diff --git a/test/test_dtype.py b/test/test_dtype.py
index 231dd704c9..051590c24e 100644
--- a/test/test_dtype.py
+++ b/test/test_dtype.py
@@ -1,3 +1,4 @@
+import operator
 import unittest
 import numpy as np
 import torch
@@ -7,6 +8,9 @@ from tinygrad.tensor import Tensor, dtypes
 from typing import Any, List
 from hypothesis import given, settings, strategies as st
 
+core_types = list(DTYPES_DICT.values())
+floats = [dt for dt in core_types if dtypes.is_float(dt)]
+
 def is_dtype_supported(dtype: DType, device: str = Device.DEFAULT):
   # for GPU, cl_khr_fp16 isn't supported
   # for LLVM, it segfaults because it can't link to the casting function
@@ -305,8 +309,10 @@ class TestTypeSpec(unittest.TestCase):
     assert Tensor.arange(3, 9, 0.7).dtype == dtypes.default_float
     assert Tensor.arange(3, 8.5, 3).dtype == dtypes.default_float
 
-core_types = list(DTYPES_DICT.values())
-floats = [dt for dt in core_types if dtypes.is_float(dt)]
+  @given(st.sampled_from(core_types), st.sampled_from([operator.gt, operator.ge, operator.le, operator.lt, operator.eq, operator.ne]))
+  def test_bool_ops(self, dtype, op):
+    assert op(Tensor.rand(4, 4, dtype=dtype), Tensor.rand(4, 4, dtype=dtype)).dtype == dtypes.bool
+
 class TestTypePromotion(unittest.TestCase):
   @given(st.sampled_from(core_types))
   def test_self_promo_to_self(self, dtype):