diff --git a/tensorflow/lite/delegates/gpu/cl/kernels/elementwise.cc b/tensorflow/lite/delegates/gpu/cl/kernels/elementwise.cc
index 3203ec3e7fc..a81c8be9ea1 100644
--- a/tensorflow/lite/delegates/gpu/cl/kernels/elementwise.cc
+++ b/tensorflow/lite/delegates/gpu/cl/kernels/elementwise.cc
@@ -58,6 +58,9 @@ std::string GetOneInputCode(const OperationType& op_type,
     case OperationType::LOG:
       result = "$0 = log($0);\n";
       break;
+    case OperationType::NEG:
+      result = "$0 = -($0);\n";
+      break;
     case OperationType::RSQRT:
       result = "$0 = rsqrt($0);\n";
       break;
diff --git a/tensorflow/lite/delegates/gpu/cl/kernels/elementwise_test.cc b/tensorflow/lite/delegates/gpu/cl/kernels/elementwise_test.cc
index d883a734214..b6b501bd11b 100644
--- a/tensorflow/lite/delegates/gpu/cl/kernels/elementwise_test.cc
+++ b/tensorflow/lite/delegates/gpu/cl/kernels/elementwise_test.cc
@@ -208,6 +208,30 @@ TEST_F(OpenCLOperationTest, Log) {
   }
 }
 
+TEST_F(OpenCLOperationTest, Neg) {
+  TensorFloat32 src_tensor;
+  src_tensor.shape = BHWC(1, 2, 1, 2);
+  src_tensor.data = {1.0f, -2.0f, 0.0f, 4.0f};
+
+  for (auto storage : env_.GetSupportedStorages()) {
+    for (auto precision : env_.GetSupportedPrecisions()) {
+      const float eps = precision == CalculationsPrecision::F32 ? 1e-6f : 1e-3f;
+      OperationDef op_def;
+      op_def.precision = precision;
+      auto data_type = DeduceDataTypeFromPrecision(precision);
+      op_def.src_tensors.push_back({data_type, storage, Layout::HWC});
+      op_def.dst_tensors.push_back({data_type, storage, Layout::HWC});
+      TensorFloat32 dst_tensor;
+      GPUOperation operation =
+          CreateElementwiseOneInput(op_def, OperationType::NEG);
+      ASSERT_OK(ExecuteGPUOperation(src_tensor, creation_context_, &operation,
+                                    BHWC(1, 2, 1, 2), &dst_tensor));
+      EXPECT_THAT(dst_tensor.data,
+                  Pointwise(FloatNear(eps), {-1.0f, 2.0f, 0.0f, -4.0f}));
+    }
+  }
+}
+
 TEST_F(OpenCLOperationTest, Rsqrt) {
   TensorFloat32 src_tensor;
   src_tensor.shape = BHWC(1, 2, 1, 2);
diff --git a/tensorflow/lite/delegates/gpu/cl/selectors/operation_selector.cc b/tensorflow/lite/delegates/gpu/cl/selectors/operation_selector.cc
index 1553f35a60e..d259e989dd9 100644
--- a/tensorflow/lite/delegates/gpu/cl/selectors/operation_selector.cc
+++ b/tensorflow/lite/delegates/gpu/cl/selectors/operation_selector.cc
@@ -336,6 +336,7 @@ absl::Status GPUOperationFromNode(const DeviceInfo& device_info,
     case OperationType::EXP:
     case OperationType::HARD_SWISH:
     case OperationType::LOG:
+    case OperationType::NEG:
     case OperationType::RSQRT:
     case OperationType::SIGMOID:
     case OperationType::SIN:
diff --git a/tensorflow/lite/delegates/gpu/common/model_builder.cc b/tensorflow/lite/delegates/gpu/common/model_builder.cc
index 0ab9e96aa7b..598e41a0fdc 100644
--- a/tensorflow/lite/delegates/gpu/common/model_builder.cc
+++ b/tensorflow/lite/delegates/gpu/common/model_builder.cc
@@ -801,6 +801,7 @@ class ElementwiseOperationParser : public TFLiteOperationParser {
       case OperationType::ELU:
       case OperationType::EXP:
       case OperationType::LOG:
+      case OperationType::NEG:
       case OperationType::RSQRT:
       case OperationType::SIGMOID:
       case OperationType::SIN:
