diff --git a/opencompass/summarizers/subjective/subjective.py b/opencompass/summarizers/subjective/subjective.py
index 55c14933e..9693c6ed4 100644
--- a/opencompass/summarizers/subjective/subjective.py
+++ b/opencompass/summarizers/subjective/subjective.py
@@ -4,7 +4,7 @@
 
 import pandas as pd
 from mmengine import ConfigDict
-
+from collections import OrderedDict
 from .utils import get_outdir
 
 
@@ -73,24 +73,24 @@ def summarize(
             None
         """
         output_dir, results_folder = get_outdir(self.cfg, time_str)
-
         flat_data, models_order = flatten_data(subjective_scores)
-
         # Create a DataFrame for each judgemodel with models as rows and datasets as columns
         judgemodel_dfs_final_corrected = {}
         for judgemodel_name, datasets_scores in flat_data.items():
             dfs = {}  # Dictionary to hold DataFrames for each dataset
             for dataset_name, scores in datasets_scores.items():
                 # Create a DataFrame with models as index and datasets as columns
-                df = pd.DataFrame.from_dict(scores,
-                                            orient='index',
-                                            columns=models_order)
+
+                order_of_rows = list(scores.keys())
+                df = pd.DataFrame.from_dict({k: scores[k] for k in order_of_rows}, orient='index')
+                df = df.reindex(order_of_rows)
                 # Insert a new row at the top for the dataset names
                 df.insert(0, 'Detailed Scores', df.index.values)
                 df.insert(0, 'Dataset',
                           [dataset_name for _ in range(len(df.index))])
                 dfs[dataset_name] = df
 
+
             # Concatenate all DataFrames for the current judgemodel
             judgemodel_df = pd.concat(dfs.values(), ignore_index=True)
             judgemodel_dfs_final_corrected[judgemodel_name] = judgemodel_df
@@ -103,3 +103,6 @@ def summarize(
             print('Your subjective evaluation results have been saved at ' +
                   str(fout))
             df.to_csv(fout, index=False)
+
+
+