
+        "output_cost_per_token_batches": 1.875e-06,
+        "output_cost_per_token_flex": 1.875e-06,
+        "rpm": 2000,
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
+        "supported_endpoints": [
+            "/v1/chat/completions",
+            "/v1/completions",
+            "/v1/batch"
+        ],
+        "supported_modalities": [
+            "text",
+            "image",
+            "audio",
+            "video"
+        ],
+        "supported_output_modalities": [
+            "text"
+        ],
+        "supports_audio_output": false,
+        "supports_audio_input": true,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_system_messages": true,
+        "supports_tool_choice": true,
+        "supports_url_context": true,
+        "supports_video_input": true,
+        "supports_vision": true,
+        "supports_web_search": true,
+        "supports_native_streaming": true,
+        "tpm": 800000,
+        "input_cost_per_token_priority": 1.35e-06,
+        "output_cost_per_token_priority": 6.75e-06,
+        "cache_read_input_token_cost_priority": 1.35e-07,
+        "search_context_cost_per_query": {
+            "search_context_size_low": 0.014,
+            "search_context_size_medium": 0.014,
+            "search_context_size_high": 0.014
+        },
+        "web_search_billing_unit": "per_query",
+        "google_maps_grounding_cost_per_query": 0.014
+    },
     "gemini/gemini-omni-flash-preview": {
         "input_cost_per_audio_token": 1.5e-06,
         "input_cost_per_token": 1.5e-06,
@@ -25759,6 +25885,63 @@
         "web_search_billing_unit": "per_query",
         "google_maps_grounding_cost_per_query": 0.014
     },
+    "gemini-3.8-flash": {
+        "prompt_cache_min_tokens": 4096,
+        "cache_read_input_token_cost": 7.5e-08,
+        "cache_read_input_token_cost_flex": 3.75e-08,
+        "input_cost_per_token": 7.5e-07,
+        "input_cost_per_token_batches": 3.75e-07,
+        "input_cost_per_token_flex": 3.75e-07,
+        "litellm_provider": "vertex_ai-language-models",
+        "max_input_tokens": 1048576,
+        "max_output_tokens": 65536,
+        "max_tokens": 65536,
+        "mode": "chat",
+        "output_cost_per_reasoning_token": 3.75e-06,
+        "output_cost_per_token": 3.75e-06,
+        "output_cost_per_token_batches": 1.875e-06,
+        "output_cost_per_token_flex": 1.875e-06,
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
+        "supported_endpoints": [
+            "/v1/chat/completions",
+            "/v1/completions",
+            "/v1/batch"
+        ],
+        "supported_modalities": [
+            "text",
+            "image",
+            "audio",
+            "video"
+        ],
+        "supported_output_modalities": [
+            "text"
+        ],
+        "supports_audio_output": false,
+        "supports_audio_input": true,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_system_messages": true,
+        "supports_tool_choice": true,
+        "supports_url_context": true,
+        "supports_video_input": true,
+        "supports_vision": true,
+        "supports_web_search": true,
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@@ -33736,19 +33954,21 @@
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@@ -33880,16 +34100,21 @@
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@@ -41340,13 +41565,13 @@
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@@ -41956,6 +42181,70 @@
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@@ -45849,6 +46138,26 @@
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@@ -47100,6 +47409,27 @@
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+        "supports_tool_choice": true,
+        "supports_vision": false
+    },
+    "azure/us-gov/text-embedding-3-large": {
+        "input_cost_per_token": 1.63e-07,
+        "litellm_provider": "azure",
+        "max_input_tokens": 8191,
+        "max_tokens": 8191,
+        "mode": "embedding",
+        "output_cost_per_token": 0.0
+    },
+    "azure/us-gov/text-embedding-3-small": {
+        "input_cost_per_token": 2.5e-08,
+        "litellm_provider": "azure",
+        "max_input_tokens": 8191,
+        "max_tokens": 8191,
+        "mode": "embedding",
+        "output_cost_per_token": 0.0
+    },
+    "cloudflare/@cf/openai/whisper": {
+        "input_cost_per_second": 7.5e-06,
+        "litellm_provider": "cloudflare",
+        "mode": "audio_transcription",
+        "output_cost_per_second": 0.0,
+        "source": "https://developers.cloudflare.com/workers-ai/models/whisper/",
+        "supported_endpoints": [
+            "/v1/audio/transcriptions"
+        ]
+    },
+    "cloudflare/@cf/openai/whisper-large-v3-turbo": {
+        "input_cost_per_second": 8.5e-06,
+        "litellm_provider": "cloudflare",
+        "mode": "audio_transcription",
+        "output_cost_per_second": 0.0,
+        "source": "https://developers.cloudflare.com/workers-ai/models/whisper-large-v3-turbo/",
+        "supported_endpoints": [
+            "/v1/audio/transcriptions"
+        ]
     }
 }
diff --git a/litellm/proxy/common_utils/model_listing_utils.py b/litellm/proxy/common_utils/model_listing_utils.py
index 9fd24162f7e..213a697b3dd 100644
--- a/litellm/proxy/common_utils/model_listing_utils.py
+++ b/litellm/proxy/common_utils/model_listing_utils.py
@@ -10,13 +10,36 @@ legacy internal names with `general_settings.use_team_public_model_name: false`.
 
 from __future__ import annotations
 
-from collections.abc import Mapping
+from collections.abc import Mapping, Sequence
+from types import MappingProxyType
 from typing import TYPE_CHECKING, Final, cast
 
 if TYPE_CHECKING:
     from litellm.router import Router
 
 
+def configured_display_names(
+    entries: Sequence[tuple[str, str]],
+    llm_router: Router | None,
+) -> Mapping[str, str]:
+    """response_id -> configured `model_info.display_name` for the listing entries
+    that have one.
+
+    Metadata is looked up by each entry's internal lookup id (so team-scoped rows
+    resolve), while the returned map is keyed by the public response id the
+    Anthropic-shaped listing is built from. Entries without a configured name are
+    omitted so the listing falls back to the id itself.
+    """
+    if llm_router is None:
+        return MappingProxyType({})
+    resolved: Final = (
+        (response_id, llm_router.get_configured_display_name(lookup_id)) for response_id, lookup_id in entries
+    )
+    return MappingProxyType(
+        {response_id: display_name for response_id, display_name in resolved if display_name is not None}
+    )
+
+
 class TeamModelNameTranslator:
     """Translates internal team routing keys to their public names for the model
     listing/retrieve responses. Stateless; the live router and general_settings
diff --git a/litellm/proxy/management_helpers/access_group_key_sync.py b/litellm/proxy/management_helpers/access_group_key_sync.py
index 5d43cb29978..c9f93fae0d9 100644
--- a/litellm/proxy/management_helpers/access_group_key_sync.py
+++ b/litellm/proxy/management_helpers/access_group_key_sync.py
@@ -38,6 +38,7 @@ from litellm.proxy._types import (
 from litellm.proxy.auth.auth_checks import (
     _delete_cache_access_object,  # pyright: ignore[reportPrivateUsage]  # the access-group endpoints reach for this same cache primitive
 )
+from litellm.proxy.db.routing_prisma_wrapper import WriterPinnedClient
 from litellm.repositories.table_repositories import AccessGroupRepository
 
 
@@ -72,8 +73,9 @@ _REPOINT_KEY_SQL: Final = (
 
 
 def _raw_executor(prisma_client: object) -> _RawExecutor:
-    """Narrow the untyped Prisma client down to the raw-query call this module makes."""
-    return AccessGroupRepository(prisma_client).prisma_client.db  # pyright: ignore[reportAny]  # untyped Prisma client
+    """Narrow the untyped Prisma client down to the raw-query call this module makes, pinned to the writer."""
+    db: Final = AccessGroupRepository(prisma_client).prisma_client.db  # pyright: ignore[reportAny]  # untyped Prisma client
+    return WriterPinnedClient(db).db  # pyright: ignore[reportAny, reportReturnType]  # untyped Prisma client behind the pin
 
 
 async def _invalidate_access_group_cache(access_group_id: str) -> None:
diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py
index 77a80ea0052..672d94077b2 100644
--- a/litellm/proxy/proxy_server.py
+++ b/litellm/proxy/proxy_server.py
@@ -352,7 +352,10 @@ from litellm.proxy.common_utils.load_config_utils import (
     get_file_contents_from_s3,
 )
 from litellm.proxy.common_utils.model_deprecation import collect_model_deprecations
-from litellm.proxy.common_utils.model_listing_utils import TeamModelNameTranslator
+from litellm.proxy.common_utils.model_listing_utils import (
+    TeamModelNameTranslator,
+    configured_display_names,
+)
 from litellm.proxy.common_utils.openai_endpoint_utils import (
     remove_sensitive_info_from_deployment,
 )
@@ -10223,7 +10226,8 @@ async def model_list(
         # The internal routing key drives the metadata/fallback lookup, while the
         # public name is what the client sees as the model id.
         model_data = []
-        for response_id, lookup_id in TeamModelNameTranslator.listing_entries(all_models, llm_router, settings):
+        admin_entries: Final = TeamModelNameTranslator.listing_entries(all_models, llm_router, settings)
+        for response_id, lookup_id in admin_entries:
             model_info = create_model_info_response(
                 model_id=lookup_id,
                 provider="openai",
@@ -10236,7 +10240,10 @@ async def model_list(
 
         if wants_anthropic_format:
             admin_listing: Final = cast(Sequence[ModelInfoResponse], model_data)  # cast-ok: rows built above
-            return create_anthropic_model_list_response(admin_listing)
+            return create_anthropic_model_list_response(
+                admin_listing,
+                display_names=configured_display_names(admin_entries, llm_router),
+            )
 
         return dict(
             data=model_data,
@@ -10267,7 +10274,8 @@ async def model_list(
     # The internal routing key drives the metadata/fallback lookup, while the
     # public name is what the client sees as the model id.
     model_data = []
-    for response_id, lookup_id in TeamModelNameTranslator.listing_entries(all_models, llm_router, settings):
+    entries: Final = TeamModelNameTranslator.listing_entries(all_models, llm_router, settings)
+    for response_id, lookup_id in entries:
         model_info = create_model_info_response(
             model_id=lookup_id,
             provider="openai",
@@ -10280,7 +10288,10 @@ async def model_list(
 
     if wants_anthropic_format:
         listing: Final = cast(Sequence[ModelInfoResponse], model_data)  # cast-ok: rows built above
-        return create_anthropic_model_list_response(listing)
+        return create_anthropic_model_list_response(
+            listing,
+            display_names=configured_display_names(entries, llm_router),
+        )
 
     return dict(
         data=model_data,
diff --git a/litellm/rerank_api/main.py b/litellm/rerank_api/main.py
index c8f7842aebf..37ca989b8d3 100644
--- a/litellm/rerank_api/main.py
+++ b/litellm/rerank_api/main.py
@@ -6,6 +6,7 @@ from typing import Any, Final, Literal
 
 import litellm
 from litellm._logging import verbose_logger
+from litellm.litellm_core_utils.get_llm_provider_logic import declared_authenticating_provider
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
 from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig
 from litellm.llms.bedrock.rerank.handler import BedrockRerankHandler
@@ -43,10 +44,23 @@ async def arerank(
     """
     Async: Reranks a list of documents based on their relevance to the query
     """
+    _custom_llm_provider: str | None = (
+        None  # rebind-ok: set by the declared-provider guard or the get_llm_provider unpack; read in the except
+    )
     try:
         loop: Final = asyncio.get_event_loop()
         kwargs["arerank"] = True
 