@@ -2574,6 +2575,8 @@ std::unique_ptr<TFLiteOperationParser> NewOperationParser(
       return std::make_unique<PadOperationParser>(/*mirror_pad=*/true);
     case kTfLiteBuiltinMul:
       return std::make_unique<MulOperationParser>();
+    case kTfLiteBuiltinNeg:
+      return std::make_unique<ElementwiseOperationParser>(OperationType::NEG);
     case kTfLiteBuiltinPad:
       return std::make_unique<PadOperationParser>(/*mirror_pad=*/false);
     case kTfLiteBuiltinPow:
diff --git a/tensorflow/lite/delegates/gpu/common/operations.cc b/tensorflow/lite/delegates/gpu/common/operations.cc
index 94e1f0ad5e6..19d7bd919c5 100644
--- a/tensorflow/lite/delegates/gpu/common/operations.cc
+++ b/tensorflow/lite/delegates/gpu/common/operations.cc
@@ -134,6 +134,8 @@ std::string ToString(enum OperationType op) {
       return "minimum";
     case OperationType::MUL:
       return "mul";
+    case OperationType::NEG:
+      return "neg";
     case OperationType::NOT_EQUAL:
       return "not_equal";
     case OperationType::PAD:
@@ -224,6 +226,7 @@ OperationType OperationTypeFromString(const std::string& name) {
            OperationType::MEAN_STDDEV_NORMALIZATION},
           {"minimum", OperationType::MINIMUM},
           {"mul", OperationType::MUL},
+          {"neg", OperationType::NEG},
           {"not_equal", OperationType::NOT_EQUAL},
           {"pad", OperationType::PAD},
           {"pooling_2d", OperationType::POOLING_2D},
diff --git a/tensorflow/lite/delegates/gpu/common/operations.h b/tensorflow/lite/delegates/gpu/common/operations.h
index 968f75e155b..a93f63a02b7 100644
--- a/tensorflow/lite/delegates/gpu/common/operations.h
+++ b/tensorflow/lite/delegates/gpu/common/operations.h
@@ -63,6 +63,7 @@ enum class OperationType {
   MEAN_STDDEV_NORMALIZATION,
   MINIMUM,
   MUL,
+  NEG,
   NOT_EQUAL,
   PAD,
   POOLING_2D,
diff --git a/tensorflow/lite/delegates/gpu/gl/kernels/elementwise.cc b/tensorflow/lite/delegates/gpu/gl/kernels/elementwise.cc
index 5d50fcc0118..42dafe7597e 100644
--- a/tensorflow/lite/delegates/gpu/gl/kernels/elementwise.cc
+++ b/tensorflow/lite/delegates/gpu/gl/kernels/elementwise.cc
@@ -69,6 +69,9 @@ class ElementwiseOneArgument : public NodeShader {
             value_0.w = value_0.w > 0.0 ? log(value_0.w) : nan;
         )";
         break;
+      case OperationType::NEG:
+        source = "value_0 = -(value_0);";
+        break;
       case OperationType::RSQRT:
         source = R"(
             const float nan = normalize(vec4(0, 0, 0, 0)).x;
diff --git a/tensorflow/lite/delegates/gpu/gl/kernels/elementwise_test.cc b/tensorflow/lite/delegates/gpu/gl/kernels/elementwise_test.cc
index a32a4ea9f76..5ff7bfc9ed7 100644
--- a/tensorflow/lite/delegates/gpu/gl/kernels/elementwise_test.cc
+++ b/tensorflow/lite/delegates/gpu/gl/kernels/elementwise_test.cc
@@ -129,6 +129,18 @@ TEST(ElementwiseOneArgumentTest, Log) {
               Pointwise(FloatNear(1e-6), {0.0, 1.14473, 0.0, 0.0}));
 }
 
+TEST(ElementwiseOneArgumentTest, Neg) {
+  OperationType op_type = OperationType::NEG;
+  const BHWC shape(1, 2, 2, 1);
+  SingleOpModel model({/*type=*/ToString(op_type), /*attributes=*/{}},
+                      /*inputs=*/{GetTensorRef(0, shape)},
+                      /*outputs=*/{GetTensorRef(1, shape)});
+  ASSERT_TRUE(model.PopulateTensor(0, {1.0, -3.1415926, 0.0, 1.0}));
+  ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
