diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py
index 5b11b224bb..d6a671bf60 100644
--- a/litellm/litellm_core_utils/prompt_templates/factory.py
+++ b/litellm/litellm_core_utils/prompt_templates/factory.py
@@ -88,7 +88,10 @@ def map_system_message_pt(messages: list) -> list:
                     next_role == "user" or next_role == "assistant"
                 ):  # Next message is a user or assistant message
                     # Merge system prompt into the next message
-                    next_m["content"] = m["content"] + " " + next_m["content"]
+                    if type(next_m['content']) is list:
+                        next_m['content'].insert(0, {"type": "text", "text": m['content']})
+                    else:    
+                        next_m["content"] = m["content"] + " " + next_m["content"]
                 elif next_role == "system":  # Next message is a system message
                     # Append a user message instead of the system message
                     new_message = {"role": "user", "content": m["content"]}
diff --git a/tests/llm_translation/test_optional_params.py b/tests/llm_translation/test_optional_params.py
index e207e367e4..c11f8c9532 100644
--- a/tests/llm_translation/test_optional_params.py
+++ b/tests/llm_translation/test_optional_params.py
@@ -53,6 +53,29 @@ def test_supports_system_message():
     assert isinstance(response, litellm.ModelResponse)
 
 
+def test_supports_system_message_multipart():
+    """
+    Check if litellm.completion(...,supports_system_message=False) works with multipart user message 
+    """
+    messages = [
+        ChatCompletionSystemMessageParam(role="system", content="Listen here!"),
+        ChatCompletionUserMessageParam(role="user", content=[{"type": "text", "text": "Hello there!"}]),
+    ]
+
+    new_messages = map_system_message_pt(messages=messages)
+
+    assert len(new_messages) == 1
+    assert new_messages[0]["role"] == "user"
+
+    ## confirm you can make a openai call with this param
+
+    response = litellm.completion(
+        model="gpt-3.5-turbo", messages=new_messages, supports_system_message=False
+    )
+
+    assert isinstance(response, litellm.ModelResponse)
+
+
 @pytest.mark.parametrize(
     "stop_sequence, expected_count", [("\n", 0), (["\n"], 0), (["finish_reason"], 1)]
 )