diff --git a/src/pyrecest/evaluation/get_distance_function.py b/src/pyrecest/evaluation/get_distance_function.py
index 44a2cb3407..f5d2168fe2 100644
--- a/src/pyrecest/evaluation/get_distance_function.py
+++ b/src/pyrecest/evaluation/get_distance_function.py
@@ -255,7 +255,7 @@ def _euclidean_mtt_distance(x1, x2, *, cutoff_distance: float) -> float:
         return float(cutoff_distance * abs(first.shape[0] - second.shape[0]))
 
     deltas = first[:, None, :] - second[None, :, :]
-    costs = numpy.linalg.norm(deltas, axis=2)
+    costs = numpy.hypot.reduce(deltas, axis=2)
     costs = numpy.minimum(costs, float(cutoff_distance))
     row_indices, column_indices = linear_sum_assignment(costs)
     matched_cost = float(costs[row_indices, column_indices].sum())
diff --git a/tests/evaluation/test_euclidean_mtt_distance_orientation.py b/tests/evaluation/test_euclidean_mtt_distance_orientation.py
index 1bb01d276e..45682d5a76 100644
--- a/tests/evaluation/test_euclidean_mtt_distance_orientation.py
+++ b/tests/evaluation/test_euclidean_mtt_distance_orientation.py
@@ -41,3 +41,18 @@ def test_empty_target_dimension_disambiguates_small_dim_first_target_set():
 
     np.testing.assert_allclose(distance(no_targets, dim_first_targets), 21.0)
     np.testing.assert_allclose(distance(dim_first_targets, no_targets), 21.0)
+
+
+def test_euclidean_mtt_distance_preserves_extreme_finite_norm_below_cutoff():
+    distance = get_distance_function(
+        "euclidean_mtt",
+        {"cutoff_distance": 1.5e308},
+    )
+    first = np.array([[1.0e308, 1.0e308]])
+    second = np.array([[0.0, 0.0]])
+    expected = np.hypot(1.0e308, 1.0e308)
+
+    with np.errstate(over="raise", invalid="raise"):
+        result = distance(first, second)
+
+    np.testing.assert_allclose(result, expected, rtol=1.0e-15)