diff --git a/.circleci/config.yml b/.circleci/config.yml
index d155823c6..c1224159a 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -150,6 +150,7 @@ jobs:
               -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
               -e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
               -e AWS_REGION_NAME=$AWS_REGION_NAME \
+              -e OPENAI_API_KEY=$OPENAI_API_KEY \
               --name my-app \
               -v $(pwd)/proxy_server_config.yaml:/app/config.yaml \
               my-app:latest \
diff --git a/litellm/proxy/utils.py b/litellm/proxy/utils.py
index 905b9424e..84b09d726 100644
--- a/litellm/proxy/utils.py
+++ b/litellm/proxy/utils.py
@@ -1079,7 +1079,7 @@ def get_logging_payload(kwargs, response_obj, start_time, end_time):
     metadata = (
         litellm_params.get("metadata", {}) or {}
     )  # if litellm_params['metadata'] == None
-    call_type = kwargs.get("call_type", "litellm.completion")
+    call_type = kwargs.get("call_type")
     cache_hit = kwargs.get("cache_hit", False)
     usage = response_obj["usage"]
     if type(usage) == litellm.Usage:
@@ -1118,6 +1118,7 @@ def get_logging_payload(kwargs, response_obj, start_time, end_time):
         "completion_tokens": usage.get("completion_tokens", 0),
     }
 
+    verbose_proxy_logger.debug(f"SpendTable: created payload - payload: {payload}\n\n")
     json_fields = [
         field
         for field, field_type in LiteLLM_SpendLogs.__annotations__.items()
diff --git a/litellm/utils.py b/litellm/utils.py
index fe899388f..587a44895 100644
--- a/litellm/utils.py
+++ b/litellm/utils.py
@@ -804,6 +804,7 @@ class Logging:
             "stream": self.stream,
             "user": user,
             "call_type": str(self.call_type),
+            "litellm_call_id": self.litellm_call_id,
             **self.optional_params,
             **additional_params,
         }
@@ -1056,6 +1057,7 @@ class Logging:
                 and (
                     isinstance(result, ModelResponse)
                     or isinstance(result, EmbeddingResponse)
+                    or isinstance(result, ImageResponse)
                 )
                 and self.stream != True
             ):  # handle streaming separately
@@ -1063,11 +1065,24 @@ class Logging:
                     if self.model_call_details.get("cache_hit", False) == True:
                         self.model_call_details["response_cost"] = 0.0
                     else:
-                        self.model_call_details[
-                            "response_cost"
-                        ] = litellm.completion_cost(
-                            completion_response=result,
-                        )
+                        result._hidden_params["optional_params"] = self.optional_params
+                        if (
+                            self.call_type == CallTypes.aimage_generation.value
+                            or self.call_type == CallTypes.image_generation.value
+                        ):
+                            self.model_call_details[
+                                "response_cost"
+                            ] = litellm.completion_cost(
+                                completion_response=result,
+                                model=self.model,
+                                call_type=self.call_type,
+                            )
+                        else:
+                            self.model_call_details[
+                                "response_cost"
+                            ] = litellm.completion_cost(
+                                completion_response=result, call_type=self.call_type
+                            )
                     verbose_logger.debug(
                         f"Model={self.model}; cost={self.model_call_details['response_cost']}"
                     )
@@ -3174,6 +3189,16 @@ def completion_cost(
     messages: List = [],
     completion="",
     total_time=0.0,  # used for replicate, sagemaker
+    call_type: Literal[
+        "completion",
+        "acompletion",
+        "embedding",
+        "aembedding",
+        "atext_completion",
+        "text_completion",
+        "image_generation",
+        "aimage_generation",
+    ] = "completion",
     ### REGION ###
     custom_llm_provider=None,
     region_name=None,  # used for bedrock pricing
@@ -3232,6 +3257,19 @@ def completion_cost(
                 region_name = completion_response._hidden_params.get(
                     "region_name", region_name
                 )
+                size = completion_response._hidden_params.get(
+                    "optional_params", {}
+                ).get(
+                    "size", "1024-x-1024"
+                )  # openai default
+                quality = completion_response._hidden_params.get(
+                    "optional_params", {}
+                ).get(
+                    "quality", "standard"
+                )  # openai default
+                n = completion_response._hidden_params.get("optional_params", {}).get(
+                    "n", 1
+                )  # openai default
         else:
             if len(messages) > 0:
                 prompt_tokens = token_counter(model=model, messages=messages)
@@ -3243,7 +3281,10 @@ def completion_cost(
                 f"Model is None and does not exist in passed completion_response. Passed completion_response={completion_response}, model={model}"
             )
 
