diff --git a/webnn/reduction.https.any.js b/webnn/reduction.https.any.js
new file mode 100644
index 00000000000000..bff8aa22baa109
--- /dev/null
+++ b/webnn/reduction.https.any.js
@@ -0,0 +1,13 @@
+// META: title=test WebNN API reduction  operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-reduce
+
+testWebNNOperation(
+  ['reduceMax', 'reduceMean', 'reduceMin', 'reduceProduct', 'reduceSum'],
+  buildOperationWithSingleInput
+);
\ No newline at end of file
diff --git a/webnn/resources/test_data/reduce_max.json b/webnn/resources/test_data/reduce_max.json
new file mode 100644
index 00000000000000..a1eb6d2b831274
--- /dev/null
+++ b/webnn/resources/test_data/reduce_max.json
@@ -0,0 +1,782 @@
+{
+  "tests": [
+    {
+      "name": "reduceMax float32 1D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
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+            -51.287275192229046,
+            -42.588327517032454
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+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 99.77313232421875,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMax float32 2D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [4, 6],
+          "data": [
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+            -51.287275192229046,
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+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 99.77313232421875,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMax float32 3D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
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+      "expected": {
+        "name": "output",
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+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMax float32 4D tensor default options",
+      "inputs": {
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+      },
+      "expected": {
+        "name": "output",
+        "data": 99.77313232421875,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMax float32 5D tensor default options",
+      "inputs": {
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+      "expected": {
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+    {
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+      "inputs": {
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+      "expected": {
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+          99.77313232421875
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMax float32 4D tensor negative options.axes with options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            32.166585683408215,
+            90.42288038051404,
+            -26.341795469529572,
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+            62.504641219832195,
+            36.650633791794576,
+            99.77313292829271,
+            -72.86485546227293,
+            -46.03200926418318,
+            20.253753500247612,
+            -21.55738542170144,
+            -51.287275192229046,
+            -42.588327517032454
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [1, -1]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2],
+        "data": [
+          90.42288208007812,
+          94.99645233154297,
+          96.55397033691406,
+          99.77313232421875
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMax float32 4D tensor negative options.axes with options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            32.166585683408215,
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+            36.650633791794576,
+            99.77313292829271,
+            -72.86485546227293,
+            -46.03200926418318,
+            20.253753500247612,
+            -21.55738542170144,
+            -51.287275192229046,
+            -42.588327517032454
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [-3, 3]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 2, 2, 1],
+        "data": [
+          90.42288208007812,
+          94.99645233154297,
+          96.55397033691406,
+          99.77313232421875
+        ],
+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/webnn/resources/test_data/reduce_mean.json b/webnn/resources/test_data/reduce_mean.json
new file mode 100644
index 00000000000000..af353b5ad110c8
--- /dev/null
+++ b/webnn/resources/test_data/reduce_mean.json
@@ -0,0 +1,886 @@
+{
+  "tests": [
+    {
+      "name": "reduceMean float32 1D tensor all positive default options",
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
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+            21.637750690378144,
+            14.160698325191756,
+            15.127351396412191
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+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 40.31047439575195,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMean float32 1D tensor all negative default options",
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            -37.14687069315612,
+            -44.50042209159177,
+            -6.126548413219496,
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+            -38.74917461116718,
+            -36.81039848179957,
+            -26.274377028592966,
+            -12.56681966278282,
+            -55.28200785289164,
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+            -45.36002983401583,
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+            -76.02979877636918,
+            -23.917405754606236,
+            -22.941872716763513
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+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": -34.74319839477539,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMean float32 1D tensor all positive integers default options", // test with float32 values which are equivalent to integers
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            42,
+            24,
+            44,
+            38,
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+            8,
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+            19,
+            62,
+            71
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+          "type": "float32"
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+      },
+      "expected": {
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+        "data": 48.41666793823242,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMean float32 1D tensor all negative integers default options", // test with float32 values which are equivalent to integers
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            -73,
+            -8,
+            -55,
+            -73,
+            -61,
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+            -57,
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+            -1,
+            -75,
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+            -71,
+            -72,
+            -67,
+            -16,
+            -21,
+            -31
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+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": -47.54166793823242,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMean float32 2D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [4, 6],
