diff --git a/graphblas/io/_networkx.py b/graphblas/io/_networkx.py
index c4a31e6db..96e587cdc 100644
--- a/graphblas/io/_networkx.py
+++ b/graphblas/io/_networkx.py
@@ -96,11 +96,13 @@ def from_networkx(G, nodelist=None, dtype=None, weight="weight", name=None):
         dup_op = plus
 
     values = np.array(vals, dtype=dtype)
-    if dtype is None and values.dtype == np.int32:  # pragma: no cover (win64 numpy < 2)
-        # numpy < 2 infers the platform C long for a sequence of Python ints, which
-        # is 32-bit on Windows. values_to_numpy_buffer widens the same way for
-        # non-numpy input, so this keeps from_networkx agreeing with
-        # Matrix.from_coo on INT64 for an unweighted graph on every platform.
+    if dtype is None and values.dtype == np.int32:
+        # ``vals`` is a Python sequence, and from_coo widens an int32 dtype
+        # INFERRED from sequence input to int64 (values_to_numpy_buffer): python
+        # ints infer int32 on win64 with numpy < 2, and np.int32 scalar weights
+        # infer it on every platform. Widen the same way so from_networkx and
+        # Matrix.from_coo given the same weights agree on the result dtype.
+        # An explicit dtype= is applied above and never reaches this branch.
         values = values.astype(np.int64)
     if values.ndim != 1 or values.dtype.kind not in "biufc":
         # Defer to scipy so the error matches the previous behavior exactly.
diff --git a/graphblas/tests/test_io.py b/graphblas/tests/test_io.py
index e5eb0020d..f9eb79b70 100644
--- a/graphblas/tests/test_io.py
+++ b/graphblas/tests/test_io.py
@@ -176,6 +176,28 @@ def test_from_networkx_undirected():
     assert M_none.isequal(expected_none, check_dtype=True)
 
 
+@pytest.mark.skipif("not nx")
+def test_from_networkx_int32_weights_widen_like_from_coo():
+    # np.int32 scalar weights infer an int32 array from the edge sequence on
+    # every platform (python ints do too on win64 with numpy < 2), and from_coo
+    # widens int32 inferred from sequence input to INT64. from_networkx must
+    # widen the same way or identical weights would produce different dtypes
+    # depending on which constructor they came through.
+    G = nx.DiGraph()
+    G.add_weighted_edges_from([(0, 1, np.int32(2)), (1, 0, np.int32(3))])
+    M = gb.io.from_networkx(G)
+    expected = gb.Matrix.from_coo([0, 1], [1, 0], [np.int32(2), np.int32(3)])
+    assert expected.dtype == dtypes.INT64
+    assert M.isequal(expected, check_dtype=True)
+    # An explicit dtype bypasses inference and is preserved exactly.
+    assert gb.io.from_networkx(G, dtype="int32").dtype == dtypes.INT32
+    # Narrower ints are not special-cased; this also matches from_coo.
+    H = nx.DiGraph()
+    H.add_weighted_edges_from([(0, 1, np.int16(2))])
+    assert gb.io.from_networkx(H).dtype == dtypes.INT16
+    assert gb.Matrix.from_coo([0], [1], [np.int16(2)]).dtype == dtypes.INT16
+
+
 @pytest.mark.skipif("not nx or not ss")
 @pytest.mark.parametrize(
     "graph_cls", [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph] if nx else []