diff --git a/mlx/primitives.cpp b/mlx/primitives.cpp
index 92e54f99..5ac2548f 100644
--- a/mlx/primitives.cpp
+++ b/mlx/primitives.cpp
@@ -1863,7 +1863,7 @@ std::vector<array> Equal::jvp(
     const std::vector<array>& tangents,
     const std::vector<int>& argnums) {
   auto shape = broadcast_shapes(primals[0].shape(), primals[1].shape());
-  return {zeros(shape, bool_, stream())};
+  return {zeros(shape, tangents[0].dtype(), stream())};
 }
 
 std::vector<array> Erf::vjp(
@@ -2530,7 +2530,7 @@ std::vector<array> Greater::jvp(
     const std::vector<array>& tangents,
     const std::vector<int>& argnums) {
   auto shape = broadcast_shapes(primals[0].shape(), primals[1].shape());
-  return {zeros(shape, bool_, stream())};
+  return {zeros(shape, tangents[0].dtype(), stream())};
 }
 
 std::pair<std::vector<array>, std::vector<int>> GreaterEqual::vmap(
@@ -2557,7 +2557,7 @@ std::vector<array> GreaterEqual::jvp(
     const std::vector<array>& tangents,
     const std::vector<int>& argnums) {
   auto shape = broadcast_shapes(primals[0].shape(), primals[1].shape());
-  return {zeros(shape, bool_, stream())};
+  return {zeros(shape, tangents[0].dtype(), stream())};
 }
 
 std::vector<array> Imag::vjp(
@@ -2614,7 +2614,7 @@ std::vector<array> Less::jvp(
     const std::vector<array>& tangents,
     const std::vector<int>& argnums) {
   auto shape = broadcast_shapes(primals[0].shape(), primals[1].shape());
-  return {zeros(shape, bool_, stream())};
+  return {zeros(shape, tangents[0].dtype(), stream())};
 }
 
 std::pair<std::vector<array>, std::vector<int>> LessEqual::vmap(
@@ -2641,7 +2641,7 @@ std::vector<array> LessEqual::jvp(
     const std::vector<array>& tangents,
     const std::vector<int>& argnums) {
   auto shape = broadcast_shapes(primals[0].shape(), primals[1].shape());
-  return {zeros(shape, bool_, stream())};
+  return {zeros(shape, tangents[0].dtype(), stream())};
 }
 
 std::vector<array> Log::vjp(
@@ -3188,7 +3188,7 @@ std::vector<array> NotEqual::jvp(
     const std::vector<array>& tangents,
     const std::vector<int>& argnums) {
   auto shape = broadcast_shapes(primals[0].shape(), primals[1].shape());
-  return {zeros(shape, bool_, stream())};
+  return {zeros(shape, tangents[0].dtype(), stream())};
 }
 
 std::vector<array> Pad::vjp(
diff --git a/python/tests/test_autograd.py b/python/tests/test_autograd.py
index c37161a4..c0609db9 100644
--- a/python/tests/test_autograd.py
+++ b/python/tests/test_autograd.py
@@ -30,6 +30,24 @@ class TestAutograd(mlx_tests.MLXTestCase):
         self.assertEqual(out[0].item(), 4.0 * 1.0 + 2.0 * 3.0)
         self.assertEqual(out[1].item(), 4.0 * 1.0 + 6.0 * 3.0)
 
+    def test_jvp_comparison_tangent_dtype(self):
+        # Comparison op JVP tangents should preserve the input tangent's
+        # dtype (e.g. float32), not return bool. Using bool tangents causes
+        # downstream ops like negative to crash. (issue #3081)
+        x = mx.array([1.0, -2.0, 3.0])
+        t = mx.ones_like(x)
+
+        for op in [
+            mx.greater,
+            mx.less,
+            mx.equal,
+            mx.greater_equal,
+            mx.less_equal,
+            mx.not_equal,
+        ]:
+            _, tangents = mx.jvp(lambda x, _op=op: _op(x, 0.0), [x], [t])
+            self.assertEqual(tangents[0].dtype, mx.float32)
+
     def test_vjp(self):
         fun = lambda x: 2 * x
         out, dout = mx.vjp(fun, [mx.array(1.0)], [mx.array(2.0)])