diff --git a/litellm/utils.py b/litellm/utils.py
index 65a069906c8..40ab1c42238 100644
--- a/litellm/utils.py
+++ b/litellm/utils.py
@@ -3992,75 +3992,6 @@ def completion_with_config(config: Union[dict, str], **kwargs):
             return completion_with_fallbacks(model=model, messages=messages, fallbacks=fallback_models)
         raise e
 
-
-
-def get_model_split_test(models, completion_call_id):
-    global last_fetched_at
-    try:
-        # make the api call
-        last_fetched_at = time.time()
-        response = requests.post(
-            #http://api.litellm.ai
-            url="http://api.litellm.ai/get_model_split_test", # get the updated dict from table or update the table with the dict
-            headers={"content-type": "application/json"},
-            data=json.dumps({"completion_call_id": completion_call_id, "models": models}),
-        )
-        print_verbose(f"get_model_list response: {response.text}")
-        data = response.json()
-        # update model list
-        split_test_models = data["split_test_models"]
-        model_configs = data.get("model_configs", {})
-        # update environment - if required
-        threading.Thread(target=get_all_keys, args=()).start()
-        return split_test_models, model_configs
-    except:
-        print_verbose(
-            f"[Non-Blocking Error] get_all_keys error - {traceback.format_exc()}"
-        )
-
-
-def completion_with_split_tests(models={}, messages=[], use_client=False, override_client=False, **kwargs):
-    """
-    Example Usage: 
-
-    models =  {
-	    "gpt-4": 0.7, 
-	    "huggingface/wizard-coder": 0.3
-    }
-    messages = [{ "content": "Hello, how are you?","role": "user"}]
-    completion_with_split_tests(models=models, messages=messages)
-    """
-    import random
-    model_configs = {}
-    if use_client and not override_client:
-        if "id" not in kwargs or kwargs["id"] is None:
-            kwargs["id"] = str(uuid.uuid4())
-            #raise ValueError("Please tag this completion call, if you'd like to update it's split test values through the UI. - eg. `completion_with_split_tests(.., id=1234)`.")
-        # get the most recent model split list from server 
-        models, model_configs = get_model_split_test(models=models, completion_call_id=kwargs["id"])
-
-    try:
-        selected_llm = random.choices(list(models.keys()), weights=list(models.values()))[0]
-    except:
-        traceback.print_exc()
-        raise ValueError("""models does not follow the required format - {'model_name': 'split_percentage'}, e.g. {'gpt-4': 0.7, 'huggingface/wizard-coder': 0.3}""")
-    
-    # use dynamic model configs if users set 
-    if model_configs!={}:
-        selected_model_configs = model_configs.get(selected_llm, {})
-        if "prompt" in selected_model_configs: # special case, add this to messages as system prompt
-            messages.append({"role": "system", "content": selected_model_configs["prompt"]})
-            selected_model_configs.pop("prompt")
-        for param_name in selected_model_configs:
-            if param_name == "temperature":
-                kwargs[param_name] = float(selected_model_configs[param_name])
-            elif param_name == "max_tokens":
-                kwargs[param_name] = int(selected_model_configs[param_name])
-            else:
-                kwargs[param_name] = selected_model_configs[param_name]
-
-    return litellm.completion(model=selected_llm, messages=messages, use_client=use_client, **kwargs)
-
 def completion_with_fallbacks(**kwargs):
     nested_kwargs = kwargs.pop("kwargs", {})
     response = None