diff --git a/lm_eval/models/openai_completions.py b/lm_eval/models/openai_completions.py
index 87e4bd2bbe..c8f27ff75d 100644
--- a/lm_eval/models/openai_completions.py
+++ b/lm_eval/models/openai_completions.py
@@ -14,13 +14,11 @@
 from lm_eval.utils import eval_logger
 
 
-def get_result(response, ctxlen: int) -> Tuple[float, bool]:
+def get_result(response) -> Tuple[float, bool]:
     """Process results from OpenAI API response.
 
     :param response: dict
         OpenAI API Response
-    :param ctxlen: int
-        Length of context (so we can slice them away and only keep the predictions)
     :return:
         continuation_logprobs: np.array
             Log probabilities of continuation tokens
@@ -29,9 +27,9 @@ def get_result(response, ctxlen: int) -> Tuple[float, bool]:
     """
     is_greedy = True
     logprobs = response.logprobs.token_logprobs
-    continuation_logprobs = sum(logprobs[ctxlen:])
+    continuation_logprobs = sum(logprobs)
 
-    for i in range(ctxlen, len(response.logprobs.token_logprobs)):
+    for i in range(len(response.logprobs.token_logprobs)):
         token = response.logprobs.token_logprobs[i]
         top_tokens = response.logprobs.top_logprobs[i]
         top_token = max(top_tokens.keys(), key=lambda x: top_tokens[x])
@@ -212,7 +210,6 @@ def _collate(x):
                 client=self.client,
                 model=self.model,
                 prompt=inps,
-                echo=True,
                 max_tokens=0,
                 temperature=0.0,
                 logprobs=10,
@@ -222,7 +219,7 @@ def _collate(x):
             for resp, ctxlen, (cache_key, context_enc, continuation_enc) in zip(
                 response.choices, ctxlens, chunk
             ):
-                answer = get_result(resp, ctxlen)
+                answer = get_result(resp)
 
                 res.append(answer)