diff --git a/ai_ta_backend/cost_tracking.py b/ai_ta_backend/cost_tracking.py
index 399108a1..558756b5 100644
--- a/ai_ta_backend/cost_tracking.py
+++ b/ai_ta_backend/cost_tracking.py
@@ -2,7 +2,7 @@
 import pandas as pd
 import os
 from typing import List
-
+import tiktoken
 
 def get_cost(course_name: str)-> List:
     """
@@ -22,13 +22,69 @@ def get_cost(course_name: str)-> List:
     convo_df = df['convo']
 
     total_queries = 0
+    list_of_prompt_costs = []
+    list_of_prompt_token_lengths = []
+
+
 
     for row in convo_df:
+        
         messages = row['messages']
+        model_name = row['model']['id']
+        prompt = row['prompt']
         for message in messages:
-            if message['role'] == 'user' and message['content'] != '':
+            content = message['content']
+            if message['role'] == 'user': 
+                # prompt cost
                 total_queries += 1
+                query_cost, query_tokens = get_tokens_and_cost(content, prompt, model_name)
+                list_of_prompt_costs.append(query_cost)
+                list_of_prompt_token_lengths.append(query_tokens)
+            else:
+                # completion cost
+                pass
+
+    total_course_cost = round(sum(list_of_prompt_costs), 4)
+    avg_course_cost = round(total_course_cost/total_queries, 4)
 
     print("Total queries: ", total_queries)
+    print("Total tokens: ", round(sum(list_of_prompt_token_lengths), 4))
+    print("Total cost: ", total_course_cost)
+    print("Average cost: ", avg_course_cost)
+
+    return [total_queries, total_course_cost, avg_course_cost]
+
+def get_tokens_and_cost(query: str, std_prompt: str, model_name: str):
+    """
+    Takes an input query and returns the cost.
+    1. Cost of encoding query + prompt
+    """
+    # initialize tokenizer
+    tokenizer = tiktoken.encoding_for_model("gpt-3.5-turbo")
+    if model_name.startswith('gpt-4'):
+        # 8k context
+        prompt_cost_per_token: float = 0.03/1000
+        completion_cost_per_token: float = 0.06/1000
+
+        # 32k context
+        # prompt_cost_per_token: float = 0.06/1000
+        # completion_cost_per_token: float = 0.12/1000
+
+    elif model_name.startswith('gpt-3.5-turbo'):
+        # 4k context
+        prompt_cost_per_token: float = 0.0015/1000
+        completion_cost_per_token: float = 0.002/1000
+
+        # 16k context
+        # prompt_cost_per_token: float = 0.003/1000
+        # completion_cost_per_token: float = 0.004/1000
+
+    else:
+        print(f"No idea of cost! Pricing not supported for model: `{model_name}`")
+
+    # get cost of encoding query + prompt
+    input_str = std_prompt + " " + query
+    num_of_input_tokens = len(tokenizer.encode(input_str))
+    prompt_cost = round(float(prompt_cost_per_token * num_of_input_tokens),4)
 
-    return [total_queries]
\ No newline at end of file
+    return prompt_cost, num_of_input_tokens