diff --git a/litellm/utils.py b/litellm/utils.py
index 9b6b9f54cd..ddfc39ff70 100644
--- a/litellm/utils.py
+++ b/litellm/utils.py
@@ -2780,7 +2780,9 @@ def token_counter(
     return num_tokens
 
 
-def cost_per_token(model="", prompt_tokens=0, completion_tokens=0):
+def cost_per_token(
+    model="", prompt_tokens=0, completion_tokens=0, custom_llm_provider=None
+):
     """
     Calculates the cost per token for a given model, prompt tokens, and completion tokens.
 
@@ -2796,6 +2798,7 @@ def cost_per_token(model="", prompt_tokens=0, completion_tokens=0):
     prompt_tokens_cost_usd_dollar = 0
     completion_tokens_cost_usd_dollar = 0
     model_cost_ref = litellm.model_cost
+    model_with_provider = custom_llm_provider + "/" + model
     # see this https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models
     print_verbose(f"Looking up model={model} in model_cost_map")
 
@@ -2807,6 +2810,16 @@ def cost_per_token(model="", prompt_tokens=0, completion_tokens=0):
             model_cost_ref[model]["output_cost_per_token"] * completion_tokens
         )
         return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
+    elif model_with_provider in model_cost_ref:
+        print_verbose(f"Looking up model={model_with_provider} in model_cost_map")
+        prompt_tokens_cost_usd_dollar = (
+            model_cost_ref[model_with_provider]["input_cost_per_token"] * prompt_tokens
+        )
+        completion_tokens_cost_usd_dollar = (
+            model_cost_ref[model_with_provider]["output_cost_per_token"]
+            * completion_tokens
+        )
+        return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
     elif "ft:gpt-3.5-turbo" in model:
         print_verbose(f"Cost Tracking: {model} is an OpenAI FinteTuned LLM")
         # fuzzy match ft:gpt-3.5-turbo:abcd-id-cool-litellm
@@ -2890,6 +2903,7 @@ def completion_cost(
         # Handle Inputs to completion_cost
         prompt_tokens = 0
         completion_tokens = 0
+        custom_llm_provider = None
         if completion_response is not None:
             # get input/output tokens from completion_response
             prompt_tokens = completion_response.get("usage", {}).get("prompt_tokens", 0)
@@ -2899,6 +2913,12 @@ def completion_cost(
             model = (
                 model or completion_response["model"]
             )  # check if user passed an override for model, if it's none check completion_response['model']
+            if completion_response is not None and hasattr(
+                completion_response, "_hidden_params"
+            ):
+                custom_llm_provider = completion_response._hidden_params.get(
+                    "custom_llm_provider", ""
+                )
         else:
             if len(messages) > 0:
                 prompt_tokens = token_counter(model=model, messages=messages)
@@ -2926,6 +2946,7 @@ def completion_cost(
             model=model,
             prompt_tokens=prompt_tokens,
             completion_tokens=completion_tokens,
+            custom_llm_provider=custom_llm_provider,
         )
         return prompt_tokens_cost_usd_dollar + completion_tokens_cost_usd_dollar
     except Exception as e: