diff --git a/README.md b/README.md
index 79768e6..9696912 100644
--- a/README.md
+++ b/README.md
@@ -104,7 +104,7 @@ def summary(
     depth: int = 3,
     device: Optional[torch.device] = None,
     dtypes: Optional[List[torch.dtype]] = None,
-    mode: str | None = None,
+    mode: str = "same",
     row_settings: Optional[Iterable[str]] = None,
     verbose: int = 1,
     **kwargs: Any,
@@ -198,9 +198,10 @@ Args:
             Default: None
 
     mode (str)
-            Either "train" or "eval", which determines whether we call
-            model.train() or model.eval() before calling summary().
-            Default: "eval".
+            Either "train", "eval" or "same", which determines whether we call
+            model.train() or model.eval() before calling summary(). In any case,
+            original model mode is restored at the end.
+            Default: "same".
 
     row_settings (Iterable[str]):
             Specify which features to show in a row. Currently supported: (
diff --git a/tests/torchinfo_xl_test.py b/tests/torchinfo_xl_test.py
index 97581c7..1a9ca46 100644
--- a/tests/torchinfo_xl_test.py
+++ b/tests/torchinfo_xl_test.py
@@ -57,7 +57,7 @@ def test_eval_order_doesnt_matter() -> None:
     model2 = torchvision.models.resnet18(
         weights=torchvision.models.ResNet18_Weights.DEFAULT
     )
-    summary(model2, input_size=input_size)
+    summary(model2, input_size=input_size, mode="eval")
     model2.eval()
     with torch.inference_mode():
         output2 = model2(input_tensor)
@@ -144,7 +144,7 @@ def test_tmva_net_column_totals() -> None:
 def test_google() -> None:
     google_net = torchvision.models.googlenet(init_weights=False)
 
-    summary(google_net, (1, 3, 112, 112), depth=7)
+    summary(google_net, (1, 3, 112, 112), depth=7, mode="eval")
 
     # Check googlenet in training mode since InceptionAux layers are used in
     # forward-prop in train mode but not in eval mode.
diff --git a/torchinfo/enums.py b/torchinfo/enums.py
index 191de8d..336bd78 100644
--- a/torchinfo/enums.py
+++ b/torchinfo/enums.py
@@ -11,6 +11,7 @@ class Mode(str, Enum):
 
     TRAIN = "train"
     EVAL = "eval"
+    SAME = "same"
 
 
 @unique
diff --git a/torchinfo/torchinfo.py b/torchinfo/torchinfo.py
index e9e5823..c0b6bb5 100644
--- a/torchinfo/torchinfo.py
+++ b/torchinfo/torchinfo.py
@@ -62,7 +62,7 @@ def summary(
     depth: int = 3,
     device: torch.device | str | None = None,
     dtypes: list[torch.dtype] | None = None,
-    mode: str | None = None,
+    mode: str = "same",
     row_settings: Iterable[str] | None = None,
     verbose: int | None = None,
     **kwargs: Any,
@@ -156,9 +156,10 @@ class name as the key. If the forward pass is an expensive operation,
                 Default: None
 
         mode (str)
-                Either "train" or "eval", which determines whether we call
-                model.train() or model.eval() before calling summary().
-                Default: "eval".
+                Either "train", "eval" or "same", which determines whether we call
+                model.train() or model.eval() before calling summary(). In any case,
+                original model mode is restored at the end.
+                Default: "same".
 
         row_settings (Iterable[str]):
                 Specify which features to show in a row. Currently supported: (
@@ -198,10 +199,7 @@ class name as the key. If the forward pass is an expensive operation,
     else:
         rows = {RowSettings(name) for name in row_settings}
 
-    if mode is None:
-        model_mode = Mode.EVAL
-    else:
-        model_mode = Mode(mode)
+    model_mode = Mode(mode)
 
     if verbose is None:
         verbose = 0 if hasattr(sys, "ps1") and sys.ps1 else 1
@@ -286,7 +284,7 @@ def forward_pass(
             model.train()
         elif mode == Mode.EVAL:
             model.eval()
-        else:
+        elif mode != Mode.SAME:
             raise RuntimeError(
                 f"Specified model mode ({list(Mode)}) not recognized: {mode}"
             )