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diff --git a/model-evaluation/src/test/java/ai/vespa/models/evaluation/OnnxEvaluatorTest.java b/model-evaluation/src/test/java/ai/vespa/models/evaluation/OnnxEvaluatorTest.java
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+// Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+package ai.vespa.models.evaluation;
+
+import com.yahoo.config.subscription.ConfigGetter;
+import com.yahoo.config.subscription.FileSource;
+import com.yahoo.filedistribution.fileacquirer.FileAcquirer;
+import com.yahoo.filedistribution.fileacquirer.MockFileAcquirer;
+import com.yahoo.path.Path;
+import com.yahoo.tensor.Tensor;
+import com.yahoo.vespa.config.search.RankProfilesConfig;
+import com.yahoo.vespa.config.search.core.OnnxModelsConfig;
+import com.yahoo.vespa.config.search.core.RankingConstantsConfig;
+import org.junit.Test;
+
+import java.io.File;
+import java.util.HashMap;
+import java.util.Map;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertTrue;
+
+/**
+ * @author lesters
+ */
+public class OnnxEvaluatorTest {
+
+    private static final double delta = 0.00000000001;
+
+    @Test
+    public void testOnnxEvaluation() {
+        ModelsEvaluator models = createModels("src/test/resources/config/onnx/");
+
+        assertTrue(models.models().containsKey("add_mul"));
+        assertTrue(models.models().containsKey("one_layer"));
+
+        FunctionEvaluator function = models.evaluatorOf("add_mul", "output1");
+        function.bind("input1", Tensor.from("tensor<float>(d0[1]):[2]"));
+        function.bind("input2", Tensor.from("tensor<float>(d0[1]):[3]"));
+        assertEquals(6.0, function.evaluate().sum().asDouble(), delta);
+
+        function = models.evaluatorOf("add_mul", "output2");
+        function.bind("input1", Tensor.from("tensor<float>(d0[1]):[2]"));
+        function.bind("input2", Tensor.from("tensor<float>(d0[1]):[3]"));
+        assertEquals(5.0, function.evaluate().sum().asDouble(), delta);
+
+        function = models.evaluatorOf("one_layer");
+        function.bind("input", Tensor.from("tensor<float>(d0[2],d1[3]):[[0.1, 0.2, 0.3],[0.4,0.5,0.6]]"));
+        assertEquals(function.evaluate(), Tensor.from("tensor<float>(d0[2],d1[1]):[0.63931,0.67574]"));
+    }
+
+    private ModelsEvaluator createModels(String path) {
+        Path configDir = Path.fromString(path);
+        RankProfilesConfig config = new ConfigGetter<>(new FileSource(configDir.append("rank-profiles.cfg").toFile()),
+                                                       RankProfilesConfig.class).getConfig("");
+        RankingConstantsConfig constantsConfig = new ConfigGetter<>(new FileSource(configDir.append("ranking-constants.cfg").toFile()),
+                                                                    RankingConstantsConfig.class).getConfig("");
+        OnnxModelsConfig onnxModelsConfig = new ConfigGetter<>(new FileSource(configDir.append("onnx-models.cfg").toFile()),
+                                                               OnnxModelsConfig.class).getConfig("");
+
+        Map<String, File> fileMap = new HashMap<>();
+        for (OnnxModelsConfig.Model onnxModel : onnxModelsConfig.model()) {
+            fileMap.put(onnxModel.fileref().value(), new File(path + onnxModel.fileref().value()));
+        }
+        FileAcquirer fileAcquirer = MockFileAcquirer.returnFiles(fileMap);
+
+        return new ModelsEvaluator(config, constantsConfig, onnxModelsConfig, fileAcquirer);
+    }
+
+}