+        declared_provider: Final = declared_authenticating_provider(model, custom_llm_provider)
+        if declared_provider is not None:
+            _custom_llm_provider = declared_provider  # rebind-ok: see pre-declaration above
+        else:
+            _, _custom_llm_provider, _, _ = litellm.get_llm_provider(  # rebind-ok: see pre-declaration above
+                model=model,
+                custom_llm_provider=custom_llm_provider,
+                api_base=kwargs.get("api_base", None),
+            )
+
         func: Final = partial(
             rerank,
             model,
@@ -70,7 +84,11 @@ async def arerank(
             response = init_response
         return response
     except Exception as e:
-        raise e
+        raise exception_type(
+            model=model,
+            custom_llm_provider=_custom_llm_provider or custom_llm_provider,
+            original_exception=e,
+        )
 
 
 @client
@@ -115,6 +133,7 @@ def rerank(
     model_info: Final = kwargs.get("model_info", None)
     user: Final = kwargs.get("user", None)
     client: Final = kwargs.get("client", None)
+    _custom_llm_provider: str | None = None  # rebind-ok: set by the get_llm_provider unpack; read in the except
     try:
         _is_async: Final = kwargs.pop("arerank", False) is True
         optional_params: Final = GenericLiteLLMParams(**kwargs)
@@ -127,7 +146,7 @@ def rerank(
 
         (
             model,
-            _custom_llm_provider,
+            _custom_llm_provider,  # rebind-ok: see pre-declaration above
             dynamic_api_key,
             dynamic_api_base,
         ) = litellm.get_llm_provider(
@@ -538,4 +557,8 @@ def rerank(
         return response
     except Exception as e:
         verbose_logger.error("Error in rerank: %s", e)
-        raise exception_type(model=model, custom_llm_provider=custom_llm_provider, original_exception=e)
+        raise exception_type(
+            model=model,
+            custom_llm_provider=_custom_llm_provider or custom_llm_provider,
+            original_exception=e,
+        )
diff --git a/litellm/responses/litellm_completion_transformation/streaming_iterator.py b/litellm/responses/litellm_completion_transformation/streaming_iterator.py
index db1c3acbefb..bc25f4fffb1 100644
--- a/litellm/responses/litellm_completion_transformation/streaming_iterator.py
+++ b/litellm/responses/litellm_completion_transformation/streaming_iterator.py
@@ -1169,16 +1169,6 @@ class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator):
 
     def _emit_response_completed_event(self, litellm_model_response: ModelResponse) -> ResponseCompletedEvent | None:
         if litellm_model_response:
-            # Add cost to usage object if include_cost_in_streaming_usage is True
-            if litellm.include_cost_in_streaming_usage and self.litellm_logging_obj is not None:
-                usage: Final[object] = getattr(litellm_model_response, "usage", None)
-                if usage is not None:
-                    setattr(
-                        usage,
-                        "cost",
-                        self.litellm_logging_obj._response_cost_calculator(result=litellm_model_response),
-                    )
-
             # Transform the response
             responses_api_response: Final = (
                 LiteLLMCompletionResponsesConfig.transform_chat_completion_response_to_responses_api_response(
diff --git a/litellm/responses/streaming_iterator.py b/litellm/responses/streaming_iterator.py
index 82abac3e772..7871c85220c 100644
--- a/litellm/responses/streaming_iterator.py
+++ b/litellm/responses/streaming_iterator.py
@@ -407,23 +407,7 @@ class BaseResponsesAPIStreamingIterator:
                     openai_types.ResponsesAPIStreamEvents.RESPONSE_FAILED,
                 ):
                     self.completed_response = openai_responses_api_chunk
-                    # Add cost to usage object if include_cost_in_streaming_usage is True
-                    if litellm.include_cost_in_streaming_usage and self.logging_obj is not None:
-                        response_obj: Final[ResponsesAPIResponse | None] = getattr(
-                            openai_responses_api_chunk, "response", None
-                        )
-                        if response_obj:
-                            usage_obj: Final[ResponseAPIUsage | None] = getattr(response_obj, "usage", None)
-                            if usage_obj is not None:
-                                try:
-                                    cost: Final[float | None] = self.logging_obj._response_cost_calculator(
-                                        result=response_obj
-                                    )
-                                    if cost is not None:
-                                        setattr(usage_obj, "cost", cost)
-                                except Exception:
-                                    # Best-effort usage cost annotation should not break stream replay.
-                                    pass
+                    _stamp_responses_usage_cost(getattr(openai_responses_api_chunk, "response", None), self.logging_obj)
 
                     if _chunk_type == openai_types.ResponsesAPIStreamEvents.RESPONSE_FAILED:
                         self._handle_logging_failed_response()
@@ -1274,6 +1258,24 @@ def _add_text_like_part_events(
         )
 
 
+def _stamp_responses_usage_cost(
+    response_obj: ResponsesAPIResponse | None, logging_obj: LiteLLMLoggingObj | None
+) -> None:
+    if response_obj is None or logging_obj is None:
+        return
+    usage_obj: Final[ResponseAPIUsage | None] = getattr(response_obj, "usage", None)
+    if usage_obj is None:
+        return
+    if isinstance(getattr(usage_obj, "cost", None), (int, float)):
+        return
+    try:
+        cost: Final[float | None] = logging_obj._response_cost_calculator(result=response_obj)
+    except Exception:
+        return
+    if isinstance(cost, (int, float)) and cost > 0:
+        setattr(usage_obj, "cost", cost)
+
+
 def build_synthetic_response_events(
     *,
     transformed: ResponsesAPIResponse,
@@ -1281,15 +1283,7 @@ def build_synthetic_response_events(
     chunk_size: int,
 ) -> list[ResponsesAPIStreamingResponse]:
     openai_types: Final = _get_openai_response_types()
-    if litellm.include_cost_in_streaming_usage and logging_obj is not None:
-        usage_obj: Final = transformed.usage if hasattr(transformed, "usage") else None
-        if usage_obj is not None:
-            try:
-                cost: Final[float | None] = logging_obj._response_cost_calculator(result=transformed)
-                if cost is not None:
-                    setattr(usage_obj, "cost", cost)
-            except Exception:
-                pass
+    _stamp_responses_usage_cost(transformed, logging_obj)
 
     events: Final[list[ResponsesAPIStreamingResponse]] = [
         _build_response_status_event(openai_types.ResponsesAPIStreamEvents.RESPONSE_CREATED, transformed),
diff --git a/litellm/router.py b/litellm/router.py
index 23d8907fb49..3068433b9c3 100644
--- a/litellm/router.py
+++ b/litellm/router.py
@@ -9746,6 +9746,26 @@ class Router:
             coerce_token_limit(model_info.get("max_output_tokens")),
         )
 
