diff --git a/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_buffer_fusing.cpp b/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_buffer_fusing.cpp
index 51ac026c7c5060..710dfa6d1c85f6 100644
--- a/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_buffer_fusing.cpp
+++ b/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_buffer_fusing.cpp
@@ -700,10 +700,10 @@ void crop_in_place_optimization::update_in_place_crop_padding_simple_data_format
             if (reshape_mode == reshape::reshape_mode::base) {
                 if (crop_axis == 0 && !crop_layout.get_partial_shape()[0].is_dynamic() &&
                     crop_layout.get_partial_shape()[0].get_length() == 1 &&
-                    !(user_info.second.get_partial_shape()[0].is_static() &&
-                      user_info.second.get_partial_shape()[0].get_length() == 1)) {
+                    !reshape_desc->output_pattern.empty() &&
+                    reshape_desc->output_pattern[0] != 0 && reshape_desc->output_pattern[0] != 1) {
                     // The crop produces exactly batch=1 per slice and the reshape squeezes that dim.
-                    // The reshape absorbs that dim, so the padding axis in the output remains 0.
+                    // output_pattern[0] == -1 means the batch dim is absorbed (squeezed).
                     reshape_axis = 0;
                 } else {
                     auto mul = 1;
@@ -767,7 +767,8 @@ void crop_in_place_optimization::update_in_place_crop_padding_simple_data_format
                 padding::DynamicDimsMask reshape_dyn_pad_mask;
 
                 if (crop_axis == 0 && crop_dim_val == 1 &&
-                    !(reshape_ps[0].is_static() && reshape_ps[0].get_length() == 1)) {
+                    !reshape_desc->output_pattern.empty() &&
+                    reshape_desc->output_pattern[0] != 0 && reshape_desc->output_pattern[0] != 1) {
                     // The crop splits on the batch axis with exactly batch=1 per slice
                     // and the reshape squeezes that batch=1 dim: [1, f, y, x] -> [f, y, x].
                     // Padding offsets are in units of one 4D batch slice (= f*y*x elements),
diff --git a/src/plugins/intel_gpu/src/graph/include/reshape_inst.h b/src/plugins/intel_gpu/src/graph/include/reshape_inst.h
index 36a5ffcf72960a..bb2e5978ea903e 100644
--- a/src/plugins/intel_gpu/src/graph/include/reshape_inst.h
+++ b/src/plugins/intel_gpu/src/graph/include/reshape_inst.h
@@ -74,10 +74,13 @@ struct typed_program_node<reshape> : public typed_program_node_base<reshape> {
                 return false;
             if (input_pshape[0].get_length() != 1)
                 return false;
-            // Reject if the reshape just flattens spatial dims while keeping batch=1
-            // (e.g. [1,C,H,W] -> [1,C,H*W]).  Only allow when the batch dim is truly squeezed.
-            auto& out_ps = prim->output_partial_shape;
-            if (!out_ps[0].is_dynamic() && out_ps[0].get_length() == 1)
+            // Reject if the reshape preserves the batch=1 dim (spatial flatten, not batch squeeze).
+            // output_pattern[0] == -1 means the first dim is inferred (batch absorbed/squeezed).
+            // output_pattern[0] == 0 or 1 means batch=1 is explicitly kept.
+            // e.g. [1,C,H,W] -> [1,C,H*W] has pattern [1,-1] => reject
+            //      [1,N,H,W,C] -> [N,H,W,C] has pattern [-1,H,W,C] => allow
+            auto first_out_pattern = prim->output_pattern[0];
+            if (first_out_pattern == 0 || first_out_pattern == 1)
                 return false;
             return true;
         }
diff --git a/src/plugins/intel_gpu/tests/unit/passes/prepare_buffer_fusing_test.cpp b/src/plugins/intel_gpu/tests/unit/passes/prepare_buffer_fusing_test.cpp
index c48040eb8bc329..b6920e8a4841cf 100644
--- a/src/plugins/intel_gpu/tests/unit/passes/prepare_buffer_fusing_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/passes/prepare_buffer_fusing_test.cpp
@@ -1911,3 +1911,87 @@ TEST(prepare_buffer_fusing, in_place_crop_dynamic_batch_axis_split_with_spatial_
     for (size_t i = 0; i < slice_elems; i++)
         ASSERT_FLOAT_EQ(out1[i], input_data[1 * slice_elems + i]) << "Branch 1 mismatch at " << i;
 }
+
+// SwinTransformer layers.3 pattern: crop [1,1,H,W,C] → Reshape [-1,H,W,C] (base mode)
+// Runtime reshape output[0] == 1, but output_pattern[0] == -1 (batch squeeze).
+// In-place crop must still work correctly.
