diff --git a/src/frontends/pytorch/src/op/cat.cpp b/src/frontends/pytorch/src/op/cat.cpp
index 33d2f5f18a20fd..a200ad3f997acf 100644
--- a/src/frontends/pytorch/src/op/cat.cpp
+++ b/src/frontends/pytorch/src/op/cat.cpp
@@ -102,7 +102,7 @@ OutputVector translate_quantized_cat(const NodeContext& context) {
 };
 
 OutputVector translate_stack_fx(const NodeContext& context) {
-    num_inputs_check(context, 2, context.get_input_size());
+    num_inputs_check(context, 1, context.get_input_size());
     auto dim = context.mark_node(v0::Constant::create(element::i32, Shape{}, {0}));
     std::deque<Output<Node>> list_elems;
     auto num_elements = context.get_input_size();
@@ -112,14 +112,12 @@ OutputVector translate_stack_fx(const NodeContext& context) {
         list_elems.push_back(stack_input);
     }
     int64_t axis = 0;
-    if (context.get_input_size() > 2)
-        axis = context.const_input<int64_t>(context.get_input_size() - 1);
-    if (!context.get_input_type(context.get_input_size() - 1).is<type::List>()) {
+    if (!context.get_input_type(num_elements - 1).is<type::List>()) {
         // axis can be not present and that means that last input will have List type
-        axis = context.const_input<int64_t>(context.get_input_size() - 1);
+        axis = context.const_input<int64_t>(num_elements - 1);
     } else {
         auto stack_input =
-            context.mark_node(std::make_shared<v0::Unsqueeze>(context.get_input(static_cast<int>(context.get_input_size() - 1)), dim));
+            context.mark_node(std::make_shared<v0::Unsqueeze>(context.get_input(static_cast<int>(num_elements - 1)), dim));
         list_elems.push_back(stack_input);
     }
     return translate_cat_common(context, list_elems, axis, true);