diff --git a/test/test_dtype.py b/test/test_dtype.py
index e1d4e3166b..fb50a1713e 100644
--- a/test/test_dtype.py
+++ b/test/test_dtype.py
@@ -1,6 +1,7 @@
 import unittest
 import numpy as np
 import torch
+import operator
 from tinygrad.helpers import CI, DTYPES_DICT, getenv, DType, DEBUG, ImageDType, PtrDType, OSX, least_upper_float, temp, least_upper_dtype
 from tinygrad import Device
 from tinygrad.tensor import Tensor, dtypes
@@ -9,7 +10,6 @@ from hypothesis import given, settings, strategies as st
 
 core_dtypes = list(DTYPES_DICT.values())
 floats = [dt for dt in core_dtypes 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
@@ -308,6 +308,11 @@ 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
 
+  @unittest.skipIf(Device.DEFAULT == "WEBGPU", "WEBGPU doesn't follow the bool ops spec")
+  @given(st.sampled_from(core_dtypes), 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
+
   @given(st.sampled_from(core_dtypes),
          st.sampled_from([dtypes.int8,dtypes.int16,dtypes.int32,dtypes.int64]), st.sampled_from([dtypes.float16,dtypes.float32,dtypes.float64]))
   def test_functions_return_index(self, dtype, default_int, default_float):