+TEST(prepare_buffer_fusing, in_place_crop_dynamic_batch_axis_split_with_reshape_window_count_one) {
+    auto& engine = get_test_engine();
+    tests::random_generator rg(GET_SUITE_NAME);
+
+    // 3-way QKV split on axis 0 with f=1 (like SwinTransformer window_count=1)
+    const size_t num_heads = 3, seq_len = 4, head_dim = 2;
+    const size_t slice_elems = 1 * num_heads * seq_len * head_dim;
+
+    auto in_layout = layout{ov::PartialShape{3, -1, static_cast<int64_t>(num_heads),
+                                              static_cast<int64_t>(seq_len), static_cast<int64_t>(head_dim)},
+                            data_types::f32, format::bfzyx};
+    auto input_mem = engine.allocate_memory({{3, 1, static_cast<int64_t>(num_heads),
+                                              static_cast<int64_t>(seq_len), static_cast<int64_t>(head_dim)},
+                                             data_types::f32, format::bfzyx});
+    auto axis_mem = engine.allocate_memory({{}, data_types::i64, format::bfyx});
+    auto splits_length_mem = engine.allocate_memory({{3}, data_types::i64, format::bfyx});
+
+    const int64_t axis = 0;
+
+    auto input_data = rg.generate_random_1d<float>(input_mem->count(), -1.f, 1.f);
+    set_values(input_mem, input_data);
+    set_values<int64_t>(axis_mem, {axis});
+    set_values<int64_t>(splits_length_mem, {1, 1, 1});
+
+    // output_pattern[0] == -1: batch dim is squeezed. Runtime output[0] resolves to 1.
+    const std::vector<int64_t> squeeze_pattern = {-1, static_cast<int64_t>(num_heads),
+                                                   static_cast<int64_t>(seq_len), static_cast<int64_t>(head_dim)};
+    const ov::PartialShape squeeze_out_shape = {-1, static_cast<int64_t>(num_heads),
+                                                 static_cast<int64_t>(seq_len), static_cast<int64_t>(head_dim)};
+
+    cldnn::crop_ngraph_op_mode op_mode = cldnn::crop_ngraph_op_mode::variadic_split;
+    topology topology(
+        input_layout("input", in_layout),
+        data("axis", axis_mem),
+        data("splits_length", splits_length_mem),
+        // Q branch
+        crop("crop_q", {input_info("input"), input_info("axis"), input_info("splits_length")}, cldnn::tensor(1), cldnn::tensor(0), op_mode, 0, axis),
+        reshape("reshape_q", input_info("crop_q"), false, squeeze_pattern, squeeze_out_shape, cldnn::reshape::reshape_mode::base),
+        reorder("output_q", input_info("reshape_q"), format::bfyx, data_types::f32, std::vector<float>(), reorder_mean_mode::subtract, padding(), true),
+        // K branch
+        crop("crop_k", {input_info("input"), input_info("axis"), input_info("splits_length")}, cldnn::tensor(1), cldnn::tensor(0), op_mode, 1, axis),
+        reshape("reshape_k", input_info("crop_k"), false, squeeze_pattern, squeeze_out_shape, cldnn::reshape::reshape_mode::base),
+        reorder("output_k", input_info("reshape_k"), format::bfyx, data_types::f32, std::vector<float>(), reorder_mean_mode::subtract, padding(), true),
+        // V branch
+        crop("crop_v", {input_info("input"), input_info("axis"), input_info("splits_length")}, cldnn::tensor(1), cldnn::tensor(0), op_mode, 2, axis),
+        reshape("reshape_v", input_info("crop_v"), false, squeeze_pattern, squeeze_out_shape, cldnn::reshape::reshape_mode::base),
+        reorder("output_v", input_info("reshape_v"), format::bfyx, data_types::f32, std::vector<float>(), reorder_mean_mode::subtract, padding(), true)
+    );
+
+    auto config = get_test_default_config(engine);
+    config.set_property(ov::intel_gpu::allow_new_shape_infer(true));
+    config.set_property(ov::intel_gpu::optimize_data(true));
+    network network(engine, topology, config);
+    network.set_input_data("input", input_mem);
+
+    auto outputs = network.execute();
+
+    // Crops must still be in-place optimized despite runtime reshape output[0] == 1
+    ASSERT_TRUE(network.get_primitive("crop_q")->can_be_optimized());
+    ASSERT_TRUE(network.get_primitive("crop_k")->can_be_optimized());
+    ASSERT_TRUE(network.get_primitive("crop_v")->can_be_optimized());
+
+    auto q_mem = outputs.at("output_q").get_memory();
+    cldnn::mem_lock<float> q_out(q_mem, get_test_stream());
+    auto k_mem = outputs.at("output_k").get_memory();
+    cldnn::mem_lock<float> k_out(k_mem, get_test_stream());
+    auto v_mem = outputs.at("output_v").get_memory();
+    cldnn::mem_lock<float> v_out(v_mem, get_test_stream());
+
+    ASSERT_EQ(q_out.size(), slice_elems);
+    ASSERT_EQ(k_out.size(), slice_elems);
+    ASSERT_EQ(v_out.size(), slice_elems);
+
+    for (size_t i = 0; i < slice_elems; i++)
+        ASSERT_FLOAT_EQ(q_out[i], input_data[0 * slice_elems + i]) << "Q mismatch at " << i;
+    for (size_t i = 0; i < slice_elems; i++)
+        ASSERT_FLOAT_EQ(k_out[i], input_data[1 * slice_elems + i]) << "K mismatch at " << i;
+    for (size_t i = 0; i < slice_elems; i++)
+        ASSERT_FLOAT_EQ(v_out[i], input_data[2 * slice_elems + i]) << "V mismatch at " << i;
+}