+          "data": [
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+            14.160698325191756,
+            15.127351396412191
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+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 40.31047439575195,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMean float32 3D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
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+          "type": "float32"
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+      },
+      "expected": {
+        "name": "output",
+        "data": 40.31047439575195,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMean float32 4D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
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+            42.134198143189195,
+            21.637750690378144,
+            14.160698325191756,
+            15.127351396412191
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+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 40.31047439575195,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMean float32 5D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [2, 1, 4, 1, 3],
+          "data": [
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+            75.69370054099012,
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+            20.006068194857885,
+            94.80963809344851,
+            25.829190184293417,
+            94.13260521032214,
+            67.72958187044576,
+            16.099351117563778,
+            92.19433555001908,
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+            3.6623324286590764,
+            20.2102781903039,
+            58.56523137489753,
+            28.673493080608758,
+            42.134198143189195,
+            21.637750690378144,
+            14.160698325191756,
+            15.127351396412191
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+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 40.31047439575195,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMean float32 3D tensor options.axes",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
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+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [2]
+      },
+      "expected": {
+        "shape": [2, 3],
+        "data": [
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+          49.670711517333984,
+          50.94768142700195,
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+          23.264999389648438
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+        "type": "float32"
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+    },
+    {
+      "name": "reduceMean float32 3D tensor negative options.axes",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
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+            15.127351396412191
+          ],
+          "type": "float32"
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+      },
+      "options": {
+        "axes": [-1]
+      },
+      "expected": {
+        "shape": [2, 3],
+        "data": [
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+          49.670711517333984,
+          50.94768142700195,
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+        "type": "float32"
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+    },
+    {
+      "name": "reduceMean float32 4D tensor options.axes",
+      "inputs": {
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+      "options": {
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+      "expected": {
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+        "data": [
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+    {
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+            15.127351396412191
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+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [-4, -2]
+      },
+      "expected": {
+        "shape": [2, 3],
+        "data": [
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+          40.251548767089844,
+          22.060470581054688,
+          48.58541488647461,
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+    },
+    {
+      "name": "reduceMean float32 3D tensor options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
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+          "type": "float32"
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+      },
+      "options": {
+        "keepDimensions": false
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+      "expected": {
+        "data": 40.31047439575195,
+        "type": "float32"
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+    },
+    {
+      "name": "reduceMean float32 3D tensor options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
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+            3.6623324286590764,
+            20.2102781903039,
+            58.56523137489753,
+            28.673493080608758,
+            42.134198143189195,
+            21.637750690378144,
+            14.160698325191756,
+            15.127351396412191
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": true
+      },
+      "expected": {
+        "shape": [1, 1, 1],
+        "data": 40.31047439575195,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMean float32 4D tensor options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
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+}
\ No newline at end of file
diff --git a/webnn/resources/test_data/reduce_min.json b/webnn/resources/test_data/reduce_min.json
new file mode 100644
index 00000000000000..8db681d7ea4556
--- /dev/null
+++ b/webnn/resources/test_data/reduce_min.json
@@ -0,0 +1,782 @@
+{
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+}
\ No newline at end of file
diff --git a/webnn/resources/test_data/reduce_product.json b/webnn/resources/test_data/reduce_product.json
new file mode 100644
index 00000000000000..24c60f89359bdd
--- /dev/null
+++ b/webnn/resources/test_data/reduce_product.json
@@ -0,0 +1,782 @@
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+            48.66154478867011,
+            -85.19523905815305,
+            -18.169861657911653,
+            64.83759066937682,
+            -14.958838503675935,
+            -74.50931980014732,
+            -11.319679210869765
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [-4, -2]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 3],
+        "data": [
+          4227263.5,
+          -446960.5625,
+          3811296.75,
+          1280298.5,
+          -1343475.375,
+          1280118.75
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceProduct float32 3D tensor options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            -68.75911760233478,
+            99.4496130391401,
+            24.860555622046746,
+            -44.23515424391753,
+            -22.69974404385384,
+            79.97555172142995,
+            14.46502415218545,
+            49.231095225588376,
+            30.058706923717068,
+            69.45106401985521,
+            -20.15709647137976,
