diff --git a/opencompass/configs/summarizers/bbeh.py b/opencompass/configs/summarizers/bbeh.py
deleted file mode 100644
index ba469f82..00000000
--- a/opencompass/configs/summarizers/bbeh.py
+++ /dev/null
@@ -1,30 +0,0 @@
-from mmengine.config import read_base
-
-with read_base():
-    from .groups.bbeh import bbeh_summary_groups
-
-# Get all the BBEH subset names from the imported bbeh_summary_groups
-bbeh_subsets = []
-for group in bbeh_summary_groups:
-    if group['name'] == 'bbeh':
-        bbeh_subsets = group['subsets']
-        break
-
-summarizer = dict(
-    # Include both individual datasets and the summary metrics we want to see
-    dataset_abbrs=bbeh_subsets + ['bbeh_naive_average'] + ['bbeh_harmonic_mean'],
-    
-    # Define the summary group for bbeh
-    summary_groups=[
-        {
-            'name': 'bbeh_naive_average',
-            'subsets': bbeh_subsets,
-            'metric': 'naive_average'  # Explicitly specify the metric to use
-        },
-        {
-            'name': 'bbeh_harmonic_mean',
-            'subsets': bbeh_subsets,
-            'metric': 'harmonic_mean'
-        }
-    ]
-)
\ No newline at end of file
diff --git a/opencompass/configs/summarizers/groups/bbeh.py b/opencompass/configs/summarizers/groups/bbeh.py
index 5e5cc222..95697144 100644
--- a/opencompass/configs/summarizers/groups/bbeh.py
+++ b/opencompass/configs/summarizers/groups/bbeh.py
@@ -9,4 +9,5 @@ _bbeh = [
     'bbeh_spatial_reasoning', 'bbeh_sportqa', 'bbeh_temporal_sequence', 'bbeh_time_arithmetic',
     'bbeh_web_of_lies', 'bbeh_word_sorting', 'bbeh_zebra_puzzles'
 ]
-bbeh_summary_groups.append({'name': 'bbeh', 'subsets': _bbeh})
+bbeh_summary_groups.append({'name': 'bbeh', 'subsets': _bbeh, 'metric':'naive_average'})
+bbeh_summary_groups.append({'name': 'bbeh', 'subsets': _bbeh, 'metric':'harmonic_mean'})
\ No newline at end of file
diff --git a/opencompass/summarizers/default.py b/opencompass/summarizers/default.py
index f1094f14..88dd793b 100644
--- a/opencompass/summarizers/default.py
+++ b/opencompass/summarizers/default.py
@@ -188,18 +188,18 @@ class DefaultSummarizer:
                         eval_modes.append(dataset_eval_mode.get(dataset_abbr, 'unknown'))
                 else:
                     group_metrics = list(functools.reduce(lambda a, b: a & b, [set(dataset_metrics[dataset_abbr]) for dataset_abbr in sg['subsets']]))
-                    if need_smart_metric and len(group_metrics) > 1:
-                        for metric in group_metrics:
-                            for dataset_abbr in sg['subsets']:
-                                scores.setdefault(metric, {})[dataset_abbr + '@' + metric] = parsed_results[model_abbr][dataset_abbr][metric]
-                                eval_modes.append(dataset_eval_mode.get(sg['subsets'][0], 'unknown'))
-                    else:
-                        group_metrics = [default_metric]
+                    group_metrics.append(default_metric)
+                    for metric in group_metrics:
                         for dataset_abbr in sg['subsets']:
-                            metric = dataset_metrics[dataset_abbr][0]
-                            scores.setdefault(default_metric, {})[dataset_abbr + '@' + metric] = parsed_results[model_abbr][dataset_abbr][metric]
-                            eval_modes.append(dataset_eval_mode.get(dataset_abbr, 'unknown'))
-
+                            if metric == default_metric:
+                                metric_default = dataset_metrics[dataset_abbr][0]
+                                scores.setdefault(default_metric, {})[dataset_abbr + '@' + metric_default] = \
+                                parsed_results[model_abbr][dataset_abbr][metric_default]
+                                eval_modes.append(dataset_eval_mode.get(dataset_abbr, 'unknown'))
+                            else:
+                                scores.setdefault(metric, {})[dataset_abbr + '@' + metric] = \
+                                parsed_results[model_abbr][dataset_abbr][metric]
+                                eval_modes.append(dataset_eval_mode.get(sg['subsets'][0], 'unknown'))
                 result = {}
                 for metric in scores:
                     if default_metric == 'standard_deviation':