diff --git a/litellm/llms/prompt_templates/factory.py b/litellm/llms/prompt_templates/factory.py
index 79fc59069..baf2c3a2a 100644
--- a/litellm/llms/prompt_templates/factory.py
+++ b/litellm/llms/prompt_templates/factory.py
@@ -499,7 +499,11 @@ def convert_to_anthropic_image_obj(openai_image_url: str):
     # Infer image format from the URL
     image_format = openai_image_url.split("data:image/")[1].split(";base64,")[0]
 
-    return {"type": "base64", "media_type": image_format, "data": base64_data}
+    return {
+        "type": "base64",
+        "media_type": f"image/{image_format}",
+        "data": base64_data,
+    }
 
 
 def anthropic_messages_pt(messages: list):
@@ -515,10 +519,35 @@ def anthropic_messages_pt(messages: list):
     last_assistant_message_idx: Optional[int] = None
     # reformat messages to ensure user/assistant are alternating, if there's either 2 consecutive 'user' messages or 2 consecutive 'assistant' message, add a blank 'user' or 'assistant' message to ensure compatibility
     new_messages = []
+    if len(messages) == 1:
+        # check if the message is a user message
+        if messages[0]["role"] == "assistant":
+            new_messages.append({"role": "user", "content": ""})
+
+        # check if content is a list (vision)
+        if isinstance(messages[0]["content"], list):  # vision input
+            new_content = []
+            for m in messages[0]["content"]:
+                if m.get("type", "") == "image_url":
+                    new_content.append(
+                        {
+                            "type": "image",
+                            "source": convert_to_anthropic_image_obj(
+                                m["image_url"]["url"]
+                            ),
+                        }
+                    )
+                elif m.get("type", "") == "text":
+                    new_content.append({"type": "text", "text": m["text"]})
+            new_messages.append({"role": messages[0]["role"], "content": new_content})  # type: ignore
+        else:
+            new_messages.append(messages[0])
+
+        return new_messages
+
     for i in range(len(messages) - 1):  # type: ignore
         if i == 0 and messages[i]["role"] == "assistant":
             new_messages.append({"role": "user", "content": ""})
-
         if isinstance(messages[i]["content"], list):  # vision input
             new_content = []
             for m in messages[i]["content"]:
@@ -546,8 +575,6 @@ def anthropic_messages_pt(messages: list):
         if messages[i]["role"] == "assistant":
             last_assistant_message_idx = i
 
-    new_messages.append(messages[-1])
-
     if last_assistant_message_idx is not None:
         new_messages[last_assistant_message_idx]["content"] = new_messages[
             last_assistant_message_idx
diff --git a/litellm/tests/test_amazing_vertex_completion.py b/litellm/tests/test_amazing_vertex_completion.py
index 35d66907c..8ed15db65 100644
--- a/litellm/tests/test_amazing_vertex_completion.py
+++ b/litellm/tests/test_amazing_vertex_completion.py
@@ -351,7 +351,7 @@ def test_gemini_pro_vision_base64():
         load_vertex_ai_credentials()
         litellm.set_verbose = True
         litellm.num_retries = 3
-        image_path = "cached_logo.jpg"
+        image_path = "../proxy/cached_logo.jpg"
         # Getting the base64 string
         base64_image = encode_image(image_path)
         resp = litellm.completion(
diff --git a/litellm/tests/test_completion.py b/litellm/tests/test_completion.py
index 068ddc78f..1677e04cf 100644
--- a/litellm/tests/test_completion.py
+++ b/litellm/tests/test_completion.py
@@ -159,6 +159,51 @@ def test_completion_claude_3_stream():
         pytest.fail(f"Error occurred: {e}")
 
 
+def encode_image(image_path):
+    import base64
+
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode("utf-8")
+
+
+@pytest.mark.skip(
+    reason="we already test claude-3, this is just another way to pass images"
+)
+def test_completion_claude_3_base64():
+    try:
+        litellm.set_verbose = True
+        litellm.num_retries = 3
+        image_path = "../proxy/cached_logo.jpg"
+        # Getting the base64 string
+        base64_image = encode_image(image_path)
+        resp = litellm.completion(
+            model="anthropic/claude-3-opus-20240229",
+            messages=[
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": "Whats in this image?"},
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": "data:image/jpeg;base64," + base64_image
+                            },
+                        },
+                    ],
+                }
+            ],
+        )
+        print(f"\nResponse: {resp}")
+
+        prompt_tokens = resp.usage.prompt_tokens
+        raise Exception("it worked!")
+    except Exception as e:
+        if "500 Internal error encountered.'" in str(e):
+            pass
+        else:
+            pytest.fail(f"An exception occurred - {str(e)}")
+
+
 def test_completion_mistral_api():
     try:
         litellm.set_verbose = True
diff --git a/litellm/utils.py b/litellm/utils.py
index 69f324589..1aa1d3767 100644
--- a/litellm/utils.py
+++ b/litellm/utils.py
@@ -200,6 +200,10 @@ def map_finish_reason(
         return "content_filter"
     elif finish_reason == "STOP":  # vertex ai
         return "stop"
+    elif finish_reason == "end_turn" or finish_reason == "stop_sequence":  # anthropic
+        return "stop"
+    elif finish_reason == "max_tokens":  # anthropic
+        return "length"
     return finish_reason