+            -58.02551401530734,
+            51.89660954258355,
+            -2.020799112914105,
+            39.392973241316554,
+            26.780734701055266,
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+            48.66154478867011,
+            -85.19523905815305,
+            -18.169861657911653,
+            64.83759066937682,
+            -14.958838503675935,
+            -74.50931980014732,
+            -11.319679210869765
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": false
+      },
+      "expected": {
+        "name": "output",
+        "data": 1.5855958784642327e+37,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceProduct float32 3D tensor options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            -68.75911760233478,
+            99.4496130391401,
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+            -44.23515424391753,
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+            -11.319679210869765
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": true
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 1, 1],
+        "data": [
+          1.5855958784642327e+37
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceProduct float32 4D tensor options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            -68.75911760233478,
+            99.4496130391401,
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+            -44.23515424391753,
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+            -2.020799112914105,
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+            26.780734701055266,
+            -97.97651593223856,
+            48.66154478867011,
+            -85.19523905815305,
+            -18.169861657911653,
+            64.83759066937682,
+            -14.958838503675935,
+            -74.50931980014732,
+            -11.319679210869765
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": false
+      },
+      "expected": {
+        "name": "output",
+        "data": 1.5855958784642327e+37,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceProduct float32 4D tensor options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            -68.75911760233478,
+            99.4496130391401,
+            24.860555622046746,
+            -44.23515424391753,
+            -22.69974404385384,
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+            49.231095225588376,
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+            69.45106401985521,
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+            51.89660954258355,
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+            26.780734701055266,
+            -97.97651593223856,
+            48.66154478867011,
+            -85.19523905815305,
+            -18.169861657911653,
+            64.83759066937682,
+            -14.958838503675935,
+            -74.50931980014732,
+            -11.319679210869765
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": true
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 1, 1, 1],
+        "data": [
+          1.5855958784642327e+37
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceProduct float32 4D tensor options.axes with options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            -68.75911760233478,
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+            -97.97651593223856,
+            48.66154478867011,
+            -85.19523905815305,
+            -18.169861657911653,
+            64.83759066937682,
+            -14.958838503675935,
+            -74.50931980014732,
+            -11.319679210869765
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [1, 3]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2],
+        "data": [
+          -3638925568,
+          6523364352,
+          -414643360,
+          1610916352
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceProduct float32 4D tensor options.axes with options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            -68.75911760233478,
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+            -58.02551401530734,
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+            -18.169861657911653,
+            64.83759066937682,
+            -14.958838503675935,
+            -74.50931980014732,
+            -11.319679210869765
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [1, 3]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 2, 2, 1],
+        "data": [
+          -3638925568,
+          6523364352,
+          -414643360,
+          1610916352
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceProduct float32 4D tensor negative options.axes with options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            -68.75911760233478,
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+            -58.02551401530734,
+            51.89660954258355,
+            -2.020799112914105,
+            39.392973241316554,
+            26.780734701055266,
+            -97.97651593223856,
+            48.66154478867011,
+            -85.19523905815305,
+            -18.169861657911653,
+            64.83759066937682,
+            -14.958838503675935,
+            -74.50931980014732,
+            -11.319679210869765
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [1, -1]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2],
+        "data": [
+          -3638925568,
+          6523364352,
+          -414643360,
+          1610916352
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceProduct float32 4D tensor negative options.axes with options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            -68.75911760233478,
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+            48.66154478867011,
+            -85.19523905815305,
+            -18.169861657911653,
+            64.83759066937682,
+            -14.958838503675935,
+            -74.50931980014732,
+            -11.319679210869765
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [-3, 3]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 2, 2, 1],
+        "data": [
+          -3638925568,
+          6523364352,
+          -414643360,
+          1610916352
+        ],
+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/webnn/resources/test_data/reduce_sum.json b/webnn/resources/test_data/reduce_sum.json
new file mode 100644
index 00000000000000..d382fb094472b1
--- /dev/null
+++ b/webnn/resources/test_data/reduce_sum.json
@@ -0,0 +1,886 @@
+{
+  "tests": [
+    {
+      "name": "reduceSum float32 1D tensor all positive default options",
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
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+            32.782341359545676,
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+            32.64223805640243,
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+            88.58631651618441,
+            98.85772999308332,
+            25.62685451963782,
+            60.17613590317996
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 1313.87939453125,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 1D tensor all negative default options",
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            -51.77016701194195,