-        if size is not None and n is not None:
+        if (
+            call_type == CallTypes.image_generation.value
+            or call_type == CallTypes.aimage_generation.value
+        ):
             ### IMAGE GENERATION COST CALCULATION ###
             image_gen_model_name = f"{size}/{model}"
             image_gen_model_name_with_quality = image_gen_model_name
diff --git a/proxy_server_config.yaml b/proxy_server_config.yaml
index 2c123d156..1d499aa7d 100644
--- a/proxy_server_config.yaml
+++ b/proxy_server_config.yaml
@@ -42,6 +42,9 @@ model_list:
       api_version: 2023-06-01-preview
       api_base: https://openai-gpt-4-test-v-1.openai.azure.com/
       api_key: os.environ/AZURE_API_KEY
+  - model_name: openai-dall-e-3
+    litellm_params:
+      model: dall-e-3
 
 litellm_settings:
   drop_params: True
diff --git a/tests/test_keys.py b/tests/test_keys.py
index 9cbcc25e1..6740308ac 100644
--- a/tests/test_keys.py
+++ b/tests/test_keys.py
@@ -14,7 +14,11 @@ import litellm
 
 
 async def generate_key(
-    session, i, budget=None, budget_duration=None, models=["azure-models", "gpt-4"]
+    session,
+    i,
+    budget=None,
+    budget_duration=None,
+    models=["azure-models", "gpt-4", "dall-e-3"],
 ):
     url = "http://0.0.0.0:4000/key/generate"
     headers = {"Authorization": "Bearer sk-1234", "Content-Type": "application/json"}
@@ -129,6 +133,39 @@ async def chat_completion(session, key, model="gpt-4"):
                 pass
 
 
+async def image_generation(session, key, model="dall-e-3"):
+    url = "http://0.0.0.0:4000/v1/images/generations"
+    headers = {
+        "Authorization": f"Bearer {key}",
+        "Content-Type": "application/json",
+    }
+    data = {
+        "model": model,
+        "prompt": "A cute baby sea otter",
+    }
+
+    for i in range(3):
+        try:
+            async with session.post(url, headers=headers, json=data) as response:
+                status = response.status
+                response_text = await response.text()
+
+                print(response_text)
+                print()
+
+                if status != 200:
+                    raise Exception(
+                        f"Request did not return a 200 status code: {status}. Response: {response_text}"
+                    )
+
+                return await response.json()
+        except Exception as e:
+            if "Request did not return a 200 status code" in str(e):
+                raise e
+            else:
+                pass
+
+
 async def chat_completion_streaming(session, key, model="gpt-4"):
     client = AsyncOpenAI(api_key=key, base_url="http://0.0.0.0:4000")
     messages = [
@@ -357,6 +394,40 @@ async def test_key_info_spend_values_streaming():
         assert rounded_response_cost == rounded_key_info_spend
 
 
+@pytest.mark.asyncio
+async def test_key_info_spend_values_image_generation():
+    """
+    Test to ensure spend is correctly calculated
+    - create key
+    - make image gen call
+    - assert cost is expected value
+    """
+
+    async def retry_request(func, *args, _max_attempts=5, **kwargs):
+        for attempt in range(_max_attempts):
+            try:
+                return await func(*args, **kwargs)
+            except aiohttp.client_exceptions.ClientOSError as e:
+                if attempt + 1 == _max_attempts:
+                    raise  # re-raise the last ClientOSError if all attempts failed
+                print(f"Attempt {attempt+1} failed, retrying...")
+
+    async with aiohttp.ClientSession(
+        timeout=aiohttp.ClientTimeout(total=600)
+    ) as session:
+        ## Test Spend Update ##
+        # completion
+        key_gen = await generate_key(session=session, i=0)
+        key = key_gen["key"]
+        response = await image_generation(session=session, key=key)
+        await asyncio.sleep(5)
+        key_info = await retry_request(
+            get_key_info, session=session, get_key=key, call_key=key
+        )
+        spend = key_info["info"]["spend"]
+        assert spend > 0
+
+
 @pytest.mark.asyncio
 async def test_key_with_budgets():
     """