+    def get_configured_display_name(self, model_name: str) -> "str | None":
+        """
+        Return the display_name explicitly configured in a concrete deployment's
+        model_info for model_name, via O(1) index lookup.
+
+        Returns None for wildcard-expanded or unknown names, and treats a
+        non-string or empty configured value as absent rather than failing the
+        listing. Like get_configured_token_limits, this never triggers pattern
+        matching or deep copies, so it is safe to call per listed model on the
+        /v1/models hot path.
+        """
+        deployment: Final = self.get_deployment_by_model_group_name(model_group_name=model_name)
+        if deployment is None:
+            return None
+
+        display_name: Final = deployment.model_info.get("display_name")
+        if isinstance(display_name, str) and display_name.strip():
+            return display_name
+        return None
+
     def get_deployment_credentials_with_provider(
         self, model_id: str, team_id: str | None = None
     ) -> dict[str, Any] | None:
diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json
index a3cfb300ea6..10e8b56d925 100644
--- a/model_prices_and_context_window.json
+++ b/model_prices_and_context_window.json
@@ -9643,7 +9643,8 @@
         "supported_endpoints": [
             "/v1/images/generations",
             "/v1/images/edits"
-        ]
+        ],
+        "deprecation_date": "2026-10-01"
     },
     "azure_ai/MAI-Image-2.5-Flash": {
         "input_cost_per_image_token": 1.75e-06,
@@ -9656,7 +9657,8 @@
         "supported_endpoints": [
             "/v1/images/generations",
             "/v1/images/edits"
-        ]
+        ],
+        "deprecation_date": "2026-10-01"
     },
     "azure_ai/MAI-Image-2e": {
         "deprecation_date": "2026-08-15",
@@ -10155,7 +10157,9 @@
         "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/",
         "supports_function_calling": true,
         "supports_reasoning": true,
-        "supports_tool_choice": true
+        "supports_tool_choice": true,
+        "cache_read_input_token_cost": 1.45e-07,
+        "supports_prompt_caching": true
     },
     "azure_ai/deepseek-v4-flash": {
         "deprecation_date": "2028-02-20",
@@ -10169,18 +10173,20 @@
         "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/",
         "supports_function_calling": true,
         "supports_reasoning": true,
-        "supports_tool_choice": true
+        "supports_tool_choice": true,
+        "cache_read_input_token_cost": 2.8e-08,
+        "supports_prompt_caching": true
     },
     "azure_ai/deepseek-v4-flash-0731": {
-        "cache_read_input_token_cost": 2.8e-08,
+        "cache_read_input_token_cost": 1.4e-08,
         "deprecation_date": "2026-12-03",
-        "input_cost_per_token": 1.9e-07,
+        "input_cost_per_token": 4.4e-07,
         "litellm_provider": "azure_ai",
         "max_input_tokens": 1000000,
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
-        "output_cost_per_token": 5.1e-07,
+        "output_cost_per_token": 1.32e-06,
         "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/",
         "supports_function_calling": true,
         "supports_prompt_caching": true,
@@ -10400,11 +10406,13 @@
         "max_tokens": 262144,
         "mode": "chat",
         "output_cost_per_token": 3e-06,
-        "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/kimi-k2-5-now-in-microsoft-foundry/4492321",
+        "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/kimi/",
         "supports_function_calling": true,
         "supports_tool_choice": true,
         "supports_video_input": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 1e-07,
+        "supports_prompt_caching": true
     },
     "azure_ai/kimi-k2.6": {
         "deprecation_date": "2027-04-16",
@@ -10415,7 +10423,7 @@
         "max_tokens": 262144,
         "mode": "chat",
         "output_cost_per_token": 4e-06,
-        "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/introducing-kimi-k2-6-in-microsoft-foundry/4513125",
+        "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/kimi/",
         "supported_modalities": [
             "text",
             "image"
@@ -10426,7 +10434,9 @@
         "supports_function_calling": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "cache_read_input_token_cost": 1.6e-07,
+        "supports_prompt_caching": true
     },
     "azure_ai/ministral-3b": {
         "input_cost_per_token": 4e-08,
@@ -12110,7 +12120,7 @@
         "max_output_tokens": 2048,
         "max_tokens": 2048,
         "mode": "chat",
-        "output_cost_per_token": 2.65e-06,
+        "output_cost_per_token": 6e-07,
         "supports_pdf_input": true
     },
     "bedrock/us-west-1/meta.llama3-70b-instruct-v1:0": {
@@ -23514,6 +23524,63 @@
         "web_search_billing_unit": "per_query",
         "google_maps_grounding_cost_per_query": 0.014
     },
+    "vertex_ai/gemini-3.8-flash": {
+        "prompt_cache_min_tokens": 4096,
+        "cache_read_input_token_cost": 7.5e-08,
+        "cache_read_input_token_cost_flex": 3.75e-08,
+        "input_cost_per_token": 7.5e-07,
+        "input_cost_per_token_batches": 3.75e-07,
+        "input_cost_per_token_flex": 3.75e-07,
+        "litellm_provider": "vertex_ai",
+        "max_input_tokens": 1048576,
+        "max_output_tokens": 65536,
+        "max_tokens": 65536,
+        "mode": "chat",
+        "output_cost_per_reasoning_token": 3.75e-06,
+        "output_cost_per_token": 3.75e-06,
+        "output_cost_per_token_batches": 1.875e-06,
+        "output_cost_per_token_flex": 1.875e-06,
+        "regional_endpoint_uplift_multiplier": 1.1,
+        "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing",
+        "supported_endpoints": [
+            "/v1/chat/completions",
+            "/v1/completions",
+            "/v1/batch"
+        ],
+        "supported_modalities": [
+            "text",
+            "image",
+            "audio",
+            "video"
+        ],
+        "supported_output_modalities": [
+            "text"
+        ],
+        "supports_audio_input": true,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_system_messages": true,
+        "supports_tool_choice": true,
+        "supports_url_context": true,
+        "supports_video_input": true,
+        "supports_vision": true,
+        "supports_web_search": true,
+        "supports_native_streaming": true,
+        "input_cost_per_token_priority": 1.35e-06,
+        "output_cost_per_token_priority": 6.75e-06,
+        "cache_read_input_token_cost_priority": 1.35e-07,
+        "search_context_cost_per_query": {
+            "search_context_size_low": 0.014,
+            "search_context_size_medium": 0.014,
+            "search_context_size_high": 0.014
+        },
+        "web_search_billing_unit": "per_query",
+        "google_maps_grounding_cost_per_query": 0.014
+    },
     "vertex_ai/gemini-3.1-pro-preview": {
         "prompt_cache_min_tokens": 4096,
         "cache_read_input_token_cost": 2e-07,
@@ -25351,6 +25418,65 @@
         "web_search_billing_unit": "per_query",
         "google_maps_grounding_cost_per_query": 0.014
     },
+    "gemini/gemini-3.8-flash": {
+        "prompt_cache_min_tokens": 4096,
+        "cache_read_input_token_cost": 7.5e-08,
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@@ -25759,6 +25885,63 @@
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@@ -29602,7 +29804,10 @@
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@@ -29751,7 +29956,10 @@
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@@ -29849,7 +30060,9 @@
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@@ -30834,17 +31049,18 @@
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@@ -33705,19 +33921,21 @@
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@@ -33736,19 +33954,21 @@
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@@ -33880,16 +34100,21 @@
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@@ -41340,13 +41565,13 @@
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@@ -41956,6 +42181,70 @@
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+        "litellm_provider": "bedrock_mantle",
+        "max_input_tokens": 1050000,
+        "max_output_tokens": 128000,
+        "max_tokens": 128000,
+        "mode": "responses",
+        "use_openai_responses_path": true,
+        "supported_endpoints": [
+            "/v1/responses"
+        ],
+        "supported_modalities": [
+            "text",
+            "image"
+        ],
+        "supported_output_modalities": [
+            "text"
+        ],
+        "supports_function_calling": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_tool_choice": true,
+        "supports_vision": true,
+        "input_cost_per_token": 3.3e-06,
+        "cache_read_input_token_cost": 3.3e-07,
+        "output_cost_per_token": 1.98e-05
+    },
+    "bedrock_mantle/us-gov-west-1/xai.grok-4.3": {
+        "use_openai_responses_path": true,
+        "litellm_provider": "bedrock_mantle",
+        "max_input_tokens": 131072,
+        "max_output_tokens": 16384,
+        "max_tokens": 16384,
+        "mode": "chat",
+        "supported_endpoints": [
+            "/v1/chat/completions",
+            "/v1/responses"
+        ],
+        "supports_function_calling": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_tool_choice": true,
+        "supports_vision": true,
+        "source": "https://aws.amazon.com/bedrock/pricing/",
+        "input_cost_per_token": 1.5e-06,
+        "output_cost_per_token": 3e-06,
+        "cache_read_input_token_cost": 2.4e-07
+    },
+    "bedrock_mantle/us-gov-east-1/openai.gpt-5.4": {
+        "litellm_provider": "bedrock_mantle",
+        "max_input_tokens": 1050000,
+        "max_output_tokens": 128000,
+        "max_tokens": 128000,
+        "mode": "responses",
+        "use_openai_responses_path": true,
+        "supported_endpoints": [
+            "/v1/responses"
+        ],
+        "supported_modalities": [
+            "text",
+            "image"
+        ],
+        "supported_output_modalities": [
+            "text"
+        ],
+        "supports_function_calling": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_tool_choice": true,
+        "supports_vision": true,
+        "input_cost_per_token": 3.3e-06,
+        "cache_read_input_token_cost": 3.3e-07,
+        "output_cost_per_token": 1.98e-05
+    },
+    "azure/us-gov/gpt-5.1": {
+        "cache_read_input_token_cost": 1.71875e-07,
+        "default_reasoning_effort": "none",
+        "input_cost_per_token": 1.71875e-06,
+        "litellm_provider": "azure",
+        "max_input_tokens": 272000,
+        "max_output_tokens": 128000,
+        "max_tokens": 128000,
+        "mode": "chat",
+        "output_cost_per_token": 1.375e-05,
+        "supported_endpoints": [
+            "/v1/chat/completions",
+            "/v1/batch",
+            "/v1/responses"
+        ],
+        "supported_modalities": [
+            "text",
+            "image"
+        ],
+        "supported_output_modalities": [
+            "text",
+            "image"
+        ],
+        "supports_function_calling": true,
+        "supports_native_streaming": true,
+        "supports_none_reasoning_effort": true,
+        "supports_parallel_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_system_messages": true,
+        "supports_tool_choice": true,
+        "supports_vision": true
+    },
+    "azure/us-gov/o3-mini": {
+        "cache_read_input_token_cost": 7.57e-07,
+        "input_cost_per_token": 1.513e-06,
+        "litellm_provider": "azure",
+        "max_input_tokens": 200000,
+        "max_output_tokens": 100000,
+        "max_tokens": 100000,
+        "mode": "chat",
+        "output_cost_per_token": 6.05e-06,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_tool_choice": true,
+        "supports_vision": false
+    },
+    "azure/us-gov/text-embedding-3-large": {
+        "input_cost_per_token": 1.63e-07,
+        "litellm_provider": "azure",
+        "max_input_tokens": 8191,
+        "max_tokens": 8191,
+        "mode": "embedding",
+        "output_cost_per_token": 0.0
+    },
+    "azure/us-gov/text-embedding-3-small": {
+        "input_cost_per_token": 2.5e-08,
+        "litellm_provider": "azure",
+        "max_input_tokens": 8191,
+        "max_tokens": 8191,
+        "mode": "embedding",
+        "output_cost_per_token": 0.0
+    },
+    "cloudflare/@cf/openai/whisper": {
+        "input_cost_per_second": 7.5e-06,
+        "litellm_provider": "cloudflare",
+        "mode": "audio_transcription",
+        "output_cost_per_second": 0.0,
+        "source": "https://developers.cloudflare.com/workers-ai/models/whisper/",
+        "supported_endpoints": [
+            "/v1/audio/transcriptions"
+        ]
+    },
+    "cloudflare/@cf/openai/whisper-large-v3-turbo": {
+        "input_cost_per_second": 8.5e-06,
+        "litellm_provider": "cloudflare",
+        "mode": "audio_transcription",
+        "output_cost_per_second": 0.0,
+        "source": "https://developers.cloudflare.com/workers-ai/models/whisper-large-v3-turbo/",
+        "supported_endpoints": [
+            "/v1/audio/transcriptions"
+        ]
     }
 }
diff --git a/tests/e2e/test_junit_properties.py b/tests/e2e/test_junit_properties.py
index c0596177cc1..02c1413c840 100644
--- a/tests/e2e/test_junit_properties.py
+++ b/tests/e2e/test_junit_properties.py
@@ -24,25 +24,10 @@ from junit_properties import (
 )
 
 
-class FakeMarker:
-    def __init__(self, name: str, *args: object) -> None:
-        self.name = name
-        self.args = args
-
-
-class FakeItem:
-    """The three attributes junit_properties reads off a pytest Item."""
-
-    def __init__(
-        self, nodeid: str, location: tuple[str, int | None, str], markers: tuple[FakeMarker, ...] = ()
-    ) -> None:
-        self.nodeid = nodeid
-        self.location = location
-        self.user_properties: list[tuple[str, str]] = []
-        self._markers = markers
-
-    def iter_markers(self, name: str):
-        return (marker for marker in self._markers if marker.name == name)
+def collected_item(request: pytest.FixtureRequest, name: str) -> pytest.Item:
+    """The Item pytest collected for test ``name`` in this file: the real nodeid,
+    location and marker machinery the collection hook reads, as pytest built it."""
+    return next(item for item in request.session.items if item.path == request.path and item.name == name)
 
 
 def repo_root() -> Path | None:
@@ -109,22 +94,22 @@ class TestSourceFromLocation:
 
 
 class TestResultProperties:
-    def test_every_test_carries_package_covers_and_source(self) -> None:
-        item = FakeItem(
-            "logging/test_x.py::TestFoo::test_bar",
-            ("logging/test_x.py", 40, "TestFoo.test_bar"),
-            (FakeMarker("covers", "LOG-1", "LOG-2"),),
-        )
-        assert result_properties(item) == (
-            ("package", "logging"),
+    def test_every_test_carries_package_covers_and_source(self, request: pytest.FixtureRequest) -> None:
+        """Read off this test's own collected Item, so the nodeid and location are
+        whatever pytest reports for the launch shape in use, and the marker is added
+        at run time so the coverage registry's collect-only pass never sees it."""
+        test = type(self).test_every_test_carries_package_covers_and_source
+        request.applymarker(pytest.mark.covers("LOG-1", "LOG-2"))
+        assert result_properties(collected_item(request, test.__name__)) == (
+            ("package", "root"),
             ("covers", "LOG-1,LOG-2"),
-            ("source", "tests/e2e/logging/test_x.py:41"),
+            ("source", f"tests/e2e/test_junit_properties.py:{test.__code__.co_firstlineno}"),
         )
 
-    def test_attach_is_idempotent(self) -> None:
+    def test_attach_is_idempotent(self, request: pytest.FixtureRequest) -> None:
         """Collection can run the hook more than once; a second pass must not
         double the <property> entries in the report."""
-        item = FakeItem("logging/test_x.py::test_bar", ("logging/test_x.py", 40, "test_bar"))
+        item = collected_item(request, type(self).test_attach_is_idempotent.__name__)
         attach_result_properties(item)
         attach_result_properties(item)
         assert [name for name, _ in item.user_properties] == ["package", "covers", "source"]
diff --git a/tests/ocr_tests/test_ocr_vertex_ai.py b/tests/ocr_tests/test_ocr_vertex_ai.py
index 1ba5b9d0883..1842eb063a5 100644
--- a/tests/ocr_tests/test_ocr_vertex_ai.py
+++ b/tests/ocr_tests/test_ocr_vertex_ai.py
@@ -5,9 +5,11 @@ Note: Vertex AI OCR automatically converts URLs to base64 data URIs since
 the Vertex AI endpoint doesn't have internet access.
 """
 
-import os
 import json
+import os
 import tempfile
+from typing import Final
+
 import pytest
 from base_ocr_unit_tests import BaseOCRTest
 
