diff --git a/flexeval/core/language_model/openai_api.py b/flexeval/core/language_model/openai_api.py
index 5aa2594f..c6aa2b97 100644
--- a/flexeval/core/language_model/openai_api.py
+++ b/flexeval/core/language_model/openai_api.py
@@ -226,7 +226,7 @@ def _batch_complete_text(
             LMOutput(
                 text=res.choices[0].message.content,
                 reasoning_text=get_reasoning_text(res.choices[0].message),
-                finish_reason=res.choices[0].finish_reason,
+                finish_reason="empty" if res is self.empty_response else res.choices[0].finish_reason,
             )
             for res in api_responses
         ]
@@ -246,7 +246,7 @@ def _batch_generate_chat_response(
             LMOutput(
                 text=res.choices[0].message.content,
                 reasoning_text=get_reasoning_text(res.choices[0].message),
-                finish_reason=res.choices[0].finish_reason,
+                finish_reason="empty" if res is self.empty_response else res.choices[0].finish_reason,
                 tool_calls=[tool_call.to_dict() for tool_call in res.choices[0].message.tool_calls]
                 if res.choices[0].message.tool_calls
                 else None,
@@ -295,17 +295,21 @@ def _batch_compute_chat_log_probs(
         )
 
         log_probs = []
-        top_logprobs_list = [res.choices[0].logprobs.content[0].top_logprobs for res in api_responses]
+        top_logprobs_list = [
+            None if res is self.empty_response else res.choices[0].logprobs.content[0].top_logprobs
+            for res in api_responses
+        ]
         for index, prompt in enumerate(prompt_list):
             target_token = response_contents[index]
             index_in_unique = unique_prompt_list.index(prompt)
 
-            log_prob = None  # if target token not in top_logprobs, return None for log_prob of the token
+            log_prob = None  # if target token not in top_logprobs, or the request errored, return None
             top_logprobs = top_logprobs_list[index_in_unique]
-            for token_logprob in top_logprobs:
-                if token_logprob.token == target_token:
-                    log_prob = token_logprob.logprob
-                    break
+            if top_logprobs is not None:
+                for token_logprob in top_logprobs:
+                    if token_logprob.token == target_token:
+                        log_prob = token_logprob.logprob
+                        break
             log_probs.append(log_prob)
 
         return log_probs
@@ -450,7 +454,12 @@ def _batch_complete_text(
             **kwargs,
         )
 
-        return [LMOutput(text=res.choices[0].text, finish_reason=res.choices[0].finish_reason) for res in api_responses]
+        return [
+            LMOutput(text="", finish_reason="empty")
+            if res is self.empty_response
+            else LMOutput(text=res.choices[0].text, finish_reason=res.choices[0].finish_reason)
+            for res in api_responses
+        ]
 
     def __repr__(self) -> str:
         return f"{self.__class__.__name__}(model={self.model})"
diff --git a/flexeval/core/language_model/openai_batch_api.py b/flexeval/core/language_model/openai_batch_api.py
index 0ca208b0..6f868edc 100644
--- a/flexeval/core/language_model/openai_batch_api.py
+++ b/flexeval/core/language_model/openai_batch_api.py
@@ -257,7 +257,12 @@ def _batch_complete_text(
             **kwargs,
         )
         return [
-            LMOutput(text=res["choices"][0]["message"]["content"], finish_reason=res["choices"][0]["finish_reason"])
+            LMOutput(text="", finish_reason="empty")
+            if isinstance(res, str)
+            else LMOutput(
+                text=res["choices"][0]["message"]["content"],
+                finish_reason=res["choices"][0]["finish_reason"],
+            )
             for res in api_responses
         ]
 
@@ -273,7 +278,9 @@ def _batch_generate_chat_response(
             **kwargs,
         )
         return [
-            LMOutput(
+            LMOutput(text="", finish_reason="empty")
+            if isinstance(res, str)
+            else LMOutput(
                 text=res["choices"][0]["message"]["content"],
                 finish_reason=res["choices"][0]["finish_reason"],
                 tool_calls=res["choices"][0]["message"].get("tool_calls", None),
@@ -319,17 +326,21 @@ def _batch_compute_chat_log_probs(
         )
 
         log_probs = []
-        top_logprobs_list = [res["choices"][0]["logprobs"]["content"][0]["top_logprobs"] for res in api_responses]
+        top_logprobs_list = [
+            None if isinstance(res, str) else res["choices"][0]["logprobs"]["content"][0]["top_logprobs"]
+            for res in api_responses
+        ]
         for index, prompt in enumerate(prompt_list):
             target_token = response_contents[index]
             index_in_unique = unique_prompt_list.index(prompt)
 
-            log_prob = None  # if target token not in top_logprobs, return None for log_prob of the token
+            log_prob = None  # if target token not in top_logprobs, or the request errored, return None
             top_logprobs = top_logprobs_list[index_in_unique]
-            for token_logprob in top_logprobs:
-                if token_logprob["token"] == target_token:
-                    log_prob = token_logprob["logprob"]
-                    break
+            if top_logprobs is not None:
+                for token_logprob in top_logprobs:
+                    if token_logprob["token"] == target_token:
+                        log_prob = token_logprob["logprob"]
+                        break
             log_probs.append(log_prob)
 
         return log_probs