diff --git a/litellm/main.py b/litellm/main.py
index 07d7be2ba..ecb6edd0d 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -487,14 +487,17 @@ def mock_completion(
             if kwargs.get("acompletion", False) == True:
                 return CustomStreamWrapper(
                     completion_stream=async_mock_completion_streaming_obj(
-                        model_response, mock_response=mock_response, model=model
+                        model_response, mock_response=mock_response, model=model, n=n
                     ),
                     model=model,
                     custom_llm_provider="openai",
                     logging_obj=logging,
                 )
             response = mock_completion_streaming_obj(
-                model_response, mock_response=mock_response, model=model
+                model_response,
+                mock_response=mock_response,
+                model=model,
+                n=n,
             )
             return response
         if n is None:
diff --git a/litellm/tests/test_mock_request.py b/litellm/tests/test_mock_request.py
index 6b58c94b2..48b054371 100644
--- a/litellm/tests/test_mock_request.py
+++ b/litellm/tests/test_mock_request.py
@@ -73,3 +73,22 @@ def test_mock_request_n_greater_than_1():
 
     except:
         traceback.print_exc()
+
+
+@pytest.mark.asyncio()
+async def test_async_mock_streaming_request_n_greater_than_1():
+    generator = await litellm.acompletion(
+        messages=[{"role": "user", "content": "Why is LiteLLM amazing?"}],
+        mock_response="LiteLLM is awesome",
+        stream=True,
+        model="gpt-3.5-turbo",
+        n=5,
+    )
+    complete_response = ""
+    async for chunk in generator:
+        print(chunk)
+        # complete_response += chunk["choices"][0]["delta"]["content"] or ""
+
+    # assert (
+    #     complete_response == "LiteLLM is awesome"
+    # ), f"Unexpected response got {complete_response}"
diff --git a/litellm/utils.py b/litellm/utils.py
index 1bc8bf771..cae86c6f8 100644
--- a/litellm/utils.py
+++ b/litellm/utils.py
@@ -9708,18 +9708,45 @@ class TextCompletionStreamWrapper:
             raise StopAsyncIteration
 
 
-def mock_completion_streaming_obj(model_response, mock_response, model):
+def mock_completion_streaming_obj(
+    model_response, mock_response, model, n: Optional[int] = None
+):
     for i in range(0, len(mock_response), 3):
-        completion_obj = {"role": "assistant", "content": mock_response[i : i + 3]}
-        model_response.choices[0].delta = completion_obj
+        completion_obj = Delta(role="assistant", content=mock_response[i : i + 3])
+        if n is None:
+            model_response.choices[0].delta = completion_obj
+        else:
+            _all_choices = []
+            for j in range(n):
+                _streaming_choice = litellm.utils.StreamingChoices(
+                    index=j,
+                    delta=litellm.utils.Delta(
+                        role="assistant", content=mock_response[i : i + 3]
+                    ),
+                )
+                _all_choices.append(_streaming_choice)
+            model_response.choices = _all_choices
         yield model_response
 
 
-async def async_mock_completion_streaming_obj(model_response, mock_response, model):
+async def async_mock_completion_streaming_obj(
+    model_response, mock_response, model, n: Optional[int] = None
+):
     for i in range(0, len(mock_response), 3):
         completion_obj = Delta(role="assistant", content=mock_response[i : i + 3])
-        model_response.choices[0].delta = completion_obj
-        model_response.choices[0].finish_reason = "stop"
+        if n is None:
+            model_response.choices[0].delta = completion_obj
+        else:
+            _all_choices = []
+            for j in range(n):
+                _streaming_choice = litellm.utils.StreamingChoices(
+                    index=j,
+                    delta=litellm.utils.Delta(
+                        role="assistant", content=mock_response[i : i + 3]
+                    ),
+                )
+                _all_choices.append(_streaming_choice)
+            model_response.choices = _all_choices
         yield model_response