diff --git a/ndsl/testing/comparison.py b/ndsl/testing/comparison.py
index c1a5b1ef..b5c1ac0d 100644
--- a/ndsl/testing/comparison.py
+++ b/ndsl/testing/comparison.py
@@ -260,11 +260,17 @@ def __init__(
         # We might have sliced outputs in the translate test. Rather than funnel the slicing
         # all the way down, we bail out if we can measure input vs reference
         if input_values is not None and input_values.shape == reference_values.shape:
-            self.number_changing_values = (
-                (input_values != reference_values).flatten().shape[0]
-            )
+            self.number_changing_values = (input_values != reference_values).sum()
+            # column information is only relevant if data is three-dimensional
+            if len(input_values.shape) == 3:
+                self.changing_column_map = (input_values != reference_values).any(
+                    axis=2
+                )
+            else:
+                self.changing_column_map = None
         else:
             self.number_changing_values = None
+            self.changing_column_map = None
 
     def _compute_all_metrics(
         self,
@@ -338,20 +344,38 @@ def report(self, file_path: str | None = None) -> list[str]:
         failed_indices = np.logical_not(self.success).nonzero()
         # List all errors to terminal and file
         bad_indices_count = len(failed_indices[0])
+        if self.changing_column_map is not None:
+            if self.success.ndim == 3:
+                bad_column_count = (
+                    np.logical_not(self.success).any(axis=2) & self.changing_column_map
+                ).sum()
+                total_column_count = self.changing_column_map.sum()
+                bad_column_pct = round(bad_column_count / total_column_count * 100, 2)
+            else:
+                bad_column_count = None
+                total_column_count = None
+                bad_column_pct = None
+        else:
+            bad_column_count = None
+            total_column_count = None
+            bad_column_pct = None
         full_count = len(self.references.flatten())
         failures_of_all_grid_points_pct = round(
             100.0 * (bad_indices_count / full_count), 2
         )
-        if self.number_changing_values is not None:
+        if (
+            self.number_changing_values is not None
+            and bad_indices_count is not None
+            and bad_column_count is not None
+        ):
             failures_of_changing_gridpoint_pct = round(
                 100.0 * (bad_indices_count / self.number_changing_values), 2
             )
-            report_local_failures = f"Failures (changing grid points) ({bad_indices_count}/{self.number_changing_values}) ({failures_of_changing_gridpoint_pct}%)\n"
+            report_local_failures = f"Failures: (changing columns, chainging points, all points) | {bad_column_count}/{total_column_count} - {bad_column_pct}%, {bad_indices_count}/{self.number_changing_values} - {failures_of_changing_gridpoint_pct}%, {bad_indices_count}/{full_count} - {failures_of_all_grid_points_pct}%\n"
         else:
-            report_local_failures = ""
+            report_local_failures = f"all grid points: {bad_indices_count}/{full_count} - {failures_of_all_grid_points_pct}%\n"
         report = [
             f"{report_local_failures}"
-            f"Failures (all grid points) ({bad_indices_count}/{full_count}) ({failures_of_all_grid_points_pct}%)\n",
             f"Index   Computed   Reference   "
             f"{'🔶 ' if not self.absolute_eps.is_default else ''}Absolute E(<{self.absolute_eps.value:.2e})  "
             f"{'🔶 ' if not self.relative_fraction.is_default else ''}Relative E(<{self.relative_fraction.value * 100:.2e}%)   "