diff --git a/support_status.md b/support_status.md
index f4f736539..bf3baeb7b 100644
--- a/support_status.md
+++ b/support_status.md
@@ -27,6 +27,7 @@
 | AvgPool3D | 1 ~ 17 |
 | BatchMatMul | 1 ~ 17 |
 | BatchMatMulV2 | 1 ~ 17 |
+| BatchMatMulV3 | 1 ~ 17 |
 | BatchToSpaceND | 1 ~ 17 |
 | BiasAdd | 1 ~ 17 |
 | BiasAddV1 | 1 ~ 17 |
diff --git a/tests/test_backend.py b/tests/test_backend.py
index c2d5960ec..b9674a63b 100755
--- a/tests/test_backend.py
+++ b/tests/test_backend.py
@@ -1075,6 +1075,15 @@ def func(x, y):
             return tf.identity(x_, name=_TFOUTPUT)
         self._run_test_case(func, [_OUTPUT], {_INPUT: x_val, _INPUT1: x_val}, rtol=1e-5)
 
+    @check_tf_min_version("2.6")
+    def test_matmulinteger(self):
+        x_val = np.array([1, 2, -3, -4], dtype=np.int8).reshape((2, 2))
+        y_val = np.array([1, 2, -3, -4], dtype=np.int8).reshape((2, 2))
+        def func(x, y):
+            x_ = tf.matmul(x, y, output_type=tf.int32)
+            return tf.identity(x_, name=_TFOUTPUT)
+        self._run_test_case(func, [_OUTPUT], {_INPUT: x_val, _INPUT1: y_val})
+
     @check_onnxruntime_incompatibility("Sub")
     def test_sub(self):
         x_val = np.array([1.0, 2.0, -3.0, -4.0], dtype=np.float32).reshape((2, 2))
diff --git a/tf2onnx/onnx_opset/math.py b/tf2onnx/onnx_opset/math.py
index bdebd1fa6..719a5114c 100644
--- a/tf2onnx/onnx_opset/math.py
+++ b/tf2onnx/onnx_opset/math.py
@@ -363,13 +363,13 @@ def version_1(cls, ctx, node, **kwargs):
                                       name=op_name, shapes=shapes, dtypes=dtypes)
 
 
-@tf_op(["MatMul", "BatchMatMul", "BatchMatMulV2"])
+@tf_op(["MatMul", "BatchMatMul", "BatchMatMulV2", "BatchMatMulV3"])
 class MatMul:
     @classmethod
     def version_1(cls, ctx, node, **kwargs):
         # tensorflow allows transpose and conjugated. If found, insert the required transpose.
         # We could use Gemm as well but tensorflow does not pass bias in matmul.
-        node.type = "MatMul"
+        if node.type != "MatMulInteger": node.type = "MatMul"
 
         attrs = ["transpose_a", "transpose_b", "adjoint_a", "adjoint_b", "adj_x", "adj_y"]
         attrs_val = [node.get_attr(attr) for attr in attrs]
@@ -408,7 +408,19 @@ def version_1(cls, ctx, node, **kwargs):
             val = node.get_attr(i)
             if val is not None and val.i != 0:
                 raise ValueError(node.type + " attribute " + i + " is not supported")
-
+    @classmethod
+    def version_10(cls, ctx, node, **kwargs):
+        if (ctx.get_dtype(node.input[0]) in [onnx_pb.TensorProto.INT8, onnx_pb.TensorProto.UINT8] and
+                ctx.get_dtype(node.input[1]) in [onnx_pb.TensorProto.INT8, onnx_pb.TensorProto.UINT8] and
+                ctx.get_dtype(node.output[0]) == onnx_pb.TensorProto.INT32):
+            node.type = "MatMulInteger"
+            zpdata_a = np.zeros(1, dtype=utils.map_onnx_to_numpy_type(ctx.get_dtype(node.input[0])))
+            zero_point_node_a = ctx.make_const(utils.make_name("zero_point_a"), zpdata_a)
+            zpdata_b = np.zeros(1, dtype=utils.map_onnx_to_numpy_type(ctx.get_dtype(node.input[1])))
+            zero_point_node_b = ctx.make_const(utils.make_name("zero_point_b"), zpdata_b)
+            ctx.replace_inputs(node, [node.input[0], node.input[1],
+                                      zero_point_node_a.output[0], zero_point_node_b.output[0]])
+        cls.version_1(ctx, node, **kwargs)
 
 @tf_op("Erf")
 class Erf: