diff --git a/tests/trace/test_client_trace.py b/tests/trace/test_client_trace.py
index cde47f0f4f5..716424554b8 100644
--- a/tests/trace/test_client_trace.py
+++ b/tests/trace/test_client_trace.py
@@ -1443,7 +1443,7 @@ def test_named_reuse(client):
     dataset = weave.ref(d_ref.uri()).get()
 
     @weave.op()
-    async def dummy_score(model_output):
+    async def dummy_score(output):
         return 1
 
     class SimpleModel(weave.Model):
diff --git a/tests/trace/test_evaluate.py b/tests/trace/test_evaluate.py
index f5ada25215f..a07d9dd5d23 100644
--- a/tests/trace/test_evaluate.py
+++ b/tests/trace/test_evaluate.py
@@ -11,7 +11,7 @@
 
 
 expected_eval_result = {
-    "model_output": {"mean": 9.5},
+    "output": {"mean": 9.5},
     "score": {"true_count": 1, "true_fraction": 0.5},
     "model_latency": {"mean": pytest.approx(0, abs=1)},
 }
@@ -24,8 +24,8 @@ async def predict(self, input) -> str:
 
 
 @weave.op()
-def score(target, model_output):
-    return target == model_output
+def score(target, output):
+    return target == output
 
 
 @weave.op()
@@ -57,7 +57,7 @@ async def model_predict(input, target) -> str:
     )
     result = asyncio.run(evaluation.evaluate(model_predict))
     assert result == {
-        "model_output": {"mean": 18.5},
+        "output": {"mean": 18.5},
         "score": {"true_count": 0, "true_fraction": 0.0},
         "model_latency": {
             "mean": pytest.approx(0, abs=1),
@@ -111,8 +111,8 @@ async def infer(self, input) -> str:
 def test_score_as_class(client):
     class MyScorer(weave.Scorer):
         @weave.op()
-        def score(self, target, model_output):
-            return target == model_output
+        def score(self, target, output):
+            return target == output
 
     evaluation = Evaluation(
         dataset=dataset_rows,
@@ -121,7 +121,7 @@ def score(self, target, model_output):
     model = EvalModel()
     result = asyncio.run(evaluation.evaluate(model))
     assert result == {
-        "model_output": {"mean": 9.5},
+        "output": {"mean": 9.5},
         "MyScorer": {"true_count": 1, "true_fraction": 0.5},
         "model_latency": {
             "mean": pytest.approx(0, abs=1),
@@ -137,8 +137,8 @@ def summarize(self, score_rows):
             return {"awesome": 3}
 
         @weave.op()
-        def score(self, target, model_output):
-            return target == model_output
+        def score(self, target, output):
+            return target == output
 
     evaluation = Evaluation(
         dataset=dataset_rows,
@@ -147,7 +147,7 @@ def score(self, target, model_output):
     model = EvalModel()
     result = asyncio.run(evaluation.evaluate(model))
     assert result == {
-        "model_output": {"mean": 9.5},
+        "output": {"mean": 9.5},
         "MyScorer": {"awesome": 3},
         "model_latency": {
             "mean": pytest.approx(0, abs=1),
@@ -167,7 +167,7 @@ def return_pred(pred):
 
     result = asyncio.run(evaluation.evaluate(return_pred))
     assert result == {
-        "model_output": {
+        "output": {
             "a": {"true_count": 1, "true_fraction": 1.0},
             "b": {"true_count": 0, "true_fraction": 0.0},
         },
diff --git a/tests/trace/test_evaluation_performance.py b/tests/trace/test_evaluation_performance.py
index bcc36c2ebca..17f01192f26 100644
--- a/tests/trace/test_evaluation_performance.py
+++ b/tests/trace/test_evaluation_performance.py
@@ -91,8 +91,8 @@ def predict(question: str):
         return "I don't know"
 
     @weave.op()
-    def score(question: str, expected: str, model_output: str):
-        return model_output == expected
+    def score(question: str, expected: str, output: str):
+        return output == expected
 
     evaluation = weave.Evaluation(
         name="My Evaluation",
diff --git a/tests/trace/test_weave_client.py b/tests/trace/test_weave_client.py
index a5eb8a49bc3..a746a871a9b 100644
--- a/tests/trace/test_weave_client.py
+++ b/tests/trace/test_weave_client.py
@@ -393,8 +393,8 @@ async def model_predict(input) -> str:
     dataset_rows = [{"input": "1 + 2", "target": 3}, {"input": "2**4", "target": 15}]
 
     @weave.op()
-    async def score(target, model_output):
-        return target == model_output
+    async def score(target, output):
+        return target == output
 
     evaluation = Evaluation(
         name="my-eval",
@@ -747,8 +747,8 @@ async def model_predict(input) -> str:
     dataset_rows = [{"input": "1 + 2", "target": 3}, {"input": "2**4", "target": 15}]
 
     @weave.op()
-    async def score(target, model_output):
-        return target == model_output
+    async def score(target, output):
+        return target == output
 
     evaluation = Evaluation(
         name="my-eval",
@@ -757,7 +757,7 @@ async def score(target, model_output):
     )
     result = asyncio.run(evaluation.evaluate(model_predict))
     expected_eval_result = {
-        "model_output": {"mean": 9.5},
+        "output": {"mean": 9.5},
         "score": {"true_count": 1, "true_fraction": 0.5},
     }
     assert result == expected_eval_result
@@ -857,8 +857,8 @@ def test_nested_ref_is_inner(client):
     dataset_rows = [{"input": "1 + 2", "target": 3}, {"input": "2**4", "target": 15}]
 
     @weave.op()
-    async def score(target, model_output):
-        return target == model_output
+    async def score(target, output):
+        return target == output
 
     evaluation = Evaluation(
         name="my-eval",