diff --git a/python/tvm/relax/frontend/onnx/onnx_frontend.py b/python/tvm/relax/frontend/onnx/onnx_frontend.py
index 7e8616f65f7b..8a5eec070412 100644
--- a/python/tvm/relax/frontend/onnx/onnx_frontend.py
+++ b/python/tvm/relax/frontend/onnx/onnx_frontend.py
@@ -728,10 +728,10 @@ class Mod(BinaryBase):
     @classmethod
     def _impl_v10(cls, bb, inputs, attr, params):
         if attr.get("fmod", 0) == 0:
-            cls.numpy_op = _np.fmod
+            cls.numpy_op = _np.mod
             cls.relax_op = relax.op.floor_mod
         else:
-            cls.numpy_op = _np.mod
+            cls.numpy_op = _np.fmod
             cls.relax_op = relax.op.mod
         return cls.base_impl(bb, inputs, attr, params)
 
diff --git a/tests/python/relax/test_frontend_onnx.py b/tests/python/relax/test_frontend_onnx.py
index 09f3a7b01d88..c7a1d4f2390c 100644
--- a/tests/python/relax/test_frontend_onnx.py
+++ b/tests/python/relax/test_frontend_onnx.py
@@ -362,7 +362,7 @@ def verify_binary_scalar(op_name, attrs={}, domain=None, dtype=TensorProto.INT32
         "Mul": np.multiply,
         "Div": np.divide,
         "Pow": np.power,
-        "Mod": np.mod if attrs.get("fmod", 0) else np.fmod,
+        "Mod": np.fmod if attrs.get("fmod", 0) else np.mod,
     }[op_name]
     expected_value = op(lhs, rhs).astype(dtype_str)
 
@@ -697,6 +697,31 @@ def test_mod(int_mode: bool):
     verify_binary_scalar("Mod", attrs={"fmod": fmod}, dtype=dtype)
 
 
+@pytest.mark.parametrize(
+    "fmod, dtype, a_vals, b_vals",
+    [
+        (0, TensorProto.INT32, [-5, 5, -5, 5], [3, 3, -3, -3]),
+        (1, TensorProto.INT32, [-5, 5, -5, 5], [3, 3, -3, -3]),
+        (1, TensorProto.FLOAT, [-5.5, 5.5, -5.5, 5.5], [3.0, 3.0, -3.0, -3.0]),
+    ],
+)
+def test_mod_constant_fold_negative_operands(fmod, dtype, a_vals, b_vals):
+    """Mod over two constants is folded at import time. The folded value must
+    match onnxruntime for negative operands, where integer mod (sign follows
+    divisor) and fmod (sign follows dividend) disagree."""
+    a = make_constant_node("a", dtype, [4], a_vals)
+    b = make_constant_node("b", dtype, [4], b_vals)
+    mod_node = helper.make_node("Mod", ["a", "b"], ["c"], fmod=fmod)
+    graph = helper.make_graph(
+        [a, b, mod_node],
+        "mod_constant_fold_test",
+        inputs=[],
+        outputs=[helper.make_tensor_value_info("c", dtype, [4])],
+    )
+    model = helper.make_model(graph, producer_name="mod_constant_fold_test")
+    check_correctness(model)
+
+
 SHAPE_PARAMS = [
     ([[32, 32], [32, 32]], [32, 32]),
     ([[32, 1], [1, 2]], [32, 2]),