diff --git a/python/ray/ray_perf.py b/python/ray/ray_perf.py
index bcda187926ba..eae62e396fd3 100644
--- a/python/ray/ray_perf.py
+++ b/python/ray/ray_perf.py
@@ -1,5 +1,6 @@
 """This is the script for `ray microbenchmark`."""
 
+import asyncio
 import os
 import time
 import numpy as np
@@ -22,6 +23,18 @@ def small_value_batch(self, n):
         ray.get([small_value.remote() for _ in range(n)])
 
 
+@ray.remote
+class AsyncActor:
+    async def small_value(self):
+        return b"ok"
+
+    async def small_value_with_arg(self, x):
+        return b"ok"
+
+    async def small_value_batch(self, n):
+        await asyncio.wait([small_value.remote() for _ in range(n)])
+
+
 @ray.remote(num_cpus=0)
 class Client:
     def __init__(self, servers):
@@ -190,6 +203,51 @@ def actor_multi2_direct_arg():
     timeit("n:n actor calls with arg async", actor_multi2_direct_arg,
            n * len(clients))
 
+    a = AsyncActor.remote()
+
+    def actor_sync():
+        ray.get(a.small_value.remote())
+
+    timeit("1:1 async-actor calls sync", actor_sync)
+
+    a = AsyncActor.remote()
+
+    def async_actor():
+        ray.get([a.small_value.remote() for _ in range(1000)])
+
+    timeit("1:1 async-actor calls async", async_actor, 1000)
+
+    a = AsyncActor.remote()
+
+    def async_actor():
+        ray.get([a.small_value_with_arg.remote(i) for i in range(1000)])
+
+    timeit("1:1 async-actor calls with args async", async_actor, 1000)
+
+    n = 5000
+    n_cpu = multiprocessing.cpu_count() // 2
+    actors = [AsyncActor.remote() for _ in range(n_cpu)]
+    client = Client.remote(actors)
+
+    def async_actor_async():
+        ray.get(client.small_value_batch.remote(n))
+
+    timeit("1:n async-actor calls async", async_actor_async, n * len(actors))
+
+    n = 5000
+    m = 4
+    n_cpu = multiprocessing.cpu_count() // 2
+    a = [AsyncActor.remote() for _ in range(n_cpu)]
+
+    @ray.remote
+    def async_actor_work(actors):
+        ray.get([actors[i % n_cpu].small_value.remote() for i in range(n)])
+
+    def async_actor_multi():
+        ray.get([async_actor_work.remote(a) for _ in range(m)])
+
+    timeit("n:n async-actor calls async", async_actor_multi, m * n)
+
 
 if __name__ == "__main__":
     main()