diff --git a/aide/backend/backend_openai.py b/aide/backend/backend_openai.py
index e751e5e..db76938 100644
--- a/aide/backend/backend_openai.py
+++ b/aide/backend/backend_openai.py
@@ -19,6 +19,23 @@
     openai.InternalServerError,
 )
 
+# (docs) https://platform.openai.com/docs/guides/function-calling/supported-models
+SUPPORTED_FUNCTION_CALL_MODELS = {
+    "gpt-4o",
+    "gpt-4o-2024-08-06",
+    "gpt-4o-2024-05-13",
+    "gpt-4o-mini",
+    "gpt-4o-mini-2024-07-18",
+    "gpt-4-turbo",
+    "gpt-4-turbo-2024-04-09",
+    "gpt-4-turbo-preview",
+    "gpt-4-0125-preview",
+    "gpt-4-1106-preview",
+    "gpt-3.5-turbo",
+    "gpt-3.5-turbo-0125",
+    "gpt-3.5-turbo-1106",
+}
+
 
 @once
 def _setup_openai_client():
@@ -26,21 +43,41 @@ def _setup_openai_client():
     _client = openai.OpenAI(max_retries=0)
 
 
+def is_function_call_supported(model_name: str) -> bool:
+    """Return True if the model supports function calling."""
+    return model_name in SUPPORTED_FUNCTION_CALL_MODELS
+
+
 def query(
     system_message: str | None,
     user_message: str | None,
     func_spec: FunctionSpec | None = None,
     **model_kwargs,
 ) -> tuple[OutputType, float, int, int, dict]:
+    """
+    Query the OpenAI API, optionally with function calling.
+    Function calling support is only checked for feedback/review operations.
+    """
     _setup_openai_client()
-    filtered_kwargs: dict = select_values(notnone, model_kwargs)  # type: ignore
+    filtered_kwargs: dict = select_values(notnone, model_kwargs)
+    model_name = filtered_kwargs.get("model", "")
+    logger.debug(f"OpenAI query called with model='{model_name}'")
 
     messages = opt_messages_to_list(system_message, user_message)
 
     if func_spec is not None:
-        filtered_kwargs["tools"] = [func_spec.as_openai_tool_dict]
-        # force the model the use the function
-        filtered_kwargs["tool_choice"] = func_spec.openai_tool_choice_dict
+        # Only check function call support for feedback/search operations
+        if func_spec.name == "submit_review":
+            if not is_function_call_supported(model_name):
+                logger.warning(
+                    f"Review function calling was requested, but model '{model_name}' "
+                    "does not support function calling. Falling back to plain text generation."
+                )
+                filtered_kwargs.pop("tools", None)
+                filtered_kwargs.pop("tool_choice", None)
+            else:
+                filtered_kwargs["tools"] = [func_spec.as_openai_tool_dict]
+                filtered_kwargs["tool_choice"] = func_spec.openai_tool_choice_dict
 
     t0 = time.time()
     completion = backoff_create(
@@ -53,22 +90,30 @@ def query(
 
     choice = completion.choices[0]
 
-    if func_spec is None:
+    if func_spec is None or "tools" not in filtered_kwargs:
         output = choice.message.content
     else:
-        assert (
-            choice.message.tool_calls
-        ), f"function_call is empty, it is not a function call: {choice.message}"
-        assert (
-            choice.message.tool_calls[0].function.name == func_spec.name
-        ), "Function name mismatch"
-        try:
-            output = json.loads(choice.message.tool_calls[0].function.arguments)
-        except json.JSONDecodeError as e:
-            logger.error(
-                f"Error decoding the function arguments: {choice.message.tool_calls[0].function.arguments}"
+        tool_calls = getattr(choice.message, "tool_calls", None)
+
+        if not tool_calls:
+            logger.warning(
+                f"No function call used despite function spec. Fallback to text. "
+                f"Message content: {choice.message.content}"
+            )
+            output = choice.message.content
+        else:
+            first_call = tool_calls[0]
+            assert first_call.function.name == func_spec.name, (
+                f"Function name mismatch: expected {func_spec.name}, "
+                f"got {first_call.function.name}"
             )
-            raise e
+            try:
+                output = json.loads(first_call.function.arguments)
+            except json.JSONDecodeError as e:
+                logger.error(
+                    f"Error decoding function arguments:\n{first_call.function.arguments}"
+                )
+                raise e
 
     in_tokens = completion.usage.prompt_tokens
     out_tokens = completion.usage.completion_tokens