diff --git a/src/lighteval/models/endpoint_model.py b/src/lighteval/models/endpoint_model.py
index e8f18502f..c1b5a50fe 100644
--- a/src/lighteval/models/endpoint_model.py
+++ b/src/lighteval/models/endpoint_model.py
@@ -23,6 +23,7 @@
 import asyncio
 from typing import Coroutine, List, Optional, Union
 
+import torch
 from huggingface_hub import (
     AsyncInferenceClient,
     InferenceClient,
@@ -314,17 +315,22 @@ def loglikelihood(
         ):
             dataloader = DataLoader(dataset, batch_size=batch_size, collate_fn=lambda batch: batch)
 
-            for batch in tqdm(dataloader, desc="Loglikleihoods", position=1, leave=False, disable=self.disable_tqdm):
+            for batch in tqdm(dataloader, desc="Loglikelihoods", position=1, leave=False, disable=self.disable_tqdm):
                 if self.use_async:
                     responses = asyncio.run(self.__async_process_batch_logprob(batch))
                 else:
                     responses = self.__process_batch_logprob(batch)
-                for ix, response in enumerate(responses):
-                    len_choice = len(batch[ix].tokenized_continuation)
+                for cur_request, response in zip(batch, responses):
+                    cont_toks = torch.tensor(cur_request.tokenized_continuation)
+                    len_choice = len(cont_toks)
+
                     logits = [t.logprob for t in response.details.prefill[-len_choice:] if t.logprob is not None]
+
+                    greedy_tokens = torch.tensor(logits).argmax(dim=-1)
+                    max_equal = (greedy_tokens == cont_toks).all().squeeze(0)
                     results.append(
                         LoglikelihoodReturn(
-                            result=sum(logits),
+                            result=(sum(logits), bool(max_equal)),
                             input_tokens=[t.id for t in response.details.prefill[:-len_choice]],
                             generated_tokens=[t.id for t in response.details.prefill[-len_choice:]],
                             truncated_tokens_count=-1,
@@ -355,13 +361,16 @@ def loglikelihood_rolling(
         ):
             dataloader = DataLoader(dataset, batch_size=batch_size, collate_fn=lambda batch: batch)
 
-            for batch in tqdm(dataloader, desc="Loglikleihoods", position=1, leave=False, disable=self.disable_tqdm):
+            for batch in tqdm(
+                dataloader, desc="Loglikelihoods, rolling", position=1, leave=False, disable=self.disable_tqdm
+            ):
                 if self.use_async:
                     responses = asyncio.run(self.__async_process_batch_logprob(batch, rolling=True))
                 else:
                     responses = self.__process_batch_logprob(batch, rolling=True)
                 for response in responses:
                     logits = [t.logprob for t in response.details.tokens[:-1]]
+
                     results.append(
                         LoglikelihoodReturn(
                             result=sum(logits),