diff --git a/HISTORY.rst b/HISTORY.rst
index f5f20e666fbffcca3b9df6b761540f9d94ac9ba7..a3bf0ffe30762e39ea547ab7dbe65d3b72d0b90a 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -6,6 +6,7 @@ History
 
 * Added --show-distance to cli
 * Fixed a bug where --tolerance was ignored in cli if testing a single image
+* Added benchmark.py to examples
 
 
 0.2.1 (2017-06-03)
diff --git a/examples/benchmark.py b/examples/benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..4904437428ab6aadbd49f430f32985a0b644a056
--- /dev/null
+++ b/examples/benchmark.py
@@ -0,0 +1,77 @@
+import timeit
+
+# Note: This example is only tested with Python 3 (not Python 2)
+
+# This is a very simple benchmark to give you an idea of how fast each step of face recognition will run on your system.
+# Notice that face detection gets very slow at large image sizes. So you might consider running face detection on a
+# scaled down version of your image and then running face encodings on the the full size image.
+
+TEST_IMAGES = [
+    "obama-240p.jpg",
+    "obama-480p.jpg",
+    "obama-720p.jpg",
+    "obama-1080p.jpg"
+]
+
+
+def run_test(setup, test, iterations_per_test=5, tests_to_run=10):
+    fastest_execution = min(timeit.Timer(test, setup=setup).repeat(tests_to_run, iterations_per_test))
+    execution_time = fastest_execution / iterations_per_test
+    fps = 1.0 / execution_time
+    return execution_time, fps
+
+
+setup_locate_faces = """
+import face_recognition
+
+image = face_recognition.load_image_file("{}")
+"""
+
+test_locate_faces = """
+face_locations = face_recognition.face_locations(image)
+"""
+
+setup_face_landmarks = """
+import face_recognition
+
+image = face_recognition.load_image_file("{}")
+face_locations = face_recognition.face_locations(image)
+"""
+
+test_face_landmarks = """
+landmarks = face_recognition.face_landmarks(image, face_locations=face_locations)[0]
+"""
+
+setup_encode_face = """
+import face_recognition
+
+image = face_recognition.load_image_file("{}")
+face_locations = face_recognition.face_locations(image)
+"""
+
+test_encode_face = """
+encoding = face_recognition.face_encodings(image, known_face_locations=face_locations)[0]
+"""
+
+setup_end_to_end = """
+import face_recognition
+
+image = face_recognition.load_image_file("{}")
+"""
+
+test_end_to_end = """
+encoding = face_recognition.face_encodings(image)[0]
+"""
+
+print("Benchmarks (Note: All benchmarks are only using a single CPU core)")
+print()
+
+for image in TEST_IMAGES:
+    size = image.split("-")[1].split(".")[0]
+    print("Timings at {}:".format(size))
+
+    print(" - Face locations: {:.4f}s ({:.2f} fps)".format(*run_test(setup_locate_faces.format(image), test_locate_faces)))
+    print(" - Face landmarks: {:.4f}s ({:.2f} fps)".format(*run_test(setup_face_landmarks.format(image), test_face_landmarks)))
+    print(" - Encode face (inc. landmarks): {:.4f}s ({:.2f} fps)".format(*run_test(setup_encode_face.format(image), test_encode_face)))
+    print(" - End-to-end: {:.4f}s ({:.2f} fps)".format(*run_test(setup_end_to_end.format(image), test_end_to_end)))
+    print()
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