@@ -139,3 +141,19 @@ def test_vertex_ai_ocr_routing():
     assert isinstance(
         deepseek_variant, VertexAIDeepSeekOCRConfig
     ), "DeepSeek variant should route to VertexAIDeepSeekOCRConfig"
+
+
+@pytest.mark.parametrize("model", ("deepseek-ocr-maas", "deepseek-ai/deepseek-ocr-maas"))
+def test_deepseek_request_uses_single_provider_namespace(model: str) -> None:
+    from litellm.llms.vertex_ai.ocr.deepseek_transformation import (
+        VertexAIDeepSeekOCRConfig,
+    )
+
+    request: Final = VertexAIDeepSeekOCRConfig().transform_ocr_request(
+        model=model,
+        document={"type": "image_url", "image_url": "data:image/png;base64,AA=="},
+        optional_params={},
+        headers={},
+    )
+
+    assert request.data["model"] == "deepseek-ai/deepseek-ocr-maas"
diff --git a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
index 0e1c832ebf5..b7f0ca1efe1 100644
--- a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
+++ b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
@@ -1522,7 +1522,7 @@ def test_gpt_5_6_alias_prices_match_sol(local_model_cost_map):
     sol = litellm.model_cost["gpt-5.6-sol"]
 
     cost_fields = sorted(field for field in sol if "cost" in field)
-    assert len(cost_fields) == 23
+    assert len(cost_fields) == 27
 
     for field in cost_fields:
         assert alias.get(field) == sol.get(field), field
@@ -4039,8 +4039,8 @@ def test_fast_service_tier_matches_priority_above_the_context_threshold(_local_m
     )
 
     assert fast == priority
-    assert fast[0] == pytest.approx(300_000 * 8e-06, rel=1e-9)
-    assert fast[1] == pytest.approx(1_000 * 3e-05, rel=1e-9)
+    assert fast[0] == pytest.approx(300_000 * 1.6e-05, rel=1e-9)
+    assert fast[1] == pytest.approx(1_000 * 6e-05, rel=1e-9)
 
 
 def test_priority_reasoning_tokens_bill_at_the_priority_output_rate(_local_model_cost_map):
@@ -4200,6 +4200,86 @@ def test_generic_cost_per_token_gemini_37_flash(_local_model_cost_map):
     assert completion_cost == pytest.approx(0.001875)
 
 
+GEMINI_38_FLASH_LAUNCH_PRICING = [
+    ("gemini-3.8-flash", 7.5e-07, 3.75e-06, 7.5e-08),
+    ("gemini/gemini-3.8-flash", 7.5e-07, 3.75e-06, 7.5e-08),
+    ("vertex_ai/gemini-3.8-flash", 7.5e-07, 3.75e-06, 7.5e-08),
+]
+
+
+@pytest.mark.parametrize("model,input_cost,output_cost,cache_read_cost", GEMINI_38_FLASH_LAUNCH_PRICING)
+def test_gemini_38_flash_launch_pricing(model, input_cost, output_cost, cache_read_cost, _local_model_cost_map):
+    model_cost_map = litellm.model_cost[model]
+    assert model_cost_map["input_cost_per_token"] == input_cost
+    assert model_cost_map["output_cost_per_token"] == output_cost
+    assert model_cost_map["output_cost_per_reasoning_token"] == output_cost
+    assert model_cost_map["cache_read_input_token_cost"] == cache_read_cost
+    assert model_cost_map["mode"] == "chat"
+    assert model_cost_map["supports_reasoning"] is True
+    assert model_cost_map["supports_function_calling"] is True
+    assert model_cost_map["max_input_tokens"] == 1048576
+
+
+GEMINI_38_FLASH_FIELDS_SHARED_WITH_37_FLASH = (
+    "input_cost_per_token",
+    "output_cost_per_token",
+    "output_cost_per_reasoning_token",
+    "cache_read_input_token_cost",
+    "input_cost_per_token_batches",
+    "output_cost_per_token_batches",
+    "input_cost_per_token_flex",
+    "output_cost_per_token_flex",
+    "cache_read_input_token_cost_flex",
+    "input_cost_per_token_priority",
+    "output_cost_per_token_priority",
+    "cache_read_input_token_cost_priority",
+    "search_context_cost_per_query",
+    "google_maps_grounding_cost_per_query",
+    "prompt_cache_min_tokens",
+    "max_input_tokens",
+    "max_output_tokens",
+    "supports_reasoning",
+    "supports_function_calling",
+    "supports_prompt_caching",
+    "supports_vision",
+    "supports_pdf_input",
+    "supports_audio_input",
+    "supports_video_input",
+    "supports_response_schema",
+    "supports_tool_choice",
+    "supports_web_search",
+    "supports_url_context",
+)
+
+
+@pytest.mark.parametrize("prefix", ["", "gemini/", "vertex_ai/"])
+def test_gemini_38_flash_matches_37_flash_promotional_pricing(prefix, _local_model_cost_map):
+    new_model = litellm.model_cost[f"{prefix}gemini-3.8-flash"]
+    old_model = litellm.model_cost[f"{prefix}gemini-3.7-flash"]
+    for field in GEMINI_38_FLASH_FIELDS_SHARED_WITH_37_FLASH:
+        assert new_model[field] == old_model[field], field
+
+
+def test_generic_cost_per_token_gemini_38_flash(_local_model_cost_map):
+    usage = Usage(
+        prompt_tokens=1000,
+        completion_tokens=500,
+        total_tokens=1500,
+        completion_tokens_details=CompletionTokensDetailsWrapper(
+            reasoning_tokens=200,
+            text_tokens=300,
+        ),
+        prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=1000),
+    )
+    prompt_cost, completion_cost = generic_cost_per_token(
+        model="gemini-3.8-flash",
+        usage=usage,
+        custom_llm_provider="gemini",
+    )
+    assert prompt_cost == pytest.approx(0.00075)
+    assert completion_cost == pytest.approx(0.001875)
+
+
 def test_grok_46_launch_pricing(_local_model_cost_map):
     model_cost_map = litellm.model_cost["xai/grok-4.6"]
     assert model_cost_map["input_cost_per_token"] == 2e-06
diff --git a/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py b/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py
index 8ac050a04f9..bacbcbf132b 100644
--- a/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py
+++ b/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py
@@ -592,6 +592,59 @@ def test_stream_chunk_builder_litellm_usage_chunks():
     assert usage.total_tokens == 77
 
 
+def test_calculate_usage_honors_openai_sdk_completion_usage_chunks():
+    from openai.types.completion_usage import CompletionUsage
+
+    content_chunk = ModelResponseStream(
+        id="chatcmpl-sdk-usage-1",
+        created=1745513206,
+        model="mantle-claude",
+        object="chat.completion.chunk",
+        system_fingerprint=None,
+        choices=[
+            StreamingChoices(
+                finish_reason="stop",
+                index=0,
+                delta=Delta(
+                    provider_specific_fields=None,
+                    content="ok",
+                    role=None,
+                    function_call=None,
+                    tool_calls=None,
+                    audio=None,
+                ),
+                logprobs=None,
+            )
+        ],
+        provider_specific_fields=None,
+        stream_options={"include_usage": True},
+    )
+    usage_chunk = ModelResponseStream(
+        id="chatcmpl-sdk-usage-1",
+        created=1745513207,
+        model="mantle-claude",
+        object="chat.completion.chunk",
+        system_fingerprint=None,
+        choices=[],
+        provider_specific_fields=None,
+        stream_options={"include_usage": True},
+    )
+    usage_chunk.usage = CompletionUsage(
+        prompt_tokens=20, completion_tokens=60, total_tokens=80, cost=0.000704
+    )
+    assert type(usage_chunk.usage) is CompletionUsage
+
+    chunks = [content_chunk, usage_chunk]
+    usage = ChunkProcessor(chunks=chunks).calculate_usage(
+        chunks=chunks, model="mantle-claude", completion_output=""
+    )
+
+    assert usage.prompt_tokens == 20
+    assert usage.completion_tokens == 60
+    assert usage.total_tokens == 80
+    assert getattr(usage, "cost", None) == pytest.approx(0.000704)
+
+
 def test_get_model_from_chunks_azure_model_router():
     """
     Test that _get_model_from_chunks finds the actual model from Azure Model Router chunks.
diff --git a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_ai_gemini_transformation.py b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_ai_gemini_transformation.py
index 8c1de12e7d9..4679b978f78 100644
--- a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_ai_gemini_transformation.py
+++ b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_ai_gemini_transformation.py
@@ -1096,10 +1096,13 @@ def test_natively_signed_parallel_turn_never_carries_a_placeholder(model):
         "gemini-3.5-flash",
         "gemini-3.6-flash",
         "gemini-3.7-flash",
+        "gemini-3.8-flash",
         "vertex_ai/gemini-3.5-flash",
         "vertex_ai/gemini-3.7-flash",
+        "vertex_ai/gemini-3.8-flash",
         "gemini/gemini-3.5-flash",
         "gemini/gemini-3.7-flash",
+        "gemini/gemini-3.8-flash",
     ],
 )
 def test_placeholder_scoped_to_first_call_across_gemini_3_variants(model):
diff --git a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py
index bd07bec900f..d2788408e09 100644
--- a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py
+++ b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py
@@ -1185,6 +1185,18 @@ def test_vertex_ai_map_thinking_param_with_budget_tokens_0():
     }
 
 
+def test_vertex_ai_map_thinking_param_without_budget_tokens_for_gemini_3():
+    v = VertexGeminiConfig()
+    result = v.map_openai_params(
+        non_default_params={"thinking": {"type": "enabled"}},
+        optional_params={},
+        model="gemini-3.5-flash",
+        drop_params=False,
+    )
+
+    assert result["thinkingConfig"] == {"includeThoughts": True}
+
+
 def test_vertex_ai_map_tools():
     v = VertexGeminiConfig()
     optional_params = {}
diff --git a/tests/test_litellm/proxy/management_helpers/test_access_group_key_sync.py b/tests/test_litellm/proxy/management_helpers/test_access_group_key_sync.py
new file mode 100644
index 00000000000..60c36e33e09
--- /dev/null
+++ b/tests/test_litellm/proxy/management_helpers/test_access_group_key_sync.py
@@ -0,0 +1,57 @@
+from types import SimpleNamespace
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+
+from litellm.proxy.db.prisma_client import PrismaWrapper
+from litellm.proxy.db.routing_prisma_wrapper import RoutingPrismaWrapper
+from litellm.proxy.management_helpers.access_group_key_sync import (
+    sync_key_access_group_membership,
+    sync_key_regeneration_access_group_membership,
+)
+
+
+def _routed_prisma_client():
+    writer_inner = MagicMock(name="writer_prisma")
+    reader_inner = MagicMock(name="reader_prisma")
+    writer_inner.query_raw = AsyncMock(return_value=[])
+    reader_inner.query_raw = AsyncMock(return_value=[])
+    writer = PrismaWrapper(original_prisma=writer_inner, iam_token_db_auth=False)
+    reader = PrismaWrapper(original_prisma=reader_inner, iam_token_db_auth=False)
+    routing = RoutingPrismaWrapper(writer=writer, reader=reader)
+    return SimpleNamespace(db=routing), writer_inner, reader_inner
+
+
+@pytest.mark.asyncio
+async def test_regeneration_repoint_update_runs_on_the_writer():
+    prisma_client, writer_inner, reader_inner = _routed_prisma_client()
+
+    await sync_key_regeneration_access_group_membership(
+        prisma_client=prisma_client,
+        previous_key_token="old-token",
+        new_key_token="new-token",
+        data=None,
+        existing_key_row=MagicMock(),
+    )
+
+    writer_inner.query_raw.assert_awaited_once()
+    assert writer_inner.query_raw.await_args.args[0].startswith('UPDATE "LiteLLM_AccessGroupTable"')
+    reader_inner.query_raw.assert_not_awaited()
+
+
+@pytest.mark.asyncio
+async def test_membership_attach_and_detach_updates_run_on_the_writer():
+    prisma_client, writer_inner, reader_inner = _routed_prisma_client()
+
+    await sync_key_access_group_membership(
+        prisma_client=prisma_client,
+        key_token="token",
+        previous_access_group_ids=["ag-old"],
+        updated_access_group_ids=["ag-new"],
+    )
+
+    assert writer_inner.query_raw.await_count == 2
+    assert all(
+        call.args[0].startswith('UPDATE "LiteLLM_AccessGroupTable"') for call in writer_inner.query_raw.await_args_list
+    )
+    reader_inner.query_raw.assert_not_awaited()
diff --git a/tests/test_litellm/proxy/proxy_server/test_routes_models.py b/tests/test_litellm/proxy/proxy_server/test_routes_models.py
index 2b126b1ea95..bc6106a06f8 100644
--- a/tests/test_litellm/proxy/proxy_server/test_routes_models.py
+++ b/tests/test_litellm/proxy/proxy_server/test_routes_models.py
@@ -45,6 +45,7 @@ def patched_models(monkeypatch):
     deployment = MagicMock()
     deployment.litellm_params.model = "gpt-4"
     router.get_deployment_by_model_group_name = MagicMock(return_value=deployment)
+    router.get_configured_display_name = MagicMock(return_value=None)
 
     monkeypatch.setattr(proxy_server, "llm_router", router)
     monkeypatch.setattr(proxy_server, "prisma_client", MagicMock())
@@ -187,6 +188,83 @@ def test_anthropic_format_carries_router_configured_token_limits(client, auth_as
     assert (claude["max_input_tokens"], claude["max_tokens"]) == (500000, 4096)
 
