diff --git a/litellm/main.py b/litellm/main.py
index c131838970..b9acd6e4af 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -1131,6 +1131,42 @@ def batch_completion(
         results = [future.result() for future in completions]
     return results
 
+# send one request to multiple models
+# return as soon as one of the llms responds
+def batch_completion_models(*args, **kwargs):
+    """
+    Send a request to multiple language models concurrently and return the response
+    as soon as one of the models responds.
+
+    Args:
+        *args: Variable-length positional arguments passed to the completion function.
+        **kwargs: Additional keyword arguments:
+            - models (str or list of str): The language models to send requests to.
+            - Other keyword arguments to be passed to the completion function.
+
+    Returns:
+        str or None: The response from one of the language models, or None if no response is received.
+
+    Note:
+        This function utilizes a ThreadPoolExecutor to parallelize requests to multiple models.
+        It sends requests concurrently and returns the response from the first model that responds.
+    """
+    import concurrent
+    if "model" in kwargs:
+        kwargs.pop("model")
+    if "models" in kwargs:
+        models = kwargs["models"]
+        kwargs.pop("models")
+        with concurrent.futures.ThreadPoolExecutor(max_workers=len(models)) as executor:
+            futures = [executor.submit(completion, *args, model=model, **kwargs) for model in models]
+
+            for future in concurrent.futures.as_completed(futures):
+                if future.result() is not None:
+                    return future.result()
+
+    return None  # If no response is received from any model
+
+
 ### EMBEDDING ENDPOINTS ####################
 @client
 @timeout(  # type: ignore