diff --git a/onnxruntime/test/providers/openvino/openvino_ep_workload_type_test.cc b/onnxruntime/test/providers/openvino/openvino_ep_workload_type_test.cc
new file mode 100644
index 0000000000000..5b9e4539f02a9
--- /dev/null
+++ b/onnxruntime/test/providers/openvino/openvino_ep_workload_type_test.cc
@@ -0,0 +1,204 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+#include <chrono>
+#include <cmath>
+#include <string>
+#include <thread>
+#include <unordered_map>
+#include <vector>
+
+#include "core/session/onnxruntime_cxx_api.h"
+#include "gtest/gtest.h"
+
+extern std::unique_ptr<Ort::Env> ort_env;
+
+constexpr const ORTCHAR_T* kSqueezeNetModelUri =
+    ORT_TSTR("testdata/squeezenet/model.onnx");
+
+class OVEPWorkloadTypeTests : public ::testing::Test {
+ protected:
+  // Check whether the NPU device can be registered at all.
+  static bool IsNPUAvailable() {
+    try {
+      Ort::SessionOptions opts;
+      std::unordered_map<std::string, std::string> ov;
+      ov["device_type"] = "NPU";
+      opts.AppendExecutionProvider_OpenVINO_V2(ov);
+      return true;
+    } catch (...) {
+      return false;
+    }
+  }
+
+  // Allow NPU resources to be fully released between tests.
+  // Without this delay the NPU driver may fail to re-initialise
+  void TearDown() override {
+    std::this_thread::sleep_for(std::chrono::milliseconds(200));
+  }
+
+  static Ort::Session CreateSqueezeNetSession(
+      Ort::SessionOptions& session_options,
+      std::unordered_map<std::string, std::string>& ov_options) {
+    session_options.SetIntraOpNumThreads(1);
+    session_options.SetGraphOptimizationLevel(
+        GraphOptimizationLevel::ORT_ENABLE_ALL);
+    session_options.AppendExecutionProvider_OpenVINO_V2(ov_options);
+
+    return Ort::Session(*ort_env, kSqueezeNetModelUri, session_options);
+  }
+
+  // Run a single inference on the SqueezeNet session and return output
+  static std::vector<float> RunSqueezeNet(Ort::Session& session,
+                                          const std::string& phase_label) {
+    Ort::AllocatorWithDefaultOptions allocator;
+    std::string input_name =
+        session.GetInputNameAllocated(0, allocator).get();
+    std::string output_name =
+        session.GetOutputNameAllocated(0, allocator).get();
+    const char* input_names[] = {input_name.c_str()};
+    const char* output_names[] = {output_name.c_str()};
+
+    // SqueezeNet input: 1 × 3 × 224 × 224 = 150 528 floats
+    std::vector<int64_t> input_shape = {1, 3, 224, 224};
+    constexpr size_t kInputSize = 1 * 3 * 224 * 224;
+    std::vector<float> input_values(kInputSize, 1.0f);
+
+    Ort::MemoryInfo mem_info =
+        Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
+
+    Ort::Value input_tensor = Ort::Value::CreateTensor<float>(
+        mem_info, input_values.data(), input_values.size(),
+        input_shape.data(), input_shape.size());
+
+    auto outputs = session.Run(Ort::RunOptions{nullptr}, input_names,
+                               &input_tensor, 1, output_names, 1);
+
+    EXPECT_EQ(outputs.size(), 1u) << phase_label;
+    if (outputs.empty()) return {};
+
+    auto type_shape = outputs[0].GetTensorTypeAndShapeInfo();
+    std::vector<int64_t> out_shape = type_shape.GetShape();
+
+    // Expected: [1, 1000, 1, 1]
+    EXPECT_EQ(out_shape.size(), 4u) << phase_label;
+    if (out_shape.size() == 4u) {
+      EXPECT_EQ(out_shape[0], 1) << phase_label;
+      EXPECT_EQ(out_shape[1], 1000) << phase_label;
+      EXPECT_EQ(out_shape[2], 1) << phase_label;
+      EXPECT_EQ(out_shape[3], 1) << phase_label;
+    }
+
+    size_t num_elements = type_shape.GetElementCount();
+    EXPECT_EQ(num_elements, 1000u) << phase_label;
+
+    const float* out_data = outputs[0].GetTensorData<float>();
+    std::vector<float> result(out_data, out_data + num_elements);
+
+    for (size_t i = 0; i < num_elements; ++i) {
+      EXPECT_TRUE(std::isfinite(result[i]))
+          << phase_label << " index " << i << " is not finite";
+    }
+
+    return result;
+  }
+
+  // Compare two output vectors element-wise within a tolerance.
