diff --git a/jax/_src/numpy/lax_numpy.py b/jax/_src/numpy/lax_numpy.py
index 7a333f424..51839c734 100644
--- a/jax/_src/numpy/lax_numpy.py
+++ b/jax/_src/numpy/lax_numpy.py
@@ -3072,7 +3072,7 @@ def apply_over_axes(func: Callable[[ArrayLike, int], Array], a: ArrayLike,
 
 
 @util._wraps(np.dot, lax_description=_PRECISION_DOC)
-@partial(jit, static_argnames=('precision',), inline=True)
+@partial(jit, static_argnames=('precision', 'preferred_element_type'), inline=True)
 def dot(a: ArrayLike, b: ArrayLike, *,
         precision: PrecisionLike = None,
         preferred_element_type: DTypeLike | None = None) -> Array:
@@ -3104,7 +3104,7 @@ def dot(a: ArrayLike, b: ArrayLike, *,
 
 
 @util._wraps(np.matmul, module='numpy', lax_description=_PRECISION_DOC)
-@partial(jit, static_argnames=('precision',), inline=True)
+@partial(jit, static_argnames=('precision', 'preferred_element_type'), inline=True)
 def matmul(a: ArrayLike, b: ArrayLike, *,
            precision: PrecisionLike = None,
            preferred_element_type: DTypeLike | None = None,
diff --git a/jax/_src/test_util.py b/jax/_src/test_util.py
index 7ed09de59..afabedc2d 100644
--- a/jax/_src/test_util.py
+++ b/jax/_src/test_util.py
@@ -741,6 +741,13 @@ def assert_dot_precision(expected_precision, fun, *args):
     else:
       assert precision == expected_precision, msg
 
+def assert_dot_preferred_element_type(expected, fun, *args, **kwargs):
+  jaxpr = api.make_jaxpr(partial(fun, **kwargs))(*args)
+  pref_eltypes = [eqn.params['preferred_element_type'] for eqn in iter_eqns(jaxpr.jaxpr)
+                   if eqn.primitive == lax.dot_general_p]
+  for pref_eltype in pref_eltypes:
+    msg = f"Unexpected preferred_element_type: {expected} != {pref_eltype}"
+    assert expected == pref_eltype, msg
 
 def cases_from_gens(*gens):
   sizes = [1, 3, 10]
diff --git a/tests/lax_numpy_test.py b/tests/lax_numpy_test.py
index b9f88727b..b815aa77b 100644
--- a/tests/lax_numpy_test.py
+++ b/tests/lax_numpy_test.py
@@ -4889,6 +4889,24 @@ class LaxBackedNumpyTests(jtu.JaxTestCase):
         partial(jnp.inner, precision=HIGHEST),
         ones_1d, ones_1d)
 
+  @jtu.sample_product(
+      funcname=['matmul', 'dot', 'vdot', 'tensordot']
+  )
+  def testPreferredElementType(self, funcname):
+    func = getattr(jnp, funcname)
+    kwargs = dict(axes=0) if funcname == 'tensordot' else {}
+
+    ones_i32 = np.ones(2, dtype='int32')
+    ones_f32 = np.ones(2, dtype='float32')
+
+    with jax.numpy_dtype_promotion('strict'):
+      jtu.assert_dot_preferred_element_type('int32', func, ones_i32, ones_i32, **kwargs)
+      jtu.assert_dot_preferred_element_type('float32', func, ones_f32, ones_f32, **kwargs)
+      jtu.assert_dot_preferred_element_type('bfloat16', func, ones_f32, ones_f32, **kwargs,
+                                            preferred_element_type='bfloat16')
+    with jax.numpy_dtype_promotion('standard'):
+      jtu.assert_dot_preferred_element_type('float32', func, ones_i32, ones_f32, **kwargs)
+
   @jtu.sample_product(
     [dict(shape=shape, varargs=varargs, axis=axis)
         for shape in [(10,), (10, 15), (10, 15, 20)]