diff --git a/scripts/compare-benchmark-jsons.py b/scripts/compare-benchmark-jsons.py
index 1802325c4cd..499294bfd42 100644
--- a/scripts/compare-benchmark-jsons.py
+++ b/scripts/compare-benchmark-jsons.py
@@ -63,6 +63,27 @@ def extract_dataset_key(df):
 improvement_threshold = 1.0 - (threshold_pct / 100.0)  # e.g., 0.7 for 30%, 0.9 for 10%
 regression_threshold = 1.0 + (threshold_pct / 100.0)  # e.g., 1.3 for 30%, 1.1 for 10%
 
+
+def compute_cv_pct(runtimes):
+    """Compute coefficient of variation (std_dev / mean * 100) as a percentage."""
+    if not isinstance(runtimes, list) or len(runtimes) < 2:
+        return float("nan")
+    n = len(runtimes)
+    mean = sum(runtimes) / n
+    if mean == 0:
+        return float("nan")
+    variance = sum((x - mean) ** 2 for x in runtimes) / (n - 1)
+    return (variance**0.5 / mean) * 100
+
+
+# Compute CV% from all_runtimes when available
+has_z_pr = "all_runtimes_pr" in df3.columns
+has_z_base = "all_runtimes_base" in df3.columns
+if has_z_pr:
+    df3["cv_pct_pr"] = df3["all_runtimes_pr"].apply(compute_cv_pct)
+if has_z_base:
+    df3["cv_pct_base"] = df3["all_runtimes_base"].apply(compute_cv_pct)
+
 # Generate summary statistics
 df3["ratio"] = df3["value_pr"] / df3["value_base"]
 df3["remark"] = pd.Series([""] * len(df3))
@@ -183,16 +204,21 @@ def format_performance(ratio, target_name):
     )
 
 # Build table
-table_df = pd.DataFrame(
-    {
-        "name": df3["name"],
-        f"PR {pr_commit_id[:8]}": df3["value_pr"],
-        f"base {base_commit_id[:8]}": df3["value_base"],
-        "ratio (PR/base)": df3["ratio"],
-        "unit": df3["unit_base"],
-        "remark": df3["remark"],
-    }
-)
+table_dict = {
+    "name": df3["name"],
+    f"PR {pr_commit_id[:8]}": df3["value_pr"],
+    f"base {base_commit_id[:8]}": df3["value_base"],
+    "ratio (PR/base)": df3["ratio"],
+    "unit": df3["unit_base"],
+}
+
+if has_z_pr:
+    table_dict["CV% PR"] = df3["cv_pct_pr"]
+if has_z_base:
+    table_dict["CV% base"] = df3["cv_pct_base"]
+
+table_dict["remark"] = df3["remark"]
+table_df = pd.DataFrame(table_dict)
 
 # Output complete formatted markdown
 print("\n".join(summary_lines))