diff --git a/litellm/__init__.py b/litellm/__init__.py
index 0f48fb4cf4..bc8fb9c135 100644
--- a/litellm/__init__.py
+++ b/litellm/__init__.py
@@ -269,6 +269,7 @@ blocked_user_list: Optional[Union[str, List]] = None
 banned_keywords_list: Optional[Union[str, List]] = None
 llm_guard_mode: Literal["all", "key-specific", "request-specific"] = "all"
 guardrail_name_config_map: Dict[str, GuardrailItem] = {}
+include_cost_in_streaming_usage: bool = False
 ### PROMPTS ###
 from litellm.types.prompts.init_prompts import PromptSpec
 
diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py
index 2e9e6770a1..3721851a38 100644
--- a/litellm/litellm_core_utils/streaming_handler.py
+++ b/litellm/litellm_core_utils/streaming_handler.py
@@ -1584,7 +1584,9 @@ class CustomStreamWrapper:
         except StopIteration:
             if self.sent_last_chunk is True:
                 complete_streaming_response = litellm.stream_chunk_builder(
-                    chunks=self.chunks, messages=self.messages
+                    chunks=self.chunks,
+                    messages=self.messages,
+                    logging_obj=self.logging_obj,
                 )
 
                 response = self.model_response_creator()
@@ -1768,7 +1770,9 @@ class CustomStreamWrapper:
             if self.sent_last_chunk is True:
                 # log the final chunk with accurate streaming values
                 complete_streaming_response = litellm.stream_chunk_builder(
-                    chunks=self.chunks, messages=self.messages
+                    chunks=self.chunks,
+                    messages=self.messages,
+                    logging_obj=self.logging_obj,
                 )
                 response = self.model_response_creator()
                 if complete_streaming_response is not None:
diff --git a/litellm/main.py b/litellm/main.py
index 108abbde84..7762e178f6 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -5704,7 +5704,11 @@ def stream_chunk_builder_text_completion(
 
 
 def stream_chunk_builder(  # noqa: PLR0915
-    chunks: list, messages: Optional[list] = None, start_time=None, end_time=None
+    chunks: list,
+    messages: Optional[list] = None,
+    start_time=None,
+    end_time=None,
+    logging_obj: Optional[Logging] = None,
 ) -> Optional[Union[ModelResponse, TextCompletionResponse]]:
     try:
         if chunks is None:
@@ -5829,6 +5833,12 @@ def stream_chunk_builder(  # noqa: PLR0915
 
         setattr(response, "usage", usage)
 
+        # 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)
+            )
+
         return response
     except Exception as e:
         verbose_logger.exception(
diff --git a/litellm/types/utils.py b/litellm/types/utils.py
index f8df5781de..6606b0e0b8 100644
--- a/litellm/types/utils.py
+++ b/litellm/types/utils.py
@@ -887,6 +887,7 @@ class Usage(CompletionUsage):
     )  # hidden param for prompt caching. Might change, once openai introduces their equivalent.
 
     server_tool_use: Optional[ServerToolUse] = None
+    cost: Optional[float] = None
 
     completion_tokens_details: Optional[CompletionTokensDetailsWrapper] = None
     """Breakdown of tokens used in a completion."""
@@ -907,6 +908,7 @@ class Usage(CompletionUsage):
             Union[CompletionTokensDetailsWrapper, dict]
         ] = None,
         server_tool_use: Optional[ServerToolUse] = None,
+        cost: Optional[float] = None,
         **params,
     ):
         # handle reasoning_tokens
@@ -978,6 +980,11 @@ class Usage(CompletionUsage):
         else:  # maintain openai compatibility in usage object if possible
             del self.server_tool_use
 
+        if cost is not None:
+            self.cost = cost
+        else:
+            del self.cost
+
         ## ANTHROPIC MAPPING ##
         if "cache_creation_input_tokens" in params and isinstance(
             params["cache_creation_input_tokens"], int
diff --git a/tests/local_testing/test_streaming.py b/tests/local_testing/test_streaming.py
index 823b635028..445dad57a5 100644
--- a/tests/local_testing/test_streaming.py
+++ b/tests/local_testing/test_streaming.py
@@ -3948,3 +3948,45 @@ def test_is_delta_empty():
             audio=None,
         )
     )
+
+
+def test_streaming_with_cost_calculation():
+    from litellm.types.utils import Usage
+    from typing import Optional
+
+    litellm.include_cost_in_streaming_usage = True
+
+    ## Test 1: check if usage object can handle 'cost' field
+    usage_object = Usage(
+        prompt_tokens=100,
+        completion_tokens=100,
+        total_tokens=200,
+        cost=1.0,
+    )
+    assert usage_object.cost is not None
+
+    print(f"usage_object: {usage_object}")
+
+    ## Test 2: check if usage object has 'cost' field when streaming
+
+    response = litellm.completion(
+        model="gpt-4o-mini",
+        messages=[{"role": "user", "content": "What is the capital of France?"}],
+        stream=True,
+        stream_options={"include_usage": True},
+    )
+
+    usage_object: Optional[Usage] = None
+    for chunk in response:
+        _usage_obj = getattr(chunk, "usage", None)
+        if _usage_obj is not None:
+            usage_object = _usage_obj
+            break
+
+    assert usage_object is not None
+    assert usage_object.total_tokens is not None
+    assert usage_object.total_tokens > 0
+    assert usage_object.prompt_tokens is not None
+    assert usage_object.prompt_tokens > 0
+    assert usage_object.cost is not None
+    assert usage_object.cost > 0