diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/OnnxTransform.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/OnnxTransform.cs
index df55942f25..bbb66b0e35 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/OnnxTransform.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/OnnxTransform.cs
@@ -34,7 +34,7 @@ public static void Example()
             var mlContext = new MLContext();
             var data = GetTensorData();
             var idv = mlContext.Data.LoadFromEnumerable(data);
-            var pipeline = mlContext.Transforms.ApplyOnnxModel(modelPath, new[] { outputInfo.Key }, new[] { inputInfo.Key });
+            var pipeline = mlContext.Transforms.ApplyOnnxModel(new[] { outputInfo.Key }, new[] { inputInfo.Key }, modelPath);
 
             // Run the pipeline and get the transformed values
             var transformedValues = pipeline.Fit(idv).Transform(idv);
diff --git a/src/Microsoft.ML.OnnxTransformer/OnnxCatalog.cs b/src/Microsoft.ML.OnnxTransformer/OnnxCatalog.cs
index db5f6e1114..f33a45c32b 100644
--- a/src/Microsoft.ML.OnnxTransformer/OnnxCatalog.cs
+++ b/src/Microsoft.ML.OnnxTransformer/OnnxCatalog.cs
@@ -33,15 +33,15 @@ public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog
         /// Applies a pre-trained Onnx model.
         /// </summary>
         /// <param name="catalog">The transform's catalog.</param>
-        /// <param name="modelFile">The path of the file containing the ONNX model.</param>
         /// <param name="outputColumnName">The output column resulting from the transformation.</param>
         /// <param name="inputColumnName">The input column.</param>
+        /// <param name="modelFile">The path of the file containing the ONNX model.</param>
         /// <param name="gpuDeviceId">Optional GPU device ID to run execution on, <see langword="null" /> to run on CPU.</param>
         /// <param name="fallbackToCpu">If GPU error, raise exception or fallback to CPU.</param>
         public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog,
-            string modelFile,
             string outputColumnName,
             string inputColumnName,
+            string modelFile,
             int? gpuDeviceId = null,
             bool fallbackToCpu = false)
         => new OnnxScoringEstimator(CatalogUtils.GetEnvironment(catalog), new[] { outputColumnName }, new[] { inputColumnName }, modelFile, gpuDeviceId, fallbackToCpu);
@@ -50,15 +50,15 @@ public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog
         /// Applies a pre-trained Onnx model.
         /// </summary>
         /// <param name="catalog">The transform's catalog.</param>
-        /// <param name="modelFile">The path of the file containing the ONNX model.</param>
         /// <param name="outputColumnNames">The output columns resulting from the transformation.</param>
         /// <param name="inputColumnNames">The input columns.</param>
+        /// <param name="modelFile">The path of the file containing the ONNX model.</param>
         /// <param name="gpuDeviceId">Optional GPU device ID to run execution on, <see langword="null" /> to run on CPU.</param>
         /// <param name="fallbackToCpu">If GPU error, raise exception or fallback to CPU.</param>
         public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog,
-            string modelFile,
             string[] outputColumnNames,
             string[] inputColumnNames,
+            string modelFile,
             int? gpuDeviceId = null,
             bool fallbackToCpu = false)
         => new OnnxScoringEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnNames, inputColumnNames, modelFile, gpuDeviceId, fallbackToCpu);
diff --git a/test/Microsoft.ML.OnnxTransformerTest/OnnxTransformTests.cs b/test/Microsoft.ML.OnnxTransformerTest/OnnxTransformTests.cs
index 84ec16bb8c..7cae32ce02 100644
--- a/test/Microsoft.ML.OnnxTransformerTest/OnnxTransformTests.cs
+++ b/test/Microsoft.ML.OnnxTransformerTest/OnnxTransformTests.cs
@@ -101,7 +101,7 @@ void TestSimpleCase()
             var xyData = new List<TestDataXY> { new TestDataXY() { A = new float[inputSize] } };
             var stringData = new List<TestDataDifferntType> { new TestDataDifferntType() { data_0 = new string[inputSize] } };
             var sizeData = new List<TestDataSize> { new TestDataSize() { data_0 = new float[2] } };
-            var pipe = ML.Transforms.ApplyOnnxModel(modelFile, new[] { "softmaxout_1" }, new[] { "data_0" });
+            var pipe = ML.Transforms.ApplyOnnxModel(new[] { "softmaxout_1" }, new[] { "data_0" }, modelFile);
 
             var invalidDataWrongNames = ML.Data.LoadFromEnumerable(xyData);
             var invalidDataWrongTypes = ML.Data.LoadFromEnumerable(stringData);
@@ -137,7 +137,7 @@ void TestOldSavingAndLoading(int? gpuDeviceId, bool fallbackToCpu)
 
             var inputNames = new[] { "data_0" };
             var outputNames = new[] { "softmaxout_1" };
-            var est = ML.Transforms.ApplyOnnxModel(modelFile, outputNames, inputNames, gpuDeviceId, fallbackToCpu);
+            var est = ML.Transforms.ApplyOnnxModel(outputNames, inputNames, modelFile, gpuDeviceId, fallbackToCpu);
             var transformer = est.Fit(dataView);
             var result = transformer.Transform(dataView);
             var resultRoles = new RoleMappedData(result);
@@ -241,7 +241,7 @@ public void OnnxModelScenario()
                     }
                 });
 
-            var onnx = ML.Transforms.ApplyOnnxModel(modelFile, "softmaxout_1", "data_0").Fit(dataView).Transform(dataView);
+            var onnx = ML.Transforms.ApplyOnnxModel("softmaxout_1", "data_0", modelFile).Fit(dataView).Transform(dataView);
             var scoreCol = onnx.Schema["softmaxout_1"];
 
             using (var curs = onnx.GetRowCursor(scoreCol))
@@ -271,7 +271,7 @@ public void OnnxModelMultiInput()
                         inb = new float[] {1,2,3,4,5}
                     }
                 });
-            var onnx = ML.Transforms.ApplyOnnxModel(modelFile, new[] { "outa", "outb" }, new[] { "ina", "inb" }).Fit(dataView).Transform(dataView);
+            var onnx = ML.Transforms.ApplyOnnxModel(new[] { "outa", "outb" }, new[] { "ina", "inb" }, modelFile).Fit(dataView).Transform(dataView);
 
             var outaCol = onnx.Schema["outa"];
             var outbCol = onnx.Schema["outb"];
diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs
index eb22493efc..723ab59055 100644
--- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs
+++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs
@@ -78,7 +78,7 @@ public void SimpleEndToEndOnnxConversionTest()
                 // Step 3: Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
                 string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
                 string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, outputNames, inputNames);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(data);
                 var onnxResult = onnxTransformer.Transform(data);
 
@@ -162,7 +162,7 @@ public void KmeansOnnxConversionTest()
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
                 string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
                 string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, outputNames, inputNames);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(data);
                 var onnxResult = onnxTransformer.Transform(data);
                 CompareSelectedR4VectorColumns("Score", "Score0", transformedData, onnxResult, 3);