diff --git a/neuralnetworks/1.2/IDevice.hal b/neuralnetworks/1.2/IDevice.hal
index da9a966..d83f9e6 100644
--- a/neuralnetworks/1.2/IDevice.hal
+++ b/neuralnetworks/1.2/IDevice.hal
@@ -76,6 +76,17 @@
     getType() generates (ErrorStatus status, DeviceType type);
 
     /**
+     * Gets the capabilities of a driver.
+     *
+     * @return status Error status of the call, must be:
+     *                - NONE if successful
+     *                - DEVICE_UNAVAILABLE if driver is offline or busy
+     *                - GENERAL_FAILURE if there is an unspecified error
+     * @return capabilities Capabilities of the driver.
+     */
+    getCapabilities_1_2() generates (ErrorStatus status, Capabilities capabilities);
+
+    /**
      * Gets information about extensions supported by the driver implementation.
      *
      * All extension operations and operands must be fully supported for the
diff --git a/neuralnetworks/1.2/types.hal b/neuralnetworks/1.2/types.hal
index 28d0119..7691642 100644
--- a/neuralnetworks/1.2/types.hal
+++ b/neuralnetworks/1.2/types.hal
@@ -4618,6 +4618,39 @@
 };
 
 /**
+ * The capabilities of a driver.
+ *
+ * Performance of an operation comes from the type of its first operand.
+ * This represents performance for non extension operand types.
+ */
+struct Capabilities {
+    /**
+     * Driver performance when operating on float32 data but performing
+     * calculations with range and/or precision as low as that of the IEEE
+     * 754 16-bit floating-point format.
+     */
+    PerformanceInfo relaxedFloat32toFloat16PerformanceScalar;
+    PerformanceInfo relaxedFloat32toFloat16PerformanceTensor;
+
+    /**
+     * Driver performance when operating on a particular data type.
+     * In the case of float32 data, this is used when the calculations
+     * are not relaxed.
+     */
+    struct OperandPerformance {
+        OperandType type;
+        PerformanceInfo info;
+    };
+
+    /**
+     * Performance by operand type. Must be sorted by OperandType.
+     * If a particular OperandType is not present in operandPerformance,
+     * its performance is treated as { .execTime = FLT_MAX, .powerUsage = FLT_MAX }.
+     */
+    vec<OperandPerformance> operandPerformance;
+};
+
+/**
  * Describes one operation of the model's graph.
  */
 struct Operation {
diff --git a/neuralnetworks/1.2/vts/functional/BasicTests.cpp b/neuralnetworks/1.2/vts/functional/BasicTests.cpp
index 6fb16c2..5c269df 100644
--- a/neuralnetworks/1.2/vts/functional/BasicTests.cpp
+++ b/neuralnetworks/1.2/vts/functional/BasicTests.cpp
@@ -25,7 +25,7 @@
 namespace vts {
 namespace functional {
 
-using V1_1::Capabilities;
+using V1_0::PerformanceInfo;
 
 // create device test
 TEST_F(NeuralnetworksHidlTest, CreateDevice) {}
@@ -37,6 +37,31 @@
     EXPECT_EQ(DeviceStatus::AVAILABLE, static_cast<DeviceStatus>(status));
 }
 
+// initialization
+TEST_F(NeuralnetworksHidlTest, GetCapabilitiesTest) {
+    using OperandPerformance = Capabilities::OperandPerformance;
+    Return<void> ret = device->getCapabilities_1_2([](ErrorStatus status,
+                                                      const Capabilities& capabilities) {
+        EXPECT_EQ(ErrorStatus::NONE, status);
+
+        auto isPositive = [](const PerformanceInfo& perf) {
+            return perf.execTime > 0.0f && perf.powerUsage > 0.0f;
+        };
+
+        EXPECT_TRUE(isPositive(capabilities.relaxedFloat32toFloat16PerformanceScalar));
+        EXPECT_TRUE(isPositive(capabilities.relaxedFloat32toFloat16PerformanceTensor));
+        const auto& opPerf = capabilities.operandPerformance;
+        EXPECT_TRUE(std::all_of(
+                opPerf.begin(), opPerf.end(),
+                [isPositive](const OperandPerformance& a) { return isPositive(a.info); }));
+        EXPECT_TRUE(std::is_sorted(opPerf.begin(), opPerf.end(),
+                                   [](const OperandPerformance& a, const OperandPerformance& b) {
+                                       return a.type < b.type;
+                                   }));
+    });
+    EXPECT_TRUE(ret.isOk());
+}
+
 // device version test
 TEST_F(NeuralnetworksHidlTest, GetDeviceVersionStringTest) {
     Return<void> ret = device->getVersionString([](ErrorStatus status, const hidl_string& version) {