+            -34.46467785404002,
+            -40.983506494422414,
+            -83.34922667410298,
+            -67.67525749941849,
+            -18.703119061215574,
+            -20.281060841405974,
+            -20.123054810748656,
+            -83.63451764199297,
+            -23.651085471445683,
+            -10.20843911447325,
+            -36.212902987180804,
+            -76.26201536868358,
+            -9.094745329540757,
+            -53.88933855716023,
+            -67.52341028201596,
+            -71.14580362299527,
+            -82.04484921713924,
+            -96.29924980334548,
+            -68.46700365583722,
+            -26.107193300700942,
+            -68.01821308673175,
+            -4.833076925370335,
+            -48.90069809349882
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+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": -1163.642578125,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 1D tensor all positive integers default options", // test with float32 values which are equivalent to integers
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            56,
+            90,
+            67,
+            33,
+            20,
+            58,
+            22,
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+            95,
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+            50,
+            57,
+            77,
+            89,
+            10,
+            2
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+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 1307,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 1D tensor all negative integers default options", // test with float32 values which are equivalent to integers
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            -55,
+            -36,
+            -74,
+            -17,
+            -67,
+            -95,
+            -3,
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+            -95,
+            -13,
+            -45,
+            -9,
+            -33,
+            -98,
+            -86,
+            -11,
+            -70,
+            -44,
+            -31,
+            -68,
+            -79,
+            -24,
+            -60,
+            -36
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": -1216,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 2D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [4, 6],
+          "data": [
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+            99.1748548274599,
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+            25.62685451963782,
+            60.17613590317996
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+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 1313.87939453125,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 3D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
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+            99.1748548274599,
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+          "type": "float32"
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+      },
+      "expected": {
+        "name": "output",
+        "data": 1313.87939453125,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 4D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
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+            25.62685451963782,
+            60.17613590317996
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+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 1313.87939453125,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 5D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [2, 1, 4, 1, 3],
+          "data": [
+            69.6038605453285,
+            99.1748548274599,
+            32.782341359545676,
+            8.881362932730319,
+            16.094296257493014,
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+            32.64223805640243,
+            43.998363476438975,
+            77.01777099153088,
+            79.79425759062173,
+            45.007949804090174,
+            24.39779644056914,
+            57.50268541631618,
+            57.60172895173401,
+            80.26985885745457,
+            43.65110628848945,
+            87.50368667545843,
+            94.50203768533922,
+            35.54289690580114,
+            42.856416416096344,
+            88.58631651618441,
+            98.85772999308332,
+            25.62685451963782,
+            60.17613590317996
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 1313.87939453125,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 3D tensor options.axes",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
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+            32.782341359545676,
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+            60.17613590317996
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+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [2]
+      },
+      "expected": {
+        "shape": [2, 3],
+        "data": [
+          210.44241333007812,
+          104.54179382324219,
+          226.2177734375,
+          239.025390625,
+          260.405029296875,
+          273.2470397949219
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+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 3D tensor negative options.axes",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
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+            60.17613590317996
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+          "type": "float32"
+        }
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+      "options": {
+        "axes": [-1]
+      },
+      "expected": {
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+        "data": [
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+          104.54179382324219,
+          226.2177734375,
+          239.025390625,
+          260.405029296875,
+          273.2470397949219
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 4D tensor options.axes",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            69.6038605453285,
+            99.1748548274599,
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+            60.17613590317996
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [0, 2]
+      },
+      "expected": {
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+        "data": [
+          179.63900756835938,
+          260.37457275390625,
+          219.3611297607422,
+          246.83712768554688,
+          157.4895782470703,
+          250.1780242919922
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 4D tensor negative options.axes",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            69.6038605453285,
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+            87.50368667545843,
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+            35.54289690580114,
+            42.856416416096344,
+            88.58631651618441,
+            98.85772999308332,
+            25.62685451963782,
+            60.17613590317996
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [-4, -2]
+      },
+      "expected": {
