diff --git a/python/examples/bert/bert_web_service.py b/python/examples/bert/bert_web_service.py
index 64091817d84ec2bb7e608acf06a8f550ce5972de..a5674e133cb40948ea5a3f4f3e2f4791de5c4115 100644
--- a/python/examples/bert/bert_web_service.py
+++ b/python/examples/bert/bert_web_service.py
@@ -30,6 +30,7 @@ class BertService(WebService):
     def preprocess(self, feed=[], fetch=[]):
         feed_res = []
         is_batch = True
+        print(feed)
         for ins in feed:
             feed_dict = self.reader.process(ins["words"].encode("utf-8"))
             for key in feed_dict.keys():
diff --git a/python/examples/bert/new_benchmark.py b/python/examples/bert/new_benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab367ce0d0e46860ef8bb16298b59fd0a06365c2
--- /dev/null
+++ b/python/examples/bert/new_benchmark.py
@@ -0,0 +1,59 @@
+import sys
+import os
+import yaml
+import requests
+import time
+import json
+import ast
+from paddle_serving_client.utils import MultiThreadRunner
+from paddle_serving_client.utils import benchmark_args, show_latency
+
+def parse_benchmark(filein, fileout):
+    with open(filein, "r") as fin:
+        res = yaml.load(fin)
+        del_list = []
+        for key in res["DAG"].keys():
+            if "call" in key:
+                del_list.append(key)
+        for key in del_list:
+            del res["DAG"][key]
+    with open(fileout, "w") as fout:
+        yaml.dump(res, fout, default_flow_style=False)
+
+def run_http(idx, batch_size):
+    """
+    {"feed":[{"words": "hello"}], "fetch":["pooled_output"]}
+    """
+    print("start thread ({})".format(idx))
+    url = "http://127.0.0.1:9292/bert/prediction"    
+    start = time.time()
+    with open("data-c.txt", 'r') as fin:
+        start = time.time()
+        lines = fin.readlines()
+        start_idx = 0
+        while start_idx < len(lines):
+            end_idx = min(len(lines), start_idx + batch_size)
+            feed = {}
+            feed_lst = [{"words": lines[i]} for i in range(start_idx, end_idx)]
+            data = {"feed": feed_lst, "fetch": ["pooled_output"]}
+            r = requests.post(url=url, data=json.dumps(data), headers={"Content-Type": "application/json"})
+            start_idx += batch_size
+            end = time.time()
+            if end - start > 40:
+                break            
+        end = time.time()
+    return [[end - start]]
+
+def multithread_http(thread, batch_size):
+    multi_thread_runner = MultiThreadRunner()
+    result = multi_thread_runner.run(run_http , thread, batch_size)
+
+if __name__ == "__main__":
+   if sys.argv[1] == "run":
+       thread = int(sys.argv[2])
+       batch_size = int(sys.argv[3])
+       multithread_http(thread, batch_size)
+   if sys.argv[1] == "dump":
+       filein = sys.argv[2]
+       fileout = sys.argv[3]
+       parse_benchmark(filein, fileout)
diff --git a/python/examples/bert/new_benchmark.sh b/python/examples/bert/new_benchmark.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9373be30f200f6815c195b39c75b5426d5edec1e
--- /dev/null
+++ b/python/examples/bert/new_benchmark.sh
@@ -0,0 +1,25 @@
+modelname="bert"
+# HTTP
+ps -ef | grep web_service | awk '{print $2}' | xargs kill -9 
+sleep 3
+rm -rf profile_log_$modelname
+for thread_num in 1 8 16 
+do
+  for batch_size in 1 10 100
+  do
+    python3.7 bert_web_service.py bert_seq128_model/ 9292 &
+    sleep 3
+    echo "----Bert thread num: $thread_num batch size: $batch_size mode:http ----" >>profile_log_$modelname
+    nvidia-smi --id=2 --query-compute-apps=used_memory --format=csv -lms 100 > gpu_use.log 2>&1 &
+    nvidia-smi --id=2 --query-gpu=utilization.gpu --format=csv -lms 100 > gpu_utilization.log 2>&1 &
+    echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
+    python3.7 new_benchmark.py run $thread_num $batch_size
+    python3.7 cpu_utilization.py >>profile_log_$modelname
+    ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
+    python3.7 new_benchmark.py dump benchmark.log benchmark.tmp
+    mv benchmark.tmp benchmark.log
+    awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "MAX_GPU_MEMORY:", max}' gpu_use.log >> profile_log_$modelname
+    awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_UTILIZATION:", max}' gpu_utilization.log >> profile_log_$modelname
+    cat benchmark.log >> profile_log_$modelname
+  done
+done