diff --git a/langtest/langtest.py b/langtest/langtest.py
index e30ab002f..f3e1f51d6 100644
--- a/langtest/langtest.py
+++ b/langtest/langtest.py
@@ -1758,6 +1758,7 @@ def get_leaderboard(
         category=False,
         split_wise=False,
         test_wise=False,
+        rank_by: Union[str, list] = "Avg",
         *args,
         **kwargs,
     ):
@@ -1776,15 +1777,15 @@ def get_leaderboard(
         if indices or columns:
             return leaderboard.custom_wise(indices, columns)
         if category:
-            return leaderboard.category_wise()
+            return leaderboard.category_wise(rank_by=rank_by)
 
         if test_wise:
-            return leaderboard.test_wise()
+            return leaderboard.test_wise(rank_by=rank_by)
 
         if split_wise:
-            return leaderboard.split_wise()
+            return leaderboard.split_wise(rank_by=rank_by)
 
-        return leaderboard.default()
+        return leaderboard.default(rank_by=rank_by)
 
     def __temp_generate(self, *args, **kwargs):
         """Temporary function to generate the testcases."""
diff --git a/langtest/utils/benchmark_utils.py b/langtest/utils/benchmark_utils.py
index 4b07dae65..f3e08f839 100644
--- a/langtest/utils/benchmark_utils.py
+++ b/langtest/utils/benchmark_utils.py
@@ -1,5 +1,5 @@
 import os
-from typing import TypeVar, Generic
+from typing import TypeVar, Generic, Union
 import pandas as pd
 
 
@@ -35,10 +35,16 @@ def __init__(
         """
         self.summary = Summary(path, *args, **kwargs)
 
-    def default(self):
+    def default(self, rank_by: Union[str, list] = "Avg"):
         """
         Get the score board for the models
         """
+        # check if the rank_by is a string
+        if isinstance(rank_by, str):
+            rank_by = [rank_by]
+
+        ascending = [False] * len(rank_by)
+
         df = self.summary.summary_df
         df = self.__drop_duplicates(df)
         pvt_table = df.pivot_table(
@@ -47,7 +53,7 @@ def default(self):
 
         # mean column
         pvt_table.insert(0, "Avg", pvt_table.mean(axis=1))
-        pvt_table = pvt_table.sort_values(by=["model", "Avg"], ascending=[True, False])
+        pvt_table = pvt_table.sort_values(by=rank_by, ascending=ascending)
 
         # reset the index and fill the NaN values
         pvt_table = pvt_table.rename_axis(None, axis=1).reset_index()
@@ -55,11 +61,17 @@ def default(self):
 
         return pvt_table
 
-    def split_wise(self):
+    def split_wise(self, rank_by: Union[str, list] = "Avg"):
         """
         Get the score board for the models by test type
         """
 
+        # check if the rank_by is a string
+        if isinstance(rank_by, str):
+            rank_by = [rank_by]
+
+        ascending = [False] * len(rank_by)
+
         df = self.summary.summary_df
         df = self.__drop_duplicates(df)
         pvt_table = df.pivot_table(
@@ -70,51 +82,77 @@ def split_wise(self):
 
         # mean column
         pvt_table.insert(0, "Avg", pvt_table.mean(axis=1))
-        pvt_table = pvt_table.sort_values(by=["model", "Avg"], ascending=[True, False])
+        pvt_table = pvt_table.sort_values(by=rank_by, ascending=ascending)
 
         pvt_table = pvt_table.fillna("-")
 
         return pvt_table
 
-    def test_wise(self):
+    def test_wise(self, rank_by: Union[str, list] = "Avg"):
         """
         Get the score board for the models by test type
         """
 
+        # check if the rank_by is a string
+        if isinstance(rank_by, str):
+            rank_by = [rank_by]
+
+        # check if the test_type in the rank_by
+        if "test_type" not in rank_by:
+            rank_by.insert(0, "test_type")
+            rank_by.insert(0, "category")
+
+        ascending = [True, True] + [False] * (len(rank_by) - 2)
+
         df = self.summary.summary_df
         df = self.__drop_duplicates(df)
         pvt_table = df.pivot_table(
-            index=["model", "test_type"], columns=["dataset_name"], values="score"
+            index=["category", "test_type", "model"],
+            columns=["dataset_name"],
+            values="score",
         )
 
         # mean column
         pvt_table.insert(0, "Avg", pvt_table.mean(axis=1))
-        pvt_table = pvt_table.sort_values(by=["model", "Avg"], ascending=[True, False])
+        pvt_table = pvt_table.sort_values(by=rank_by, ascending=ascending)
 
         pvt_table = pvt_table.fillna("-")
 
         return pvt_table
 
-    def category_wise(self):
+    def category_wise(self, rank_by: Union[str, list] = "Avg"):
         """
         Get the score board for the models by category
         """
+        # check if the rank_by is a string
+        if isinstance(rank_by, str):
+            rank_by = [rank_by]
+
+        ascending = [False] * len(rank_by)
+
         df = self.summary.summary_df
         df = self.__drop_duplicates(df)
         pvt_table = df.pivot_table(
-            index=["model", "category"], columns=["dataset_name"], values="score"
+            index=["category", "model"], columns=["dataset_name"], values="score"
         )
         pvt_table.insert(0, "Avg", pvt_table.mean(axis=1))
-        pvt_table = pvt_table.sort_values(by=["model", "Avg"], ascending=[True, False])
+        pvt_table = pvt_table.sort_values(by=rank_by, ascending=ascending)
         pvt_table = pvt_table.fillna("-")
-        pvt_table = pvt_table.rename_axis(None, axis=1).reset_index()
 
         return pvt_table
 
-    def custom_wise(self, indices: list, columns: list = []):
+    def custom_wise(
+        self, indices: list, columns: list = [], rank_by: Union[str, list] = "Avg"
+    ):
         """
         Get the score board for the models by custom group
         """
+        # check if the rank_by is a string
+        if isinstance(rank_by, str):
+            rank_by = [rank_by]
+
+        ascending = [False] * len(rank_by)
+
         df = self.summary.summary_df
         df = self.__drop_duplicates(df)
         pvt_table = df.pivot_table(
@@ -125,8 +163,7 @@ def custom_wise(self, indices: list, columns: list = []):
         )
         pvt_table.insert(0, "Avg", pvt_table.mean(axis=1))
         pvt_table = pvt_table.fillna("-")
-        pvt_table = pvt_table.sort_values(by=["model", "Avg"], ascending=[True, False])
-        # pvt_table = pvt_table.rename_axis(None, axis=1).reset_index()
+        pvt_table = pvt_table.sort_values(by=rank_by, ascending=ascending)
 
         return pvt_table