diff --git a/jax/_src/checkify.py b/jax/_src/checkify.py
index 13b0323c4d07..11196ca3fc23 100644
--- a/jax/_src/checkify.py
+++ b/jax/_src/checkify.py
@@ -627,9 +627,9 @@ def dynamic_slice_error_check(error, enabled_errors, operand, *start_indices, sl
   if OOBError not in enabled_errors:
     return error, out
 
-  operand_dims = np.array(operand.shape)
-  slice_sizes = np.array(slice_sizes)
   start_indices = jnp.array(start_indices)
+  operand_dims = np.array(operand.shape, dtype=start_indices.dtype)
+  slice_sizes = np.array(slice_sizes, dtype=start_indices.dtype)
   oob_mask = (start_indices < 0) | (start_indices + slice_sizes > operand_dims)
 
   payload = oob_payload(oob_mask, start_indices, range(operand.ndim), operand.shape)
diff --git a/jax/_src/numpy/indexing.py b/jax/_src/numpy/indexing.py
index aa5f5d3a7626..a4c91cef5211 100644
--- a/jax/_src/numpy/indexing.py
+++ b/jax/_src/numpy/indexing.py
@@ -606,7 +606,8 @@ def _attempt_rewriting_take_via_slice(arr: Array, idx: Any, mode: str | None,
     # We must be careful with dtypes because dynamic_slice requires all
     # start indices to have matching types.
     if len(start_indices) > 1:
-      start_indices = util.promote_dtypes(*start_indices)
+      index_dtype = lax_utils.int_dtype_for_shape(arr.shape, signed=True)
+      start_indices = [lax.convert_element_type(idx, index_dtype) for idx in start_indices]
     jnp_error._check_precondition_oob_dynamic_slice(
         arr.shape, start_indices, slice_sizes, allow_negative_indices
     )
diff --git a/tests/lax_numpy_indexing_test.py b/tests/lax_numpy_indexing_test.py
index a58daa464d84..d9be101ac787 100644
--- a/tests/lax_numpy_indexing_test.py
+++ b/tests/lax_numpy_indexing_test.py
@@ -1291,6 +1291,14 @@ def testWrongNumberOfIndices(self):
         "Too many indices: 1-dimensional array indexed with 2 regular indices."):
       jnp.zeros(3)[:, 5]
 
+  @jtu.sample_product(shape=[(), (1,)])
+  def testIndexDtypePromotion(self, shape):
+    # Regression test for https://github.com/jax-ml/jax/issues/31396
+    numbers = jnp.arange(1000)[:, None]
+    idx = jnp.int8(0).reshape(shape)
+    expected = np.array(999).reshape(shape)
+    self.assertArraysEqual(numbers[999, idx], expected)
+
 
 def _broadcastable_shapes(shape):
   """Returns all shapes that broadcast to `shape`."""