diff --git a/model_api/pyproject.toml b/model_api/pyproject.toml
index 7338eb24..1cffd3f4 100644
--- a/model_api/pyproject.toml
+++ b/model_api/pyproject.toml
@@ -28,7 +28,7 @@ classifiers = [
 ]
 dependencies = [
     "numpy>=1.16.6",
-    "opencv-python-headless~=4.0",
+    "opencv-python-headless",
     "openvino>=2025.3",
     "pillow",
 ]
diff --git a/model_api/src/model_api/models/result/segmentation.py b/model_api/src/model_api/models/result/segmentation.py
index a2cafa0c..4e4e06ab 100644
--- a/model_api/src/model_api/models/result/segmentation.py
+++ b/model_api/src/model_api/models/result/segmentation.py
@@ -212,9 +212,9 @@ def hist(self) -> dict[str, float]:
             ranges=[0, 255],
         )
         hist = {}
-        for i, count in enumerate(outHist):
+        for i, count in enumerate(outHist.flatten()):
             if count > 0:
-                hist[str(i)] = count[0].item() / self.resultImage.size
+                hist[str(i)] = count.item() / self.resultImage.size
 
         return hist
 
diff --git a/model_api/tests/unit/results/test_sseg_result.py b/model_api/tests/unit/results/test_sseg_result.py
index 26d0f83d..4ed5921f 100644
--- a/model_api/tests/unit/results/test_sseg_result.py
+++ b/model_api/tests/unit/results/test_sseg_result.py
@@ -3,8 +3,80 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
+from unittest.mock import patch
+
 import numpy as np
+import pytest
 from model_api.models.result import Contour
+from model_api.models.result.segmentation import ImageResultWithSoftPrediction
+
+
+class TestImageResultWithSoftPredictionHist:
+    """Tests for hist() method supporting OpenCV 4 and 5 return types."""
+
+    @pytest.fixture
+    def result_image(self):
+        """Create a simple test image with known pixel distribution."""
+        # 10x10 image: 50 pixels of value 0, 30 pixels of value 128, 20 pixels of value 255
+        img = np.zeros((10, 10), dtype=np.uint8)
+        img[0:5, :] = 0  # 50 pixels
+        img[5:8, :] = 128  # 30 pixels
+        img[8:10, :] = 255  # 20 pixels
+        return img
+
+    @pytest.fixture
+    def image_result(self, result_image):
+        """Create ImageResultWithSoftPrediction instance."""
+        return ImageResultWithSoftPrediction(
+            resultImage=result_image,
+            soft_prediction=np.zeros((10, 10, 3)),
+            saliency_map=np.zeros((10, 10)),
+            feature_vector=np.zeros((256,)),
+        )
+
+    def test_hist_with_opencv4_column_vector(self, image_result):
+        """OpenCV 4 returns column vector [N, 1] from calcHist."""
+        # Simulate OpenCV 4 output: column vector shape [256, 1]
+        opencv4_hist = np.zeros((256, 1), dtype=np.float32)
+        opencv4_hist[0, 0] = 50.0  # 50 pixels of value 0
+        opencv4_hist[128, 0] = 30.0  # 30 pixels of value 128
+        opencv4_hist[255, 0] = 20.0  # 20 pixels of value 255
+
+        with patch("cv2.calcHist", return_value=opencv4_hist):
+            hist = image_result.hist()
+
+        assert hist == {"0": 0.5, "128": 0.3, "255": 0.2}
+
+    def test_hist_with_opencv5_1d_array(self, image_result):
+        """OpenCV 5+ returns 1D array [N] from calcHist."""
+        # Simulate OpenCV 5 output: 1D array shape [256]
+        opencv5_hist = np.zeros((256,), dtype=np.float32)
+        opencv5_hist[0] = 50.0  # 50 pixels of value 0
+        opencv5_hist[128] = 30.0  # 30 pixels of value 128
+        opencv5_hist[255] = 20.0  # 20 pixels of value 255
+
+        with patch("cv2.calcHist", return_value=opencv5_hist):
+            hist = image_result.hist()
+
+        assert hist == {"0": 0.5, "128": 0.3, "255": 0.2}
+
+    def test_hist_both_formats_produce_same_result(self, image_result):
+        """Both OpenCV formats produce identical histogram output."""
+        opencv4_hist = np.zeros((256, 1), dtype=np.float32)
+        opencv4_hist[10, 0] = 25.0
+        opencv4_hist[20, 0] = 75.0
+
+        opencv5_hist = np.zeros((256,), dtype=np.float32)
+        opencv5_hist[10] = 25.0
+        opencv5_hist[20] = 75.0
+
+        with patch("cv2.calcHist", return_value=opencv4_hist):
+            hist_v4 = image_result.hist()
+
+        with patch("cv2.calcHist", return_value=opencv5_hist):
+            hist_v5 = image_result.hist()
+
+        assert hist_v4 == hist_v5
 
 
 def test_contour_type():
diff --git a/model_api/uv.lock b/model_api/uv.lock
index c0015287..7b774415 100644
--- a/model_api/uv.lock
+++ b/model_api/uv.lock
@@ -2093,7 +2093,7 @@ requires-dist = [
     { name = "onnx", marker = "extra == 'onnx'" },
     { name = "onnxruntime", marker = "extra == 'examples'" },
     { name = "onnxruntime", marker = "extra == 'onnx'" },
-    { name = "opencv-python-headless", specifier = "~=4.0" },
+    { name = "opencv-python-headless" },
     { name = "openvino", specifier = ">=2025.3" },
     { name = "pillow" },
     { name = "sympy", marker = "extra == 'examples'" },
diff --git a/model_converter/uv.lock b/model_converter/uv.lock
index 8f94d6fd..3823f4f7 100644
--- a/model_converter/uv.lock
+++ b/model_converter/uv.lock
@@ -1345,7 +1345,7 @@ requires-dist = [
     { name = "onnx", marker = "extra == 'onnx'" },
     { name = "onnxruntime", marker = "extra == 'examples'" },
     { name = "onnxruntime", marker = "extra == 'onnx'" },
-    { name = "opencv-python-headless", specifier = "~=4.0" },
+    { name = "opencv-python-headless" },
     { name = "openvino", specifier = ">=2025.3" },
     { name = "pillow" },
     { name = "sympy", marker = "extra == 'examples'" },