diff --git a/aten/src/ATen/native/cuda/UnaryGeometricTanKernel.cu b/aten/src/ATen/native/cuda/UnaryGeometricTanKernel.cu
index 34e055d589a..bedb5add839 100644
--- a/aten/src/ATen/native/cuda/UnaryGeometricTanKernel.cu
+++ b/aten/src/ATen/native/cuda/UnaryGeometricTanKernel.cu
@@ -12,14 +12,15 @@
 
 namespace at::native {
 
-#if AT_USE_JITERATOR()
+#if 0 && AT_USE_JITERATOR()
 constexpr char tan_name[] = "tan_impl";
 #endif
 
 void tan_kernel_cuda(TensorIteratorBase& iter) {
   auto common_dtype = iter.common_dtype();
   if (at::isComplexType(common_dtype)) {
-#if AT_USE_JITERATOR()
+    // Disabled due to accuracy issues
+#if 0 && AT_USE_JITERATOR()
     static const auto tan_string = jiterator_stringify(
         template <typename T> T tan_impl(T a) { return std::tan(a); });
     AT_DISPATCH_COMPLEX_TYPES_AND(
diff --git a/aten/src/ATen/native/cuda/UnaryGeometricTanhKernel.cu b/aten/src/ATen/native/cuda/UnaryGeometricTanhKernel.cu
index 61393eec8ca..dedb15473fc 100644
--- a/aten/src/ATen/native/cuda/UnaryGeometricTanhKernel.cu
+++ b/aten/src/ATen/native/cuda/UnaryGeometricTanhKernel.cu
@@ -12,14 +12,15 @@
 
 namespace at::native {
 
-#if AT_USE_JITERATOR()
+#if 0 && AT_USE_JITERATOR()
 constexpr char tanh_name[] = "tanh_impl";
 #endif
 
 void tanh_kernel_cuda(TensorIteratorBase& iter) {
   auto common_dtype = iter.common_dtype();
   if (at::isComplexType(common_dtype)) {
-#if AT_USE_JITERATOR()
+    // Disabled due to accuracy issues
+#if 0 && AT_USE_JITERATOR()
     static const auto tanh_string = jiterator_stringify(
         template <typename T> T tanh_impl(T a) { return std::tanh(a); });
     AT_DISPATCH_COMPLEX_TYPES_AND(
diff --git a/test/test_unary_ufuncs.py b/test/test_unary_ufuncs.py
index 15b967e5707..13f205c4d11 100644
--- a/test/test_unary_ufuncs.py
+++ b/test/test_unary_ufuncs.py
@@ -773,6 +773,48 @@ class TestUnaryUfuncs(TestCase):
                     with self.assertRaises(AttributeError):
                         torch_inplace_method = getattr(torch.Tensor, fn_name + "_")
 
+    @onlyCUDA
+    @dtypes(torch.complex64)
+    def test_tan_complex_cuda_matches_numpy(self, device, dtype):
+        # Focused accuracy check for complex tan on CUDA against NumPy reference
+        # Includes values near tan singularities on the real axis
+        eps = 1e-3
+        specials = torch.tensor(
+            [
+                math.pi / 2 - eps,
+                math.pi / 2 + eps,
+                -math.pi / 2 - eps,
+                -math.pi / 2 + eps,
+            ],
+            device=device,
+            dtype=torch.float32,
+        )
+        real = torch.randn(1024, device=device, dtype=torch.float32) * (2 * math.pi)
+        imag = torch.randn(1024, device=device, dtype=torch.float32) * 5.0
+        real = torch.cat([real, specials])
+        imag = torch.cat(
+            [
+                imag,
+                torch.linspace(
+                    -3,
+                    3,
+                    steps=specials.numel(),
+                    device=device,
+                ),
+            ]
+        )
+        z = torch.complex(real, imag).to(dtype)
+        self.compare_with_numpy(torch.tan, np.tan, z)
+
+    @onlyCUDA
+    @dtypes(torch.complex64)
+    def test_tanh_complex_cuda_matches_numpy(self, device, dtype):
+        # Focused accuracy check for complex tanh on CUDA against NumPy reference
+        real = torch.randn(2048, device=device, dtype=torch.float32) * (2 * math.pi)
+        imag = torch.randn(2048, device=device, dtype=torch.float32) * 5.0
+        z = torch.complex(real, imag).to(dtype)
+        self.compare_with_numpy(torch.tanh, np.tanh, z)
+
     def check_internal_mem_overlap(
         self, inplace_op, num_inputs, dtype, device, expected_failure=False
     ):