+  EXPECT_THAT(model.GetOutput(0),
+              Pointwise(FloatNear(1e-6), {-1.0, 3.1415926, 0.0, -1.0}));
+}
+
 TEST(ElementwiseOneArgumentTest, Rsqrt) {
   OperationType op_type = OperationType::RSQRT;
   const BHWC shape(1, 2, 2, 1);
diff --git a/tensorflow/lite/delegates/gpu/gl/kernels/registry.cc b/tensorflow/lite/delegates/gpu/gl/kernels/registry.cc
index 645e5b6c728..e6cd539500e 100644
--- a/tensorflow/lite/delegates/gpu/gl/kernels/registry.cc
+++ b/tensorflow/lite/delegates/gpu/gl/kernels/registry.cc
@@ -103,6 +103,7 @@ class Registry : public NodeShader {
     insert_elementwise_op(Type::EXP);
     insert_elementwise_op(Type::HARD_SWISH);
     insert_elementwise_op(Type::LOG);
+    insert_elementwise_op(Type::NEG);
     insert_elementwise_op(Type::MAXIMUM);
     insert_elementwise_op(Type::MINIMUM);
     insert_elementwise_op(Type::POW);
diff --git a/tensorflow/lite/delegates/gpu/metal/api.cc b/tensorflow/lite/delegates/gpu/metal/api.cc
index 5773457b642..3359174e9f8 100644
--- a/tensorflow/lite/delegates/gpu/metal/api.cc
+++ b/tensorflow/lite/delegates/gpu/metal/api.cc
@@ -371,6 +371,7 @@ absl::Status RegisterPrimaryOps(const GraphFloat32& graph, const Node* node,
     case OperationType::EXP:
     case OperationType::HARD_SWISH:
     case OperationType::LOG:
+    case OperationType::NEG:
     case OperationType::RSQRT:
     case OperationType::SIGMOID:
     case OperationType::SIN:
diff --git a/tensorflow/lite/delegates/gpu/metal/kernels/elementwise.cc b/tensorflow/lite/delegates/gpu/metal/kernels/elementwise.cc
index 9edfc884638..fb4de84be8e 100644
--- a/tensorflow/lite/delegates/gpu/metal/kernels/elementwise.cc
+++ b/tensorflow/lite/delegates/gpu/metal/kernels/elementwise.cc
@@ -45,6 +45,7 @@ std::string OneInputFunctor(OperationType op_type, const std::string& value) {
        "$0.w < FLT(0.0f) ? exp($0.w) - FLT(1.0f) : $0.w)"},
       {OperationType::EXP, "exp($0)"},
       {OperationType::LOG, "log($0)"},
+      {OperationType::NEG, "-($0)"},
       {OperationType::SQRT, "sqrt($0)"},
       {OperationType::RSQRT, "1.0 / sqrt($0)"},
       {OperationType::SQUARE, "$0 * $0"},
diff --git a/tensorflow/lite/delegates/gpu/metal/kernels/elementwise_test.mm b/tensorflow/lite/delegates/gpu/metal/kernels/elementwise_test.mm
index 4972fdeb1a9..867ed596ed8 100644
--- a/tensorflow/lite/delegates/gpu/metal/kernels/elementwise_test.mm
+++ b/tensorflow/lite/delegates/gpu/metal/kernels/elementwise_test.mm
@@ -257,6 +257,19 @@ TensorRef<BHWC> GetTensorRef(int ref, const BHWC& shape) {
   XCTAssertTrue(status.ok(), @"%s", std::string(status.message()).c_str());
 }
 
+- (void)testNeg {
+  OperationType op_type = OperationType::NEG;
+  const BHWC shape(1, 2, 2, 1);
+  SingleOpModel model({/*type=*/ToString(op_type), /*attributes=*/{}},
+                      /*inputs=*/{GetTensorRef(0, shape)},
+                      /*outputs=*/{GetTensorRef(1, shape)});
+  XCTAssertTrue(model.PopulateTensor(0, {-1.0, 3.1415926, 0.0, 1.0}));
+  auto status = model.Invoke();
+  XCTAssertTrue(status.ok(), @"%s", std::string(status.message()).c_str());
+  status = CompareVectors({1.0, -3.1415926, 0.0, -1.0}, model.GetOutput(0), 1e-6f);
+  XCTAssertTrue(status.ok(), @"%s", std::string(status.message()).c_str());
+}
+
 - (void)testPow {
   OperationType op_type = OperationType::POW;
   const BHWC shape(1, 2, 2, 1);