diff --git a/llm_core/.env.example b/llm_core/.env.example
index 34e279aa..823e9a30 100644
--- a/llm_core/.env.example
+++ b/llm_core/.env.example
@@ -12,7 +12,9 @@ DATABASE_URL=sqlite:///../data/data.sqlite
 
 # Default model to use
 # See below for options, available models are also logged on startup
-LLM_DEFAULT_MODEL="azure_openai_gpt-35-turbo"
+LLM_DEFAULT_MODEL="azure_openai_gpt-4o"
+LLM_DEFAULT_MODEL_COST_PER_MILLION_INPUT_TOKEN=5
+LLM_DEFAULT_MODEL_COST_PER_MILLION_OUTPUT_TOKEN=15
 
 # Enable LLM-as-a-judge approach 0 = disabled, 1 = enabled
 LLM_ENABLE_LLM_AS_A_JUDGE=1
diff --git a/llm_core/llm_core/models/callbacks.py b/llm_core/llm_core/models/callbacks.py
index d33b0a6b..dc8d7b32 100644
--- a/llm_core/llm_core/models/callbacks.py
+++ b/llm_core/llm_core/models/callbacks.py
@@ -1,3 +1,5 @@
+import os
+
 from langchain.callbacks.base import BaseCallbackHandler
 from langchain_core.outputs import  LLMResult
 from langchain_core.messages.ai import UsageMetadata
@@ -7,10 +9,13 @@
 
 class UsageHandler(BaseCallbackHandler):
     def on_llm_end(self, response: LLMResult, **kwargs) -> None:
+        cost_per_million_input_tokens = float(os.environ.get("LLM_DEFAULT_MODEL_COST_PER_MILLION_INPUT_TOKEN", 0.0))
+        cost_per_million_output_tokens = float(os.environ.get("LLM_DEFAULT_MODEL_COST_PER_MILLION_OUTPUT_TOKEN", 0.0))
+
         meta = get_meta()
         
-        total_usage = meta.get("total_usage", {"input_tokens": 0, "output_tokens": 0, "total_tokens": 0})
-        llm_calls = meta.get("llm_calls", [])
+        total_usage = meta.get("totalUsage", {"numInputTokens": 0, "numOutputTokens": 0, "numTotalTokens": 0, "cost": 0 })
+        llm_calls = meta.get("llmRequests", [])
 
         for generations in response.generations:
             for generation in generations:
@@ -18,16 +23,21 @@ def on_llm_end(self, response: LLMResult, **kwargs) -> None:
                 generation_usage: UsageMetadata = message["usage_metadata"]
                 model_name = message["response_metadata"].get("model_name", None)
 
-                total_usage["input_tokens"] += generation_usage["input_tokens"]
-                total_usage["output_tokens"] += generation_usage["output_tokens"]
-                total_usage["total_tokens"] += generation_usage["total_tokens"]
+                total_usage["numInputTokens"] += generation_usage["input_tokens"]
+                total_usage["numOutputTokens"] += generation_usage["output_tokens"]
+                total_usage["numTotalTokens"] += generation_usage["total_tokens"]
+
+                total_usage["cost"] += int(generation_usage["input_tokens"]) * cost_per_million_output_tokens / 1_000_000
+                total_usage["cost"] += int(generation_usage["output_tokens"]) * cost_per_million_output_tokens / 1_000_000
 
                 llm_calls.append({
-                    "model_name": model_name,
-                    "input_tokens": generation_usage["input_tokens"],
-                    "output_tokens": generation_usage["output_tokens"],
-                    "total_tokens": generation_usage["total_tokens"],
+                    "model": model_name,
+                    "costPerMillionInputToken": cost_per_million_input_tokens,
+                    "costPerMillionOutputToken": cost_per_million_output_tokens,
+                    "numInputTokens": generation_usage["input_tokens"],
+                    "numOutputTokens": generation_usage["output_tokens"],
+                    "numTotalTokens": generation_usage["total_tokens"],
                 })
 
-        emit_meta("total_usage", total_usage)
-        emit_meta("llm_calls", llm_calls)
+        emit_meta("totalUsage", total_usage)
+        emit_meta("llmRequests", llm_calls)
diff --git a/modules/modeling/module_modeling_llm/.env.example b/modules/modeling/module_modeling_llm/.env.example
index 68b0577a..ebeeb7bc 100644
--- a/modules/modeling/module_modeling_llm/.env.example
+++ b/modules/modeling/module_modeling_llm/.env.example
@@ -13,6 +13,8 @@ DATABASE_URL=sqlite:///../data/data.sqlite
 # Default model to use
 # See below for options, available models are also logged on startup
 LLM_DEFAULT_MODEL="azure_openai_gpt-4o"
+LLM_DEFAULT_MODEL_COST_PER_MILLION_INPUT_TOKEN=5
+LLM_DEFAULT_MODEL_COST_PER_MILLION_OUTPUT_TOKEN=15
 
 # Enable LLM-as-a-judge approach 0 = disabled, 1 = enabled
 LLM_ENABLE_LLM_AS_A_JUDGE=1
diff --git a/modules/programming/module_programming_llm/.env.example b/modules/programming/module_programming_llm/.env.example
index 2b6d9a88..75387424 100644
--- a/modules/programming/module_programming_llm/.env.example
+++ b/modules/programming/module_programming_llm/.env.example
@@ -12,7 +12,9 @@ DATABASE_URL=sqlite:///../data/data.sqlite
 
 # Default model to use
 # See below for options, available models are also logged on startup
-LLM_DEFAULT_MODEL="azure_openai_gpt-35"
+LLM_DEFAULT_MODEL="azure_openai_gpt-4o"
+LLM_DEFAULT_MODEL_COST_PER_MILLION_INPUT_TOKEN=5
+LLM_DEFAULT_MODEL_COST_PER_MILLION_OUTPUT_TOKEN=15
 
 # Standard OpenAI (Non-Azure) [leave blank if not used]
 # Model names prefixed with `openai_` followed by the model name, e.g. `openai_text-davinci-003`
diff --git a/modules/text/module_text_llm/.env.example b/modules/text/module_text_llm/.env.example
index 34e279aa..823e9a30 100644
--- a/modules/text/module_text_llm/.env.example
+++ b/modules/text/module_text_llm/.env.example
@@ -12,7 +12,9 @@ DATABASE_URL=sqlite:///../data/data.sqlite
 
 # Default model to use
 # See below for options, available models are also logged on startup
-LLM_DEFAULT_MODEL="azure_openai_gpt-35-turbo"
+LLM_DEFAULT_MODEL="azure_openai_gpt-4o"
+LLM_DEFAULT_MODEL_COST_PER_MILLION_INPUT_TOKEN=5
+LLM_DEFAULT_MODEL_COST_PER_MILLION_OUTPUT_TOKEN=15
 
 # Enable LLM-as-a-judge approach 0 = disabled, 1 = enabled
 LLM_ENABLE_LLM_AS_A_JUDGE=1