 
+@pytest.mark.parametrize("path", ["/v1/models", "/models"])
+def test_anthropic_format_uses_configured_display_name(client, auth_as, patched_models, path):
+    """A deployment's ``model_info.display_name`` becomes the Anthropic-native
+    ``display_name`` so Claude Code's picker shows a clean name while the id keeps
+    routing; models without one keep the id fallback, and the OpenAI-shaped
+    listing carries no display_name either way."""
+
+    def _configured(model_name):
+        return "Kimi K3" if model_name == "gpt-4" else None
+
+    patched_models.get_configured_display_name = MagicMock(side_effect=_configured)
+
+    with auth_as():
+        anthropic_response = client.get(path, headers={"anthropic-version": "2023-06-01"})
+        openai_response = client.get(path)
+
+    assert anthropic_response.status_code == 200
+    gpt_4, claude = anthropic_response.json()["data"]
+    assert (gpt_4["id"], gpt_4["display_name"]) == ("gpt-4", "Kimi K3")
+    assert (claude["id"], claude["display_name"]) == ("claude-sonnet", "claude-sonnet")
+
+    assert openai_response.status_code == 200
+    openai_models = openai_response.json()["data"]
+    assert [m["id"] for m in openai_models] == ["gpt-4", "claude-sonnet"]
+    assert all("display_name" not in m for m in openai_models)
+
+
+@pytest.mark.parametrize("params", [{}, {"scope": "expand"}])
+def test_anthropic_display_name_resolved_via_internal_team_key(
+    client, auth_as, patched_models, monkeypatch, params
+):
+    """For a team-scoped row the configured display name must be looked up by the
+    internal routing key while the entry itself is keyed by the public name, so
+    the clean name lands on the id the client actually sees."""
+    from litellm.proxy import utils as proxy_utils
+    from litellm.proxy.auth import model_checks
+
+    internal_name = "model_name_team-1_c0ffee"
+
+    patched_models.get_model_list = MagicMock(
+        return_value=[
+            {
+                "model_name": internal_name,
+                "model_info": {
+                    "team_id": "team-1",
+                    "team_public_model_name": "gpt-4-team",
+                },
+            }
+        ]
+    )
+    patched_models.get_model_names = MagicMock(return_value=[internal_name])
+    patched_models.get_configured_display_name = MagicMock(
+        side_effect=lambda model_name: "Team GPT" if model_name == internal_name else None
+    )
+
+    async def _fake_get_available_models_for_user(**kwargs):
+        return [internal_name]
+
+    monkeypatch.setattr(
+        proxy_utils,
+        "get_available_models_for_user",
+        _fake_get_available_models_for_user,
+    )
+    monkeypatch.setattr(
+        model_checks, "get_complete_model_list", lambda **kwargs: [internal_name]
+    )
+
+    with auth_as():
+        response = client.get(
+            "/v1/models", params=params, headers={"anthropic-version": "2023-06-01"}
+        )
+
+    assert response.status_code == 200
+    (entry,) = response.json()["data"]
+    assert (entry["id"], entry["display_name"]) == ("gpt-4-team", "Team GPT")
+
+
 @pytest.mark.parametrize("path", ["/v1/models", "/models"])
 def test_get_models_invalid_scope_returns_400(client, auth_as, patched_models, path):
     """Pins: ``GET /v1/models``, ``GET /models`` (error path: invalid scope)."""
diff --git a/tests/test_litellm/proxy/proxy_server/test_team_model_name_translation.py b/tests/test_litellm/proxy/proxy_server/test_team_model_name_translation.py
index aa35fd64f18..0fb9b1a6d88 100644
--- a/tests/test_litellm/proxy/proxy_server/test_team_model_name_translation.py
+++ b/tests/test_litellm/proxy/proxy_server/test_team_model_name_translation.py
@@ -19,7 +19,10 @@ from litellm.proxy._types import (
     LitellmUserRoles,
     UserAPIKeyAuth,
 )
-from litellm.proxy.common_utils.model_listing_utils import TeamModelNameTranslator
+from litellm.proxy.common_utils.model_listing_utils import (
+    TeamModelNameTranslator,
+    configured_display_names,
+)
 from litellm.proxy.proxy_server import (
     _get_proxy_model_info,
     _translate_model_name_for_response,
@@ -1391,6 +1394,27 @@ def test_resolve_public_name_respects_legacy_flag():
     )
 
 
+def test_configured_display_names_keyed_by_response_id():
+    """The map is keyed by the public response id while the router lookup uses
+    the internal routing key, and entries without a configured name are omitted."""
+    router = MagicMock()
+    router.get_configured_display_name = MagicMock(
+        side_effect=lambda model_name: "Team Sonnet" if model_name == "model_name_team-abc-123_4a6b8" else None
+    )
+
+    assert configured_display_names(
+        entries=[
+            ("team-claude-sonnet", "model_name_team-abc-123_4a6b8"),
+            ("gpt-4o", "gpt-4o"),
+        ],
+        llm_router=router,
+    ) == {"team-claude-sonnet": "Team Sonnet"}
+
+
+def test_configured_display_names_empty_without_router():
+    assert configured_display_names(entries=[("gpt-4o", "gpt-4o")], llm_router=None) == {}
+
+
 @pytest.mark.asyncio
 async def test_retrieve_model_by_public_name_returns_200(monkeypatch):
     """Regression: `GET /v1/models/{public_name}` must NOT 404. The listing
diff --git a/tests/test_litellm/rerank_api/test_main.py b/tests/test_litellm/rerank_api/test_main.py
index 587be59c550..2b6cfeda2c2 100644
--- a/tests/test_litellm/rerank_api/test_main.py
+++ b/tests/test_litellm/rerank_api/test_main.py
@@ -111,6 +111,99 @@ def test_together_rerank_honors_api_base(respx_mock: respx.MockRouter):
     assert mock_route.calls[0].request.headers["authorization"] == "Bearer fake-together-key"
 
 
+DASHSCOPE_404_BODY = {
+    "error": {
+        "message": "The model `does-not-exist` does not exist or you do not have access to it.",
+        "type": "invalid_request_error",
+        "param": None,
+        "code": "model_not_found",
+    },
+    "request_id": "mock-request-id",
+}
+
+
+def test_rerank_error_names_provider_and_keeps_body(respx_mock: respx.MockRouter, monkeypatch):
+    """Regression for the rerank error path mapping with the unresolved provider param:
+    a provider 404 surfaced as 'None - ' instead of naming the provider and its error body."""
+    monkeypatch.delenv("DASHSCOPE_API_BASE", raising=False)
+    monkeypatch.delenv("DASHSCOPE_API_BASE_RERANK", raising=False)
+
+    mock_route = respx_mock.post("https://dashscope.example/v1/reranks")
+    mock_route.return_value = httpx.Response(404, json=DASHSCOPE_404_BODY)
+
+    with pytest.raises(litellm.NotFoundError) as exc_info:
+        litellm.rerank(
+            model="dashscope/does-not-exist",
+            query=MARKER_QUERY,
+            documents=[MARKER_DOC],
+            api_key="fake-dashscope-key",
+            api_base="https://dashscope.example/v1",
+        )
+
+    assert mock_route.called
+    assert "DashscopeException" in str(exc_info.value)
+    assert "does not exist or you do not have access to it" in str(exc_info.value)
+    assert "None - " not in str(exc_info.value)
+
+
+@pytest.mark.asyncio
+async def test_arerank_error_is_mapped_to_litellm_exception(respx_mock: respx.MockRouter, monkeypatch):
+    """Regression for arerank's bare re-raise: provider errors escaped as raw
+    provider exception classes instead of the mapped litellm exception contract."""
+    monkeypatch.delenv("DASHSCOPE_API_BASE", raising=False)
+    monkeypatch.delenv("DASHSCOPE_API_BASE_RERANK", raising=False)
+    monkeypatch.setenv("DISABLE_AIOHTTP_TRANSPORT", "True")
+
+    mock_route = respx_mock.post("https://dashscope.example/v1/reranks")
+    mock_route.return_value = httpx.Response(404, json=DASHSCOPE_404_BODY)
+
+    with pytest.raises(litellm.NotFoundError) as exc_info:
+        await litellm.arerank(
+            model="dashscope/does-not-exist",
+            query=MARKER_QUERY,
+            documents=[MARKER_DOC],
+            api_key="fake-dashscope-key",
+            api_base="https://dashscope.example/v1",
+        )
+
+    assert mock_route.called
+    assert "DashscopeException" in str(exc_info.value)
+    assert "does not exist or you do not have access to it" in str(exc_info.value)
+    assert "None - " not in str(exc_info.value)
+
+
+@pytest.mark.asyncio
+async def test_arerank_declared_authenticating_provider_skips_resolution(monkeypatch):
+    """Regression for the event-loop hazard in arerank's provider pre-resolution:
+    get_llm_provider runs the blocking OAuth device flow for github_copilot/chatgpt,
+    so arerank must adopt the declared provider instead of resolving it, while the
+    except path still maps with that declared provider."""
+    from litellm.llms.base_llm.chat.transformation import BaseLLMException
+
+    resolution_calls = []
+
+    def record_resolution(*args, **kwargs):
+        resolution_calls.append((args, kwargs))
+        return "gpt-4o", "github_copilot", None, None
+
+    def rerank_raises_provider_error(*args, **kwargs):
+        raise BaseLLMException(status_code=401, message='{"error":"bad key"}')
+
+    monkeypatch.setattr(litellm, "get_llm_provider", record_resolution)
+    monkeypatch.setattr("litellm.rerank_api.main.rerank", rerank_raises_provider_error)
+
+    with pytest.raises(litellm.AuthenticationError) as exc_info:
+        await litellm.arerank(
+            model="github_copilot/gpt-4o",
+            query=MARKER_QUERY,
+            documents=[MARKER_DOC],
+        )
+
+    assert resolution_calls == []
+    assert "Github_copilotException" in str(exc_info.value)
+    assert "None - " not in str(exc_info.value)
+
+
 @pytest.mark.asyncio
 async def test_together_rerank_async_honors_env_api_base(respx_mock: respx.MockRouter, monkeypatch):
     """Regression: TOGETHER_AI_API_BASE was honored by chat but ignored by rerank."""
diff --git a/tests/test_litellm/responses/test_streaming_iterator.py b/tests/test_litellm/responses/test_streaming_iterator.py
index 677faf7f655..9edcaaef034 100644
--- a/tests/test_litellm/responses/test_streaming_iterator.py
+++ b/tests/test_litellm/responses/test_streaming_iterator.py
@@ -326,3 +326,55 @@ def test_run_post_success_hooks_does_not_report_generation_time_as_overhead():
 
     assert iterator.completed_response._hidden_params["_response_ms"] == 10000.0
     assert "litellm_overhead_time_ms" not in iterator.completed_response._hidden_params
+
+
+def _responses_api_response_with_usage() -> ResponsesAPIResponse:
+    from litellm.types.llms.openai import ResponseAPIUsage
+
+    return ResponsesAPIResponse(
+        id="resp_lit6427",
+        created_at=int(datetime(2025, 1, 1).timestamp()),
+        status="completed",
+        model="mantle-claude",
+        object="response",
+        output=[],
+        usage=ResponseAPIUsage(input_tokens=20, output_tokens=60, total_tokens=80),
+    )
+
+
+def test_stamp_responses_usage_cost_stamps_computed_cost():
+    from litellm.responses.streaming_iterator import _stamp_responses_usage_cost
+
+    response = _responses_api_response_with_usage()
+    logging_obj = Mock(spec=LiteLLMLoggingObj)
+    logging_obj._response_cost_calculator.return_value = 0.000704
+
+    _stamp_responses_usage_cost(response, logging_obj)
+
+    assert getattr(response.usage, "cost", None) == pytest.approx(0.000704)
+    logging_obj._response_cost_calculator.assert_called_once_with(result=response)
+
+
+def test_stamp_responses_usage_cost_keeps_provider_reported_cost():
+    from litellm.responses.streaming_iterator import _stamp_responses_usage_cost
+
+    response = _responses_api_response_with_usage()
+    setattr(response.usage, "cost", 0.5)
+    logging_obj = Mock(spec=LiteLLMLoggingObj)
+
+    _stamp_responses_usage_cost(response, logging_obj)
+
+    assert getattr(response.usage, "cost", None) == pytest.approx(0.5)
+    logging_obj._response_cost_calculator.assert_not_called()
+
+
+def test_stamp_responses_usage_cost_survives_calculator_failure():
+    from litellm.responses.streaming_iterator import _stamp_responses_usage_cost
+
+    response = _responses_api_response_with_usage()
+    logging_obj = Mock(spec=LiteLLMLoggingObj)
+    logging_obj._response_cost_calculator.side_effect = RuntimeError("cost map unavailable")
+
+    _stamp_responses_usage_cost(response, logging_obj)
+
+    assert getattr(response.usage, "cost", None) is None
diff --git a/tests/test_litellm/test_bedrock_usgov_pricing.py b/tests/test_litellm/test_bedrock_usgov_pricing.py
index 6b3312b5cc4..f7d95ecda01 100644
--- a/tests/test_litellm/test_bedrock_usgov_pricing.py
+++ b/tests/test_litellm/test_bedrock_usgov_pricing.py
@@ -26,9 +26,7 @@ import pytest
 
 @pytest.fixture(scope="module")
 def model_data():
-    json_path = os.path.join(
-        os.path.dirname(__file__), "../../model_prices_and_context_window.json"
-    )
+    json_path = os.path.join(os.path.dirname(__file__), "../../model_prices_and_context_window.json")
     with open(json_path) as f:
         return json.load(f)
 
@@ -51,21 +49,14 @@ def test_usgov_sonnet_4_5_pricing(model_data, model_key):
     info = model_data[model_key]
 
     assert info["input_cost_per_token"] == 3.6e-06, (
-        f"{model_key}: input_cost_per_token should be $3.60/MTok "
-        f"(got {info['input_cost_per_token']})"
+        f"{model_key}: input_cost_per_token should be $3.60/MTok (got {info['input_cost_per_token']})"
     )
-    assert (
-        info["output_cost_per_token"] == 1.8e-05
-    ), f"{model_key}: output_cost_per_token should be $18.00/MTok"
-    assert (
-        info["cache_creation_input_token_cost"] == 4.5e-06
-    ), f"{model_key}: 5m cache write should be $4.50/MTok"
-    assert (
-        info["cache_creation_input_token_cost_above_1hr"] == 7.2e-06
-    ), f"{model_key}: 1h cache write should be $7.20/MTok"
-    assert (
-        info["cache_read_input_token_cost"] == 3.6e-07
-    ), f"{model_key}: cache read should be $0.36/MTok"
+    assert info["output_cost_per_token"] == 1.8e-05, f"{model_key}: output_cost_per_token should be $18.00/MTok"
+    assert info["cache_creation_input_token_cost"] == 4.5e-06, f"{model_key}: 5m cache write should be $4.50/MTok"
+    assert info["cache_creation_input_token_cost_above_1hr"] == 7.2e-06, (
+        f"{model_key}: 1h cache write should be $7.20/MTok"
+    )
+    assert info["cache_read_input_token_cost"] == 3.6e-07, f"{model_key}: cache read should be $0.36/MTok"
 
 
 def test_usgov_carries_20_percent_premium_over_global(model_data):
@@ -84,9 +75,7 @@ def test_usgov_carries_20_percent_premium_over_global(model_data):
         "cache_read_input_token_cost",
     ):
         ratio = usgov_info[field] / global_info[field]
-        assert (
-            abs(ratio - 1.2) < 1e-9
-        ), f"{field}: us-gov / global ratio is {ratio}, expected 1.2"
+        assert abs(ratio - 1.2) < 1e-9, f"{field}: us-gov / global ratio is {ratio}, expected 1.2"
 
 
 # The us-gov.anthropic.* cross-region inference profile is the only us-gov
@@ -112,9 +101,7 @@ def test_usgov_cross_region_above_200k_carries_gov_premium(model_data, field, ex
     """
     info = model_data[USGOV_CROSS_REGION_KEY]
     assert field in info, f"{USGOV_CROSS_REGION_KEY}: missing field {field}"
-    assert (
-        info[field] == expected
-    ), f"{USGOV_CROSS_REGION_KEY}: {field} should be {expected} (got {info[field]})"
+    assert info[field] == expected, f"{USGOV_CROSS_REGION_KEY}: {field} should be {expected} (got {info[field]})"
 
 
 def test_usgov_cross_region_above_200k_ratio_to_global(model_data):
@@ -127,6 +114,176 @@ def test_usgov_cross_region_above_200k_ratio_to_global(model_data):
     usgov_info = model_data[USGOV_CROSS_REGION_KEY]
     for field in EXPECTED_USGOV_ABOVE_200K:
         ratio = usgov_info[field] / global_info[field]
-        assert (
-            abs(ratio - 1.2) < 1e-9
-        ), f"{field}: us-gov / global ratio is {ratio}, expected 1.2"
+        assert abs(ratio - 1.2) < 1e-9, f"{field}: us-gov / global ratio is {ratio}, expected 1.2"
+
+
+CLAUDE_GOV_EXPECTED = {
+    "anthropic.claude-sonnet-5": {
+        "input_cost_per_token": 2.4e-06,
+        "output_cost_per_token": 1.2e-05,
+        "cache_creation_input_token_cost": 3e-06,
+        "cache_creation_input_token_cost_above_1hr": 4.8e-06,
+        "cache_read_input_token_cost": 2.4e-07,
+    },
+    "anthropic.claude-opus-4-8": {
+        "input_cost_per_token": 6e-06,
+        "output_cost_per_token": 3e-05,
+        "cache_creation_input_token_cost": 7.5e-06,
+        "cache_creation_input_token_cost_above_1hr": 1.2e-05,
+        "cache_read_input_token_cost": 6e-07,
+    },
+}
+
+
+USGOV_CLAUDE_KEY_TEMPLATES = {
+    "bedrock/us-gov-east-1/{base_key}": "bedrock",
+    "bedrock/us-gov-west-1/{base_key}": "bedrock",
+    "us-gov.{base_key}": "bedrock_converse",
+}
+
+
+@pytest.mark.parametrize("base_key", CLAUDE_GOV_EXPECTED)
+@pytest.mark.parametrize("key_template,expected_provider", USGOV_CLAUDE_KEY_TEMPLATES.items())
+def test_usgov_claude_sonnet5_opus48_pricing(model_data, key_template, expected_provider, base_key):
+    """Sonnet 5 and Opus 4.8 gov entries, both in-region keys and the us-gov.
+    geo inference profile the model cards list for GovCloud, must match the
+    rates AWS publishes on the Bedrock pricing page (1.2x global).
+    """
+    gov_key = key_template.format(base_key=base_key)
+    assert gov_key in model_data, f"Missing model entry: {gov_key}"
+    info = model_data[gov_key]
+    assert info["litellm_provider"] == expected_provider
+    for field, expected in CLAUDE_GOV_EXPECTED[base_key].items():
+        assert info[field] == expected, f"{gov_key}: {field} should be {expected} (got {info[field]})"
+        ratio = info[field] / model_data[base_key][field]
+        assert abs(ratio - 1.2) < 1e-9, f"{gov_key}: {field} gov/global ratio is {ratio}, expected 1.2"
+
+
+CONVERSE_GOV_EXPECTED = {
+    "nvidia.nemotron-nano-3-30b": (7.2e-08, 2.88e-07),
+    "nvidia.nemotron-nano-12b-v2": (2.4e-07, 7.2e-07),
+    "nvidia.nemotron-super-3-120b": (1.8e-07, 7.8e-07),
+    "openai.gpt-oss-20b-1:0": (8.4e-08, 3.6e-07),
+    "openai.gpt-oss-120b-1:0": (1.8e-07, 7.2e-07),
+}
+
+
+@pytest.mark.parametrize("base_key", CONVERSE_GOV_EXPECTED)
+@pytest.mark.parametrize("region", ["us-gov-east-1", "us-gov-west-1"])
+def test_usgov_converse_model_pricing(model_data, region, base_key):
+    """Nemotron and gpt-oss gov entries must match the AWS Bedrock offer file,
+    which prices both GovCloud regions identically at 1.2x commercial.
+    """
+    gov_key = f"bedrock/{region}/{base_key}"
+    assert gov_key in model_data, f"Missing model entry: {gov_key}"
+    info = model_data[gov_key]
+    expected_input, expected_output = CONVERSE_GOV_EXPECTED[base_key]
+    assert info["input_cost_per_token"] == expected_input
+    assert info["output_cost_per_token"] == expected_output
+    assert info["litellm_provider"] == "bedrock"
+    base = model_data[base_key]
+    assert abs(info["input_cost_per_token"] / base["input_cost_per_token"] - 1.2) < 1e-9
+    assert abs(info["output_cost_per_token"] / base["output_cost_per_token"] - 1.2) < 1e-9
+
+
+def test_usgov_west_llama3_8b_output_price_fixed(model_data):
+    """The us-gov-west-1 llama3-8b entry carried the 70B output rate ($2.65/MTok);
+    the AWS Bedrock offer file prices output at $0.60/MTok. AWS lists the model
+    in us-gov-west-1 only, so there is no east entry to check.
+    """
+    info = model_data["bedrock/us-gov-west-1/meta.llama3-8b-instruct-v1:0"]
+    assert info["input_cost_per_token"] == 3e-07
+    assert info["output_cost_per_token"] == 6e-07
+
+
+MANTLE_GOV_TIERED_EXPECTED = {
+    "openai.gpt-5.6-luna": {
+        "input_cost_per_token": 2.64e-07,
+        "input_cost_per_token_above_272k_tokens": 5.28e-07,
+        "cache_creation_input_token_cost": 3.3e-07,
+        "cache_creation_input_token_cost_above_272k_tokens": 6.6e-07,
+        "cache_read_input_token_cost": 2.64e-08,
+        "cache_read_input_token_cost_above_272k_tokens": 5.28e-08,
+        "output_cost_per_token": 1.584e-06,
+        "output_cost_per_token_above_272k_tokens": 2.376e-06,
+    },
+    "openai.gpt-5.6-terra": {
+        "input_cost_per_token": 2.64e-06,
+        "input_cost_per_token_above_272k_tokens": 5.28e-06,
+        "cache_creation_input_token_cost": 3.3e-06,
+        "cache_creation_input_token_cost_above_272k_tokens": 6.6e-06,
+        "cache_read_input_token_cost": 2.64e-07,
+        "cache_read_input_token_cost_above_272k_tokens": 5.28e-07,
+        "output_cost_per_token": 1.584e-05,
+        "output_cost_per_token_above_272k_tokens": 2.376e-05,
+    },
+}
+
+
+@pytest.mark.parametrize("model", MANTLE_GOV_TIERED_EXPECTED)
+def test_usgov_west_mantle_terra_luna_pricing(model_data, model):
+    """Terra and Luna carry 1.2x commercial across every tier in the
+    us-gov-west-1 offer file; the us-gov-east-1 offer file has no SKUs for them.
+    """
+    gov_key = f"bedrock_mantle/us-gov-west-1/{model}"
+    assert gov_key in model_data, f"Missing model entry: {gov_key}"
+    info = model_data[gov_key]
+    for field, expected in MANTLE_GOV_TIERED_EXPECTED[model].items():
+        assert info[field] == expected, f"{gov_key}: {field} should be {expected} (got {info[field]})"
+    assert info["litellm_provider"] == "bedrock_mantle"
+    assert f"bedrock_mantle/us-gov-east-1/{model}" not in model_data
+
+
+@pytest.mark.parametrize("region", ["us-gov-east-1", "us-gov-west-1"])
+def test_usgov_mantle_gpt_5_4_pricing_has_no_long_context_tier(model_data, region):
+    """gpt-5.4 gov rates come from the offer file, which publishes only the
+    standard tier in GovCloud: no long-context SKUs exist there, unlike commercial.
+    """
+    gov_key = f"bedrock_mantle/{region}/openai.gpt-5.4"
+    assert gov_key in model_data, f"Missing model entry: {gov_key}"
+    info = model_data[gov_key]
+    assert info["input_cost_per_token"] == 3.3e-06
+    assert info["cache_read_input_token_cost"] == 3.3e-07
+    assert info["output_cost_per_token"] == 1.98e-05
+    assert not any(field.endswith("_above_272k_tokens") for field in info)
+
+
+def test_usgov_mantle_grok_4_3_west_only(model_data):
+    """grok-4.3 is priced in the us-gov-west-1 offer file only; the east offer
+    file carries grok-4.6 instead.
+    """
+    info = model_data["bedrock_mantle/us-gov-west-1/xai.grok-4.3"]
+    assert info["input_cost_per_token"] == 1.5e-06
+    assert info["output_cost_per_token"] == 3e-06
+    assert info["cache_read_input_token_cost"] == 2.4e-07
+    assert "bedrock_mantle/us-gov-east-1/xai.grok-4.3" not in model_data
+
+
+AZURE_GOV_EXPECTED = {
+    "azure/us-gov/gpt-5.1": {
+        "input_cost_per_token": 1.71875e-06,
+        "cache_read_input_token_cost": 1.71875e-07,
+        "output_cost_per_token": 1.375e-05,
+    },
+    "azure/us-gov/o3-mini": {
+        "input_cost_per_token": 1.513e-06,
+        "cache_read_input_token_cost": 7.57e-07,
+        "output_cost_per_token": 6.05e-06,
+    },
+    "azure/us-gov/text-embedding-3-large": {"input_cost_per_token": 1.63e-07},
+    "azure/us-gov/text-embedding-3-small": {"input_cost_per_token": 2.5e-08},
+}
+
+
+@pytest.mark.parametrize("gov_key", AZURE_GOV_EXPECTED)
+def test_azure_usgov_pricing(model_data, gov_key):
+    """Azure Government meters from the Azure retail prices API
+    (usgovvirginia/usgovarizona, serviceName 'Foundry Models'). No Government
+    retirement schedule is published, so these entries carry no deprecation_date.
+    """
+    assert gov_key in model_data, f"Missing model entry: {gov_key}"
+    info = model_data[gov_key]
+    for field, expected in AZURE_GOV_EXPECTED[gov_key].items():
+        assert info[field] == expected, f"{gov_key}: {field} should be {expected} (got {info[field]})"
+    assert info["litellm_provider"] == "azure"
+    assert "deprecation_date" not in info
diff --git a/tests/test_litellm/test_cloudflare_workers_ai_model_metadata.py b/tests/test_litellm/test_cloudflare_workers_ai_model_metadata.py
index 9ca4515239a..e33bcfb8378 100644
--- a/tests/test_litellm/test_cloudflare_workers_ai_model_metadata.py
+++ b/tests/test_litellm/test_cloudflare_workers_ai_model_metadata.py
@@ -75,6 +75,22 @@ def test_additional_current_models_are_present():
         assert entry["output_cost_per_token"] > 0
 
 
+@pytest.mark.parametrize(
+    "key, published_price_per_audio_minute",
+    [
+        ("cloudflare/@cf/openai/whisper", 0.00045),
+        ("cloudflare/@cf/openai/whisper-large-v3-turbo", 0.00051),
+    ],
+)
+def test_whisper_transcription_pricing_is_stored_per_second(key, published_price_per_audio_minute):
+    entry = litellm.model_cost[key]
+    assert entry["litellm_provider"] == "cloudflare"
+    assert entry["mode"] == "audio_transcription"
+    assert entry["supported_endpoints"] == ["/v1/audio/transcriptions"]
+    assert entry["output_cost_per_second"] == 0.0
+    assert entry["input_cost_per_second"] == pytest.approx(published_price_per_audio_minute / 60)
+
+
 def test_root_and_backup_have_identical_cloudflare_keys():
     if not os.path.exists(ROOT_MAP):
         pytest.skip("root cost map only ships in source checkouts")
diff --git a/tests/test_litellm/test_main.py b/tests/test_litellm/test_main.py
index 8cf878d05d9..7c2b9d0be05 100644
--- a/tests/test_litellm/test_main.py
+++ b/tests/test_litellm/test_main.py
@@ -3150,8 +3150,8 @@ def _stream_builder_logging_obj() -> LiteLLMLogging:
     return logging_obj
 
 
-def test_stream_chunk_builder_reports_streaming_usage_cost_when_enabled(monkeypatch: pytest.MonkeyPatch):
-    monkeypatch.setattr(litellm, "include_cost_in_streaming_usage", True)
+def test_stream_chunk_builder_stamps_streaming_usage_cost_by_default(monkeypatch: pytest.MonkeyPatch):
+    monkeypatch.setattr(litellm, "include_cost_in_streaming_usage", False)
     chunks: Final = [
         _stream_builder_text_chunk("gpt-4o", "Hello "),
         _stream_builder_text_chunk("gpt-4o", "world.", finish_reason="stop"),
@@ -3168,11 +3168,45 @@ def test_stream_chunk_builder_reports_streaming_usage_cost_when_enabled(monkeypa
     assert response._hidden_params["response_cost"] == pytest.approx(usage_cost)
 
 
-def test_stream_chunk_builder_defers_cost_to_logging_obj_when_usage_cost_absent(monkeypatch: pytest.MonkeyPatch):
-    monkeypatch.setattr(litellm, "include_cost_in_streaming_usage", False)
+def test_stream_chunk_builder_skips_stamp_when_cost_is_unpriceable():
+    import time as time_module
+
+    from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging
+
+    logging_obj: Final = LiteLLMLogging(
+        model="us.anthropic.claude-opus-5",
+        messages=[{"role": "user", "content": "hi"}],
+        stream=True,
+        call_type="completion",
+        start_time=time_module.time(),
+        litellm_call_id="stream-builder-alias-unpriceable",
+        function_id="1",
+    )
+    logging_obj.model_call_details["custom_llm_provider"] = "bedrock"
+    logging_obj.optional_params = {}
+    usage_chunk: Final = _stream_builder_text_chunk("bedrock-claude-opus-5", "")
+    usage_chunk.usage = Usage(prompt_tokens=40, completion_tokens=5, total_tokens=45)
+    chunks: Final = [
+        _stream_builder_text_chunk("bedrock-claude-opus-5", "Hello ", finish_reason="stop"),
+        usage_chunk,
+    ]
+
+    response: Final = litellm.stream_chunk_builder(
+        chunks=chunks, messages=[{"role": "user", "content": "hi"}], logging_obj=logging_obj
+    )
+
+    assert response is not None
+    assert getattr(response.usage, "cost", None) is None
+    assert response._hidden_params.get("response_cost") is None
+
+
+def test_stream_chunk_builder_keeps_provider_reported_usage_cost():
+    usage_chunk: Final = _stream_builder_text_chunk("gpt-4o", "")
+    usage_chunk.usage = Usage(prompt_tokens=10, completion_tokens=5, total_tokens=15, cost=0.5)
     chunks: Final = [
         _stream_builder_text_chunk("gpt-4o", "Hello "),
         _stream_builder_text_chunk("gpt-4o", "world.", finish_reason="stop"),
+        usage_chunk,
     ]
 
     response: Final = litellm.stream_chunk_builder(
@@ -3180,4 +3214,26 @@ def test_stream_chunk_builder_defers_cost_to_logging_obj_when_usage_cost_absent(
     )
 
