diff --git a/src/votekit/utils.py b/src/votekit/utils.py
index 6badce3b..9631360f 100644
--- a/src/votekit/utils.py
+++ b/src/votekit/utils.py
@@ -1,7 +1,7 @@
 import math
 import random
 from itertools import permutations
-from typing import Literal, Optional, Sequence, Union
+from typing import Any, Literal, Optional, Sequence, Union, cast
 
 import numpy as np
 import pandas as pd
@@ -369,6 +369,66 @@ def first_place_votes(
     )
 
 
+def _ballots_are_materialized(profile: RankProfile) -> bool:
+    """
+    Checks if the ballots are materialized in a ``RankProfile``.
+
+    Args:
+        profile (RankProfile): RankProfile of ballots.
+
+    Returns:
+        bool:
+            True if ballots are materialized, False otherwise.
+    """
+    return "ballots" in profile.__dict__
+
+
+def _mentions_from_df(profile: RankProfile) -> dict[str, float]:
+    assert profile.max_ranking_length is not None
+
+    ranking_cols = [f"Ranking_{i}" for i in range(1, profile.max_ranking_length + 1)]
+
+    tilde = frozenset({"~"})
+
+    rank_sets = cast(Any, profile.df[ranking_cols].stack())
+
+    mask = rank_sets.map(lambda s: isinstance(s, frozenset) and bool(s) and s != tilde)
+
+    rank_sets = rank_sets[mask]
+    exploded = rank_sets.explode()
+
+    if exploded.empty:
+        return {c: 0.0 for c in profile.candidates}
+
+    weights = profile.df["Weight"].reindex(exploded.index.get_level_values(0)).to_numpy()
+
+    totals = pd.Series(weights).groupby(exploded.to_numpy(), sort=False).sum()
+
+    return {c: totals.get(c, 0.0) for c in profile.candidates}
+
+
+def fast_mentions(profile: RankProfile) -> dict[str, float]:
+    """
+    Decides which way to compute mentions based on whether ballots are materialized in the profile.
+    If they are, uses the traditional mentions calculation.
+    If not, uses a faster pandas-based approach.
+
+    Args:
+        profile (RankProfile): RankProfile of ballots.
+
+    Returns:
+        dict[str, float]:
+            Dictionary mapping candidates to mention totals (values).
+    """
+    if not isinstance(profile, RankProfile):
+        raise TypeError("Profile must be of type RankProfile.")
+
+    if _ballots_are_materialized(profile):
+        return mentions(profile)
+
+    return _mentions_from_df(profile)
+
+
 def mentions(
     profile: RankProfile,
 ) -> dict[str, float]:
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 8d52fd6a..1a943623 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -1,4 +1,5 @@
 from itertools import permutations
+from pathlib import Path
 from typing import Literal, cast
 
 import pytest
@@ -12,6 +13,7 @@
     borda_scores,
     elect_cands_from_set_ranking,
     expand_tied_ballot,
+    fast_mentions,
     first_place_votes,
     index_to_lexicographic_ballot,
     mentions,
@@ -24,6 +26,8 @@
     validate_score_vector,
 )
 
+CSV_DIR = Path(__file__).resolve().parents[0] / "data" / "csv"
+
 profile_no_ties = RankProfile(
     ballots=(
         RankBallot(ranking=tuple(map(frozenset, [{"A"}, {"B"}])), weight=1),
@@ -40,6 +44,14 @@
     )
 )
 
+profile_with_duplicates = RankProfile(
+    ballots=(
+        RankBallot(ranking=tuple(map(frozenset, [{"A"}, {"B"}, {"B"}])), weight=1),
+        RankBallot(ranking=tuple(map(frozenset, [{"A"}, {"B"}, {"C"}])), weight=1 / 2),
+        RankBallot(ranking=tuple(map(frozenset, [{"B"}, {"B"}, {"B"}])), weight=3),
+    )
+)
+
 profile_with_missing = RankProfile(
     ballots=(
         RankBallot(ranking=tuple(map(frozenset, [{"A", "B"}, {"D"}])), weight=1),
@@ -251,11 +263,57 @@ def test_mentions():
     assert isinstance(test["A"], float)
 
 
+def test_mentions_with_ties():
+    correct = {"A": 9 / 2, "B": 9 / 2, "C": 7 / 2}
+    test = mentions(profile_with_ties)
+    assert correct == test
+    assert isinstance(test["A"], float)
+
+
+def test_mentions_with_duplicates():
+    correct = {"A": 3 / 2, "B": 23 / 2, "C": 1 / 2}
+    test = mentions(profile_with_duplicates)
+    assert correct == test
+    assert isinstance(test["A"], float)
+
+
+def test_fast_mentions():
+    correct = {"A": 9 / 2, "B": 9 / 2, "C": 7 / 2}
+    test = fast_mentions(profile_no_ties)
+    assert correct == test
+    assert isinstance(test["A"], float)
+
+
+def test_fast_mentions_with_ties():
+    correct = {"A": 9 / 2, "B": 9 / 2, "C": 7 / 2}
+    test = fast_mentions(profile_with_ties)
+    assert correct == test
+    assert isinstance(test["A"], float)
+
+
+def test_fast_mentions_with_duplicates():
+    correct = {"A": 3 / 2, "B": 23 / 2, "C": 1 / 2}
+    test = fast_mentions(profile_with_duplicates)
+    assert correct == test
+    assert isinstance(test["A"], float)
+
+
+@pytest.mark.slow
+def test_fast_and_slow_mentions_are_same():
+    profile = cast(RankProfile, RankProfile.from_csv(CSV_DIR / "albany_profile.csv"))
+    assert mentions(profile) == fast_mentions(profile)
+
+
 def test_mentions_errors():
     with pytest.raises(TypeError, match="Profile must be of type RankProfile"):
         mentions(cast(RankProfile, ScoreProfile(ballots=(ScoreBallot(scores={"A": 3}),))))
 
 
+def test_fast_mentions_errors():
+    with pytest.raises(TypeError, match="Profile must be of type RankProfile"):
+        fast_mentions(cast(RankProfile, ScoreProfile(ballots=(ScoreBallot(scores={"A": 3}),))))
+
+
 def test_borda_no_ties():
     true_borda = {"A": 15 / 2, "B": 9, "C": 19 / 2}