diff --git a/debug/accuracy_tools/api_accuracy_checker/test/ut/compare/test_compare.py b/debug/accuracy_tools/api_accuracy_checker/test/ut/compare/test_compare.py
new file mode 100644
index 0000000000000000000000000000000000000000..6dc5cca0299bc060cd42b2ac544456c94964cc61
--- /dev/null
+++ b/debug/accuracy_tools/api_accuracy_checker/test/ut/compare/test_compare.py
@@ -0,0 +1,74 @@
+import csv
+import os
+import shutil
+import time
+import unittest
+import torch.nn.functional
+
+from api_accuracy_checker.compare.compare import Comparator
+
+current_time = time.strftime("%Y%m%d%H%M%S")
+RESULT_FILE_NAME = "accuracy_checking_result_" + current_time + ".csv"
+DETAILS_FILE_NAME = "accuracy_checking_details_" + current_time + '.csv'
+base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+
+class TestCompare(unittest.TestCase):
+    def setUp(self):
+        self.output_path = os.path.join(base_dir, "../compare_result")
+        os.mkdir(self.output_path, mode=0o750)
+        self.result_csv_path = os.path.join(self.output_path, RESULT_FILE_NAME)
+        self.details_csv_path = os.path.join(self.output_path, DETAILS_FILE_NAME)
+        self.is_continue_run_ut = False
+        self.compare = Comparator(self.result_csv_path, self.details_csv_path, self.is_continue_run_ut)
+
+    def tearDown(self) -> None:
+        if os.path.exists(self.output_path):
+            shutil.rmtree(self.output_path)
+
+    def test_compare_dropout(self):
+        dummmy_input = torch.randn(100, 100)
+        bench_out = torch.nn.functional.dropout2d(dummmy_input, 0.3)
+        npu_out = torch.nn.functional.dropout2d(dummmy_input, 0.3)
+        self.assertTrue(self.compare._compare_dropout(bench_out, npu_out))
+
+    def test_compare_core_wrapper(self):
+        dummy_input = torch.randn(100, 100)
+        bench_out, npu_out = dummy_input, dummy_input
+        test_final_success, detailed_result_total = self.compare._compare_core_wrapper(bench_out, npu_out)
+        self.assertTrue(test_final_success)
+        self.assertEqual(detailed_result_total, [['torch.float32', 'torch.float32', (100, 100), 1.0, 0.0, 'N/A', 'N/A',
+                                                  'N/A', 'N/A', 'pass', '\n']])
+
+        bench_out, npu_out = [dummy_input, dummy_input], [dummy_input, dummy_input]
+        test_final_success, detailed_result_total = self.compare._compare_core_wrapper(bench_out, npu_out)
+        self.assertTrue(test_final_success)
+        self.assertEqual(detailed_result_total, [['torch.float32', 'torch.float32', (100, 100), 1.0, 0.0, 'N/A', 'N/A',
+                                                  'N/A', 'N/A', 'pass', '\n'], ['torch.float32', 'torch.float32',
+                                                                                (100, 100), 1.0, 0.0, 'N/A', 'N/A',
+                                                                                'N/A', 'N/A', 'pass', '\n']])
+
+    def test_compare_output(self):
+        bench_out, npu_out = torch.randn(100, 100), torch.randn(100, 100)
+        bench_grad, npu_grad = [torch.randn(100, 100)], [torch.randn(100, 100)]
+        api_name = 'Functional*conv2d*0'
+        is_fwd_success, is_bwd_success = self.compare.compare_output(api_name, bench_out, npu_out, bench_grad, npu_grad)
+        self.assertFalse(is_fwd_success)
+        self.assertFalse(is_bwd_success)
+
+        dummy_input = torch.randn(100, 100)
+        bench_out, npu_out = dummy_input, dummy_input
+        is_fwd_success, is_bwd_success = self.compare.compare_output(api_name, bench_out, npu_out)
+        self.assertTrue(is_fwd_success)
+        self.assertTrue(is_bwd_success)
+
+    def test_record_results(self):
+        args = ('Functional*conv2d*0', False, 'N/A', [['torch.float64', 'torch.float32', (32, 64, 112, 112), 1.0,
+                                                       0.012798667686, 'N/A', 0.81631212311, 0.159979121213, 'N/A',
+                                                       'error', '\n']], None)
+        self.compare.record_results(*args)
+        with open(self.details_csv_path, 'r') as file:
+            csv_reader = csv.reader(file)
+            next(csv_reader)
+            api_name_list = [row[0] for row in csv_reader]
+        self.assertEqual(api_name_list[0], 'Functional*conv2d*0.forward.output.0')