diff --git a/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp b/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
index 5fe8415147..f0ce9382b4 100644
--- a/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
+++ b/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
@@ -66,7 +66,8 @@ void copy_back(MixedTyped* dst, const std::vector<RequestArgument>& ra, char* sr
 // Top level driver for models and examples generated by test_generator.py
 // Test driver for those generated from ml/nn/runtime/test/spec
 void EvaluatePreparedModel(sp<IPreparedModel>& preparedModel, std::function<bool(int)> is_ignored,
-                           const std::vector<MixedTypedExampleType>& examples) {
+                           const std::vector<MixedTypedExampleType>& examples,
+                           float fpRange = 1e-5f) {
     const uint32_t INPUT = 0;
     const uint32_t OUTPUT = 1;
 
@@ -174,7 +175,7 @@ void EvaluatePreparedModel(sp<IPreparedModel>& preparedModel, std::function<bool
         MixedTyped filtered_test = filter(test, is_ignored);
 
         // We want "close-enough" results for float
-        compare(filtered_golden, filtered_test);
+        compare(filtered_golden, filtered_test, fpRange);
     }
 }
 
@@ -274,7 +275,9 @@ void Execute(sp<V1_1::IDevice>& device, std::function<V1_1::Model(void)> create_
     }
     ASSERT_NE(nullptr, preparedModel.get());
 
-    EvaluatePreparedModel(preparedModel, is_ignored, examples);
+    // If in relaxed mode, set the error range to be 5ULP of FP16.
+    float fpRange = !model.relaxComputationFloat32toFloat16 ? 1e-5f : 5.0f * 0.0009765625f;
+    EvaluatePreparedModel(preparedModel, is_ignored, examples, fpRange);
 }
 
 }  // namespace generated_tests