diff --git a/src/lighteval/metrics/metrics.py b/src/lighteval/metrics/metrics.py
index ff4b6b05..306ae603 100644
--- a/src/lighteval/metrics/metrics.py
+++ b/src/lighteval/metrics/metrics.py
@@ -44,6 +44,7 @@
     Faithfulness,
     LoglikelihoodAcc,
     MajAtK,
+    PassAtK,
     Recall,
     StringDistance,
     acc_golds_likelihood,
@@ -364,6 +365,14 @@ class Metrics(Enum):
         corpus_level_fn=CorpusLevelF1Score(average=None, num_classes=3).compute,
         higher_is_better=True,
     )
+    pass_at_k_32 = SampleLevelMetric(
+        metric_name="pass@k:32",
+        sample_level_fn=PassAtK(k=32, strip_strings=True).compute,
+        category=MetricCategory.GENERATIVE_SAMPLING,
+        use_case=MetricUseCase.REASONING,
+        corpus_level_fn=np.mean,
+        higher_is_better=True,
+    )
     perfect_exact_match = SampleLevelMetric(
         metric_name="perfect_em",
         sample_level_fn=ExactMatches().compute,
diff --git a/src/lighteval/metrics/metrics_sample.py b/src/lighteval/metrics/metrics_sample.py
index 352c2b98..2b8023f9 100644
--- a/src/lighteval/metrics/metrics_sample.py
+++ b/src/lighteval/metrics/metrics_sample.py
@@ -1043,3 +1043,118 @@ def compute_score(self, pred: str, gold: str) -> int:
         if self.type_exact_match == "suffix":
             return 1 if pred.endswith(gold) else 0
         return 1 if gold == pred else 0
+
+
+class PassAtK:
+    def __init__(
+        self,
+        k: int,
+        n: int = None,
+        normalize_gold: callable = None,
+        normalize_pred: callable = None,
+        strip_strings: bool = False,
+        sample_scoring_function: callable | str = None,
+    ):
+        """Computing pass at k
+
+        Args:
+            k (int): Threshold for the number of successful attempts.
+            n (int): Number of samples to generate
+            normalize_gold (callable, optional): Function to use to normalize the reference strings.
+                Defaults to None if no normalization is applied.
+            normalize_pred (callable, optional): Function to use to normalize the predicted strings.
+                Defaults to None if no normalization is applied.
+            strip_strings (bool, optional): Whether to strip both reference and predictions. Defaults to False.
+            sample_scoring_function (callable or str, optional): Function to use to score each sample.
+                Either pass the full function (should take a string prediction and a string gold, and return a score between 0 and 1)
+                or a string (any of `prefix`, `suffix` or `full`) to define the type of exact match that you want. Defaults to "full".
+                `prefix` checks if the prediction starts with the gold,
+                `suffix` if the prediction ends with the gold,
+                `full` if the prediction and gold are equal
+        """
+        self.k = k
+        self.n = n
+        self.normalize_gold = normalize_gold
+        self.normalize_pred = normalize_pred
+        self.strip_strings = strip_strings
+
+        # Managed the logic of the per prediction of sample scoring
+        if isinstance(sample_scoring_function, callable):
+            self.score_sample = sample_scoring_function
+            self.type_exact_match = None
+        else:
+            if isinstance(sample_scoring_function, str):
+                if sample_scoring_function not in ["prefix", "suffix", "full"]:
+                    raise ValueError(
+                        f"type_exact_match (used in parametrized_exact_match) must be one of prefix, suffix, or full. Was {sample_scoring_function} instead."
+                    )
+                self.type_exact_match = sample_scoring_function
+            else:
+                self.type_exact_match = "full"
+            self.score_sample = self.default_sample_scoring
+
+    def compute(self, golds: list[str], predictions: list[str], **kwargs) -> dict[str, float]:
+        """Computes the metric over a list of golds and predictions for one single item with possibly many samples.
+        It applies normalisation (if needed) to model prediction and gold, computes their per prediction score,
+        then aggregates the scores over the samples using a pass@k.
+
+        Args:
+            golds (list[str]): Reference targets
+            predictions (list[str]): k predicted strings
+
+        Returns:
+            float: Aggregated score over the current sample's items.
+        """
+        if len(golds) > 1:
+            raise Exception("Cannot compute pass@k with several golds")
+
+        if self.n is None:
+            self.n = len(predictions)
+            logger.warning("n undefined in the pass@k. We assume it's the same as the sample's number of predictions.")
+        elif len(predictions) < self.n:
+            logger.warning(f"Number of predictions is less than {self.n} for pass@k.")
+
+        gold = self.get_processed_gold(golds[0])
+
+        all_scores = []
+        for pred in predictions[: self.n]:
+            cur_pred = self.get_processed_pred(pred=pred)
+            all_scores.append(self.score_sample(cur_pred, gold))
+
+        return self.pass_at_k(all_scores)
+
+    def get_processed_gold(self, gold: str) -> float:
+        if self.strip_strings:
+            gold = gold.strip()
+
+        if self.normalize_gold:
+            gold = self.normalize_gold(gold)
+
+        return gold
+
+    def get_processed_pred(self, pred: str) -> float:
+        if not pred:
+            return ""
+
+        if self.strip_strings:
+            pred = pred.strip()
+
+        if self.normalize_pred:
+            pred = self.normalize_pred(pred)
+
+        return pred
+
+    def default_sample_scoring(self, pred: str, gold: str) -> int:
+        if self.type_exact_match == "prefix":
+            return 1 if pred.startswith(gold) else 0
+        if self.type_exact_match == "suffix":
+            return 1 if pred.endswith(gold) else 0
+        return 1 if gold == pred else 0
+
+    def pass_at_k(self, all_scores: list[int]) -> float:
+        """Algo from https://arxiv.org/pdf/2107.03374"""
+        c: int = all_scores.count(1)
+        if self.n - c < self.k:
+            return 1.0
+
+        return 1.0 - np.prod(1.0 - self.k / np.arange(self.n - c + 1, self.n + 1))