+  static void CompareOutputs(const std::vector<float>& expected,
+                             const std::vector<float>& actual,
+                             const std::string& label,
+                             float tolerance = 1e-4f) {
+    ASSERT_EQ(expected.size(), actual.size()) << label << " size mismatch";
+    for (size_t i = 0; i < expected.size(); ++i) {
+      EXPECT_NEAR(expected[i], actual[i], tolerance)
+          << label << " mismatch at index " << i;
+    }
+  }
+};
+
+namespace onnxruntime {
+namespace test {
+
+// Test 1: Dynamic workload-type switching with consistency check
+//   Baseline (no workload type) → Efficient → Default
+TEST_F(OVEPWorkloadTypeTests, OVEPWorkloadTypeDynamicSwitch) {
+  if (!IsNPUAvailable()) {
+    GTEST_SKIP() << "NPU device not available, skipping workload type test";
+  }
+
+  Ort::SessionOptions session_options;
+  std::unordered_map<std::string, std::string> ov_options;
+  ov_options["device_type"] = "NPU";
+
+  Ort::Session session = CreateSqueezeNetSession(session_options, ov_options);
+
+  const char* const keys[] = {"ep.dynamic.workload_type"};
+
+  // Phase 1: Baseline (no workload type set)
+  auto baseline_output = RunSqueezeNet(session, "Baseline");
+
+  // Phase 2: Switch to Efficient
+  const char* const eff_val[] = {"Efficient"};
+  session.SetEpDynamicOptions(keys, eff_val, 1);
+  auto efficient_output = RunSqueezeNet(session, "Efficient");
+
+  // Phase 3: Switch to Default
+  const char* const def_val[] = {"Default"};
+  session.SetEpDynamicOptions(keys, def_val, 1);
+  auto default_output = RunSqueezeNet(session, "Default");
+
+  // All modes should produce the same results
+  CompareOutputs(baseline_output, efficient_output,
+                 "Baseline vs Efficient");
+  CompareOutputs(baseline_output, default_output,
+                 "Baseline vs Default");
+}
+
+// Test 2: Multiple inferences per workload mode
+// Runs 10 inferences in each mode:
+// Baseline × 10 → Efficient × 10 → Default × 10
+TEST_F(OVEPWorkloadTypeTests, OVEPWorkloadTypeMultipleInferencesPerMode) {
+  if (!IsNPUAvailable()) {
+    GTEST_SKIP() << "NPU device not available, skipping workload type test";
+  }
+
+  Ort::SessionOptions session_options;
+  std::unordered_map<std::string, std::string> ov_options;
+  ov_options["device_type"] = "NPU";
+
+  Ort::Session session = CreateSqueezeNetSession(session_options, ov_options);
+
+  const char* const keys[] = {"ep.dynamic.workload_type"};
+  const char* const eff_val[] = {"Efficient"};
+  const char* const def_val[] = {"Default"};
+
+  constexpr int kIterationsPerMode = 10;
+
+  // Phase 1: Baseline – 10 runs without workload type
+  // Save the first run as the reference output.
+  auto reference_output = RunSqueezeNet(session, "Baseline iter 0");
+  for (int i = 1; i < kIterationsPerMode; ++i) {
+    auto output = RunSqueezeNet(session, "Baseline iter " + std::to_string(i));
+    CompareOutputs(reference_output, output,
+                   "Baseline iter " + std::to_string(i) + " vs reference");
+  }
+
+  // Phase 2: Efficient – 10 runs
+  session.SetEpDynamicOptions(keys, eff_val, 1);
+  for (int i = 0; i < kIterationsPerMode; ++i) {
+    auto output = RunSqueezeNet(session, "Efficient iter " + std::to_string(i));
+    CompareOutputs(reference_output, output,
+                   "Efficient iter " + std::to_string(i) + " vs reference");
+  }
+
+  // Phase 3: Default – 10 runs
+  session.SetEpDynamicOptions(keys, def_val, 1);
+  for (int i = 0; i < kIterationsPerMode; ++i) {
+    auto output = RunSqueezeNet(session, "Default iter " + std::to_string(i));
+    CompareOutputs(reference_output, output,
+                   "Default iter " + std::to_string(i) + " vs reference");
+  }
+}
+
+}  // namespace test
+}  // namespace onnxruntime