diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py
index 4d89bbd9f2..1817a47262 100644
--- a/litellm/proxy/_types.py
+++ b/litellm/proxy/_types.py
@@ -1013,6 +1013,6 @@ class TokenCountRequest(LiteLLMBase):
 
 class TokenCountResponse(LiteLLMBase):
     total_tokens: int
-    model: str
-    base_model: str
+    request_model: str
+    model_used: str
     tokenizer_type: str
diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py
index 690a0cb0b6..4cab8bce20 100644
--- a/litellm/proxy/proxy_server.py
+++ b/litellm/proxy/proxy_server.py
@@ -4779,33 +4779,38 @@ async def token_counter(request: TokenCountRequest):
 
     prompt = request.prompt
     messages = request.messages
+    if prompt is None and messages is None:
+        raise HTTPException(
+            status_code=400, detail="prompt or messages must be provided"
+        )
 
+    deployment = None
+    litellm_model_name = None
     if llm_router is not None:
         # get 1 deployment corresponding to the model
         for _model in llm_router.model_list:
             if _model["model_name"] == request.model:
                 deployment = _model
                 break
+    if deployment is not None:
+        litellm_model_name = deployment.get("litellm_params", {}).get("model")
+        # remove the custom_llm_provider_prefix in the litellm_model_name
+        if "/" in litellm_model_name:
+            litellm_model_name = litellm_model_name.split("/", 1)[1]
 
-    litellm_model_name = deployment.get("litellm_params", {}).get("model")
-    # remove the custom_llm_provider_prefix in the litellm_model_name
-    if "/" in litellm_model_name:
-        litellm_model_name = litellm_model_name.split("/", 1)[1]
-
-    if prompt is None and messages is None:
-        raise HTTPException(
-            status_code=400, detail="prompt or messages must be provided"
-        )
+    model_to_use = (
+        litellm_model_name or request.model
+    )  # use litellm model name, if it's not avalable then fallback to request.model
     total_tokens, tokenizer_used = token_counter(
-        model=litellm_model_name,
+        model=model_to_use,
         text=prompt,
         messages=messages,
         return_tokenizer_used=True,
     )
     return TokenCountResponse(
         total_tokens=total_tokens,
-        model=request.model,
-        base_model=litellm_model_name,
+        request_model=request.model,
+        model_used=model_to_use,
         tokenizer_type=tokenizer_used,
     )
 
diff --git a/litellm/tests/test_proxy_token_counter.py b/litellm/tests/test_proxy_token_counter.py
new file mode 100644
index 0000000000..859ddf5c74
--- /dev/null
+++ b/litellm/tests/test_proxy_token_counter.py
@@ -0,0 +1,138 @@
+# Test the following scenarios:
+# 1. Generate a Key, and use it to make a call
+
+
+import sys, os
+import traceback
+from dotenv import load_dotenv
+from fastapi import Request
+from datetime import datetime
+
+load_dotenv()
+import os, io, time
+
+# this file is to test litellm/proxy
+
+sys.path.insert(
+    0, os.path.abspath("../..")
+)  # Adds the parent directory to the system path
+import pytest, logging, asyncio
+import litellm, asyncio
+from litellm.proxy.proxy_server import token_counter
+from litellm.proxy.utils import PrismaClient, ProxyLogging, hash_token, update_spend
+from litellm._logging import verbose_proxy_logger
+
+verbose_proxy_logger.setLevel(level=logging.DEBUG)
+
+from litellm.proxy._types import TokenCountRequest, TokenCountResponse
+
+
+from litellm import Router
+
+
+@pytest.mark.asyncio
+async def test_vLLM_token_counting():
+    """
+    Test Token counter for vLLM models
+    - User passes model="special-alias"
+    - token_counter should infer that special_alias -> maps to wolfram/miquliz-120b-v2.0
+    -> token counter should use hugging face tokenizer
+    """
+
+    llm_router = Router(
+        model_list=[
+            {
+                "model_name": "special-alias",
+                "litellm_params": {
+                    "model": "openai/wolfram/miquliz-120b-v2.0",
+                    "api_base": "https://exampleopenaiendpoint-production.up.railway.app/",
+                },
+            }
+        ]
+    )
+
+    setattr(litellm.proxy.proxy_server, "llm_router", llm_router)
+
+    response = await token_counter(
+        request=TokenCountRequest(
+            model="special-alias",
+            messages=[{"role": "user", "content": "hello"}],
+        )
+    )
+
+    print("response: ", response)
+
+    assert (
+        response.tokenizer_type == "huggingface_tokenizer"
+    )  # SHOULD use the hugging face tokenizer
+    assert response.model_used == "wolfram/miquliz-120b-v2.0"
+
+
+@pytest.mark.asyncio
+async def test_token_counting_model_not_in_model_list():
+    """
+    Test Token counter - when a model is not in model_list
+    -> should use the default OpenAI tokenizer
+    """
+
+    llm_router = Router(
+        model_list=[
+            {
+                "model_name": "gpt-4",
+                "litellm_params": {
+                    "model": "gpt-4",
+                },
+            }
+        ]
+    )
+
+    setattr(litellm.proxy.proxy_server, "llm_router", llm_router)
+
+    response = await token_counter(
+        request=TokenCountRequest(
+            model="special-alias",
+            messages=[{"role": "user", "content": "hello"}],
+        )
+    )
+
+    print("response: ", response)
+
+    assert (
+        response.tokenizer_type == "openai_tokenizer"
+    )  # SHOULD use the OpenAI tokenizer
+    assert response.model_used == "special-alias"
+
+
+@pytest.mark.asyncio
+async def test_gpt_token_counting():
+    """
+    Test Token counter
+    -> should work for gpt-4
+    """
+
+    llm_router = Router(
+        model_list=[
+            {
+                "model_name": "gpt-4",
+                "litellm_params": {
+                    "model": "gpt-4",
+                },
+            }
+        ]
+    )
+
+    setattr(litellm.proxy.proxy_server, "llm_router", llm_router)
+
+    response = await token_counter(
+        request=TokenCountRequest(
+            model="gpt-4",
+            messages=[{"role": "user", "content": "hello"}],
+        )
+    )
+
+    print("response: ", response)
+
+    assert (
+        response.tokenizer_type == "openai_tokenizer"
+    )  # SHOULD use the OpenAI tokenizer
+    assert response.request_model == "gpt-4"