     assert response is not None
-    assert response._hidden_params.get("response_cost") is None
+    assert getattr(response.usage, "cost", None) == pytest.approx(0.5)
+    assert response._hidden_params["response_cost"] == pytest.approx(0.5)
+
+
+def test_stream_chunk_builder_prices_alias_from_openai_sdk_usage_chunk():
+    from openai.types.completion_usage import CompletionUsage
+
+    usage_chunk: Final = _stream_builder_text_chunk("mantle-claude", "")
+    usage_chunk.usage = CompletionUsage(prompt_tokens=20, completion_tokens=60, total_tokens=80, cost=0.000704)
+    assert type(usage_chunk.usage) is CompletionUsage
+    chunks: Final = [
+        _stream_builder_text_chunk("mantle-claude", "Hello "),
+        _stream_builder_text_chunk("mantle-claude", "world.", finish_reason="stop"),
+        usage_chunk,
+    ]
+
+    response: Final = litellm.stream_chunk_builder(chunks=chunks, messages=[{"role": "user", "content": "hi"}])
+
+    assert response is not None
+    assert response.usage.prompt_tokens == 20
+    assert response.usage.completion_tokens == 60
+    assert getattr(response.usage, "cost", None) == pytest.approx(0.000704)
+    assert response._hidden_params["response_cost"] == pytest.approx(0.000704)
diff --git a/tests/test_litellm/test_openai_service_tier_long_context_pricing.py b/tests/test_litellm/test_openai_service_tier_long_context_pricing.py
new file mode 100644
index 00000000000..c0860a5b55f
--- /dev/null
+++ b/tests/test_litellm/test_openai_service_tier_long_context_pricing.py
@@ -0,0 +1,156 @@
+import json
+from functools import lru_cache
+from pathlib import Path
+
+import pytest
+
+import litellm
+
+REPO_ROOT = Path(__file__).parents[2]
+MAIN_PATH = REPO_ROOT / "model_prices_and_context_window.json"
+BACKUP_PATH = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json"
+
+FLEX_LONG_CONTEXT = {
+    "gpt-5.4": {
+        "input_cost_per_token_above_272k_tokens_flex": 2.5e-06,
+        "output_cost_per_token_above_272k_tokens_flex": 1.125e-05,
+        "cache_read_input_token_cost_above_272k_tokens_flex": 2.5e-07,
+    },
+    "gpt-5.4-pro": {
+        "input_cost_per_token_above_272k_tokens_flex": 3e-05,
+        "output_cost_per_token_above_272k_tokens_flex": 0.000135,
+    },
+    "gpt-5.5": {
+        "input_cost_per_token_above_272k_tokens_flex": 5e-06,
+        "output_cost_per_token_above_272k_tokens_flex": 2.25e-05,
+        "cache_read_input_token_cost_above_272k_tokens_flex": 5e-07,
+    },
+}
+
+PRIORITY_LONG_CONTEXT = {
+    "gpt-5.6": {
+        "input_cost_per_token_above_272k_tokens_priority": 1.6e-05,
+        "output_cost_per_token_above_272k_tokens_priority": 6e-05,
+        "cache_read_input_token_cost_above_272k_tokens_priority": 1.6e-06,
+        "cache_creation_input_token_cost_above_272k_tokens_priority": 2e-05,
+    },
+    "gpt-5.6-sol": {
+        "input_cost_per_token_above_272k_tokens_priority": 1.6e-05,
+        "output_cost_per_token_above_272k_tokens_priority": 6e-05,
+        "cache_read_input_token_cost_above_272k_tokens_priority": 1.6e-06,
+        "cache_creation_input_token_cost_above_272k_tokens_priority": 2e-05,
+    },
+    "gpt-5.6-terra": {
+        "input_cost_per_token_above_272k_tokens_priority": 8e-06,
+        "output_cost_per_token_above_272k_tokens_priority": 3.6e-05,
+        "cache_read_input_token_cost_above_272k_tokens_priority": 8e-07,
+        "cache_creation_input_token_cost_above_272k_tokens_priority": 1e-05,
+    },
+    "gpt-5.6-luna": {
+        "input_cost_per_token_above_272k_tokens_priority": 8e-07,
+        "output_cost_per_token_above_272k_tokens_priority": 3.6e-06,
+        "cache_read_input_token_cost_above_272k_tokens_priority": 8e-08,
+        "cache_creation_input_token_cost_above_272k_tokens_priority": 1e-06,
+    },
+}
+
+EXPECTED = {**FLEX_LONG_CONTEXT, **PRIORITY_LONG_CONTEXT}
+
+NO_PUBLISHED_PRIORITY_LONG_CONTEXT = ("gpt-5.4", "gpt-5.5")
+
+
+@pytest.fixture(autouse=True)
+def _local_model_cost_map(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
+    monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))
+
+
+@lru_cache(maxsize=2)
+def _load(path: Path) -> dict[str, dict[str, object]]:
+    with open(path) as f:
+        return json.load(f)
+
+
+@pytest.mark.parametrize("path", [MAIN_PATH, BACKUP_PATH], ids=["main", "backup"])
+@pytest.mark.parametrize("model", sorted(EXPECTED))
+def test_service_tier_long_context_rates_are_published(model: str, path: Path) -> None:
+    """Each tier must carry its own above-272K rates, in both price files."""
+    info = _load(path).get(model)
+    assert info is not None, f"{model} not found in {path.name}"
+    for key, expected in EXPECTED[model].items():
+        assert info.get(key) == pytest.approx(expected), f"{model}.{key} is {info.get(key)!r}, expected {expected!r}"
+
+
+@pytest.mark.parametrize("model", sorted(EXPECTED))
+def test_tier_long_context_rate_is_half_or_double_the_standard(model: str) -> None:
+    """Flex is half the standard long-context rate; priority is double it."""
+    info = _load(MAIN_PATH)[model]
+    tier = "flex" if model in FLEX_LONG_CONTEXT else "priority"
+    ratio = 0.5 if tier == "flex" else 2.0
+    for base in ("input_cost_per_token", "output_cost_per_token"):
+        standard = info[f"{base}_above_272k_tokens"]
+        tiered = info[f"{base}_above_272k_tokens_{tier}"]
+        assert tiered == pytest.approx(standard * ratio), (
+            f"{model}.{base}_above_272k_tokens_{tier} is {tiered!r}, "
+            f"expected {ratio}x the standard long-context rate {standard!r}"
+        )
+
+
+@pytest.mark.parametrize("model", NO_PUBLISHED_PRIORITY_LONG_CONTEXT)
+def test_no_priority_long_context_rates_where_openai_publishes_none(model: str) -> None:
+    """Guard against back-filling a rate OpenAI does not publish."""
+    info = _load(MAIN_PATH)[model]
+    assert "input_cost_per_token_above_272k_tokens_priority" not in info
+
+
+LONG_CONTEXT_PROMPT_TOKENS = 300_000
+COMPLETION_TOKENS = 1_000
+
+TIERED_COST_CASES = [
+    ("gpt-5.4", "flex", 2.5e-06, 1.125e-05),
+    ("gpt-5.4-pro", "flex", 3e-05, 0.000135),
+    ("gpt-5.5", "flex", 5e-06, 2.25e-05),
+    ("gpt-5.6", "priority", 1.6e-05, 6e-05),
+    ("gpt-5.6-sol", "priority", 1.6e-05, 6e-05),
+    ("gpt-5.6-terra", "priority", 8e-06, 3.6e-05),
+    ("gpt-5.6-luna", "priority", 8e-07, 3.6e-06),
+]
+
+
+@pytest.mark.parametrize("model,tier,input_rate,output_rate", TIERED_COST_CASES)
+def test_cost_per_token_bills_long_context_at_the_tier_rate(
+    model: str, tier: str, input_rate: float, output_rate: float
+) -> None:
+    """A prompt over 272K on flex or priority must bill at that tier's long-context rate."""
+    input_cost, output_cost = litellm.cost_per_token(
+        model=model,
+        prompt_tokens=LONG_CONTEXT_PROMPT_TOKENS,
+        completion_tokens=COMPLETION_TOKENS,
+        service_tier=tier,
+    )
+    assert input_cost == pytest.approx(LONG_CONTEXT_PROMPT_TOKENS * input_rate)
+    assert output_cost == pytest.approx(COMPLETION_TOKENS * output_rate)
+
+
+@pytest.mark.parametrize("model,tier,input_rate,output_rate", TIERED_COST_CASES)
+def test_cost_per_token_tier_differs_from_the_standard_long_context_cost(
+    model: str, tier: str, input_rate: float, output_rate: float
+) -> None:
+    """Flex halves the standard long-context bill and priority doubles it."""
+    ratio = 0.5 if tier == "flex" else 2.0
+    standard = sum(
+        litellm.cost_per_token(
+            model=model,
+            prompt_tokens=LONG_CONTEXT_PROMPT_TOKENS,
+            completion_tokens=COMPLETION_TOKENS,
+        )
+    )
+    tiered = sum(
+        litellm.cost_per_token(
+            model=model,
+            prompt_tokens=LONG_CONTEXT_PROMPT_TOKENS,
+            completion_tokens=COMPLETION_TOKENS,
+            service_tier=tier,
+        )
+    )
+    assert tiered == pytest.approx(standard * ratio)
diff --git a/tests/test_litellm/test_router.py b/tests/test_litellm/test_router.py
index 84f6344be35..d6328118f57 100644
--- a/tests/test_litellm/test_router.py
+++ b/tests/test_litellm/test_router.py
@@ -7271,6 +7271,71 @@ def test_get_configured_token_limits_coerces_numeric_strings():
     assert router.get_configured_token_limits("quoted-limits-model") == (32000, 8000)
 
 
+def test_get_configured_display_name_reads_deployment_model_info():
+    router = litellm.Router(
+        model_list=[
+            {
+                "model_name": "Kimi K3-claude-compatible",
+                "litellm_params": {"model": "openai/some-unmapped-model"},
+                "model_info": {"display_name": "Kimi K3"},
+            }
+        ]
+    )
+
+    assert router.get_configured_display_name("Kimi K3-claude-compatible") == "Kimi K3"
+
+
+def test_get_configured_display_name_returns_none_for_unset_or_unknown():
+    router = litellm.Router(
+        model_list=[
+            {
+                "model_name": "no-display-model",
+                "litellm_params": {"model": "openai/some-unmapped-model"},
+            }
+        ]
+    )
+
+    assert router.get_configured_display_name("no-display-model") is None
+    assert router.get_configured_display_name("not-a-real-model") is None
+
+
+def test_get_configured_display_name_skips_wildcard_pattern_matching():
+    router = litellm.Router(
+        model_list=[
+            {
+                "model_name": "bedrock/*",
+                "litellm_params": {"model": "bedrock/*"},
+                "model_info": {"display_name": "Bedrock"},
+            }
+        ]
+    )
+
+    with patch.object(
+        router.pattern_router, "route", side_effect=AssertionError("pattern route called")
+    ):
+        assert (
+            router.get_configured_display_name("bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0")
+            is None
+        )
+
+
+def test_get_configured_display_name_treats_malformed_values_as_absent():
+    malformed = ["", "   ", 12345, ["Kimi K3"], {"name": "Kimi K3"}, True]
+    router = litellm.Router(
+        model_list=[
+            {
+                "model_name": f"bad-display-{i}",
+                "litellm_params": {"model": "openai/some-unmapped-model"},
+                "model_info": {"display_name": bad},
+            }
+            for i, bad in enumerate(malformed)
+        ]
+    )
+
+    for i in range(len(malformed)):
+        assert router.get_configured_display_name(f"bad-display-{i}") is None
+
+
 @pytest.mark.asyncio
 async def test_acreate_batch_disable_fallbacks_surfaces_owning_provider_error():
     router = litellm.Router(
diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py
index 521e91daded..0790b41c349 100644
--- a/tests/test_litellm/test_utils.py
+++ b/tests/test_litellm/test_utils.py
@@ -4655,6 +4655,7 @@ GEMINI_4096_CACHE_MIN_MODELS: Final = tuple(
         "gemini-3.5-flash",
         "gemini-3.6-flash",
         "gemini-3.7-flash",
+        "gemini-3.8-flash",
         "gemini-3.1-pro-preview",
         "gemini-3.1-pro-preview-customtools",
     )
diff --git a/whitelisted_bedrock_models.txt b/whitelisted_bedrock_models.txt
index 7e20081988d..8753d7c3c77 100644
--- a/whitelisted_bedrock_models.txt
+++ b/whitelisted_bedrock_models.txt
@@ -217,3 +217,17 @@ bedrock/us-east-1/zai.glm-5
 bedrock/us-west-2/zai.glm-5
 bedrock/us-gov-east-1/anthropic.claude-haiku-4-5-20251001-v1:0
 bedrock/us-gov-west-1/anthropic.claude-haiku-4-5-20251001-v1:0
+bedrock/us-gov-west-1/nvidia.nemotron-nano-3-30b
+bedrock/us-gov-west-1/nvidia.nemotron-nano-12b-v2
+bedrock/us-gov-west-1/nvidia.nemotron-super-3-120b
+bedrock/us-gov-west-1/openai.gpt-oss-20b-1:0
+bedrock/us-gov-west-1/openai.gpt-oss-120b-1:0
+bedrock/us-gov-west-1/anthropic.claude-sonnet-5
+bedrock/us-gov-west-1/anthropic.claude-opus-4-8
+bedrock/us-gov-east-1/nvidia.nemotron-nano-3-30b
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