+        "shape": [2, 3],
+        "data": [
+          179.63900756835938,
+          260.37457275390625,
+          219.3611297607422,
+          246.83712768554688,
+          157.4895782470703,
+          250.1780242919922
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 3D tensor options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            69.6038605453285,
+            99.1748548274599,
+            32.782341359545676,
+            8.881362932730319,
+            16.094296257493014,
+            11.806894918510213,
+            32.64223805640243,
+            43.998363476438975,
+            77.01777099153088,
+            79.79425759062173,
+            45.007949804090174,
+            24.39779644056914,
+            57.50268541631618,
+            57.60172895173401,
+            80.26985885745457,
+            43.65110628848945,
+            87.50368667545843,
+            94.50203768533922,
+            35.54289690580114,
+            42.856416416096344,
+            88.58631651618441,
+            98.85772999308332,
+            25.62685451963782,
+            60.17613590317996
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": false
+      },
+      "expected": {
+        "data": 1313.87939453125,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 3D tensor options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            69.6038605453285,
+            99.1748548274599,
+            32.782341359545676,
+            8.881362932730319,
+            16.094296257493014,
+            11.806894918510213,
+            32.64223805640243,
+            43.998363476438975,
+            77.01777099153088,
+            79.79425759062173,
+            45.007949804090174,
+            24.39779644056914,
+            57.50268541631618,
+            57.60172895173401,
+            80.26985885745457,
+            43.65110628848945,
+            87.50368667545843,
+            94.50203768533922,
+            35.54289690580114,
+            42.856416416096344,
+            88.58631651618441,
+            98.85772999308332,
+            25.62685451963782,
+            60.17613590317996
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": true
+      },
+      "expected": {
+        "shape": [1, 1, 1],
+        "data": 1313.87939453125,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 4D tensor options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            69.6038605453285,
+            99.1748548274599,
+            32.782341359545676,
+            8.881362932730319,
+            16.094296257493014,
+            11.806894918510213,
+            32.64223805640243,
+            43.998363476438975,
+            77.01777099153088,
+            79.79425759062173,
+            45.007949804090174,
+            24.39779644056914,
+            57.50268541631618,
+            57.60172895173401,
+            80.26985885745457,
+            43.65110628848945,
+            87.50368667545843,
+            94.50203768533922,
+            35.54289690580114,
+            42.856416416096344,
+            88.58631651618441,
+            98.85772999308332,
+            25.62685451963782,
+            60.17613590317996
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": false
+      },
+      "expected": {
+        "data": 1313.87939453125,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 4D tensor options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            69.6038605453285,
+            99.1748548274599,
+            32.782341359545676,
+            8.881362932730319,
+            16.094296257493014,
+            11.806894918510213,
+            32.64223805640243,
+            43.998363476438975,
+            77.01777099153088,
+            79.79425759062173,
+            45.007949804090174,
+            24.39779644056914,
+            57.50268541631618,
+            57.60172895173401,
+            80.26985885745457,
+            43.65110628848945,
+            87.50368667545843,
+            94.50203768533922,
+            35.54289690580114,
+            42.856416416096344,
+            88.58631651618441,
+            98.85772999308332,
+            25.62685451963782,
+            60.17613590317996
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": true
+      },
+      "expected": {
+        "shape": [1, 1, 1, 1],
+        "data": 1313.87939453125,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 4D tensor options.axes with options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            69.6038605453285,
+            99.1748548274599,
+            32.782341359545676,
+            8.881362932730319,
+            16.094296257493014,
+            11.806894918510213,
+            32.64223805640243,
+            43.998363476438975,
+            77.01777099153088,
+            79.79425759062173,
+            45.007949804090174,
+            24.39779644056914,
+            57.50268541631618,
+            57.60172895173401,
+            80.26985885745457,
+            43.65110628848945,
+            87.50368667545843,
+            94.50203768533922,
+            35.54289690580114,
+            42.856416416096344,
+            88.58631651618441,
+            98.85772999308332,
+            25.62685451963782,
+            60.17613590317996
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [1, 3]
+      },
+      "expected": {
+        "shape": [2, 2],
+        "data": [
+          355.21942138671875,
+          185.98255920410156,
+          362.3598937988281,
+          410.3175354003906
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 4D tensor options.axes with options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            69.6038605453285,
+            99.1748548274599,
+            32.782341359545676,
+            8.881362932730319,
+            16.094296257493014,
+            11.806894918510213,
+            32.64223805640243,
+            43.998363476438975,
+            77.01777099153088,
+            79.79425759062173,
+            45.007949804090174,
+            24.39779644056914,
+            57.50268541631618,
+            57.60172895173401,
+            80.26985885745457,
+            43.65110628848945,
+            87.50368667545843,
+            94.50203768533922,
+            35.54289690580114,
+            42.856416416096344,
+            88.58631651618441,
+            98.85772999308332,
+            25.62685451963782,
+            60.17613590317996
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [1, 3]
+      },
+      "expected": {
+        "shape": [1, 2, 2, 1],
+        "data": [
+          355.21942138671875,
+          185.98255920410156,
+          362.3598937988281,
+          410.3175354003906
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 4D tensor negative options.axes with options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            69.6038605453285,
+            99.1748548274599,
+            32.782341359545676,
+            8.881362932730319,
+            16.094296257493014,
+            11.806894918510213,
+            32.64223805640243,
+            43.998363476438975,
+            77.01777099153088,
+            79.79425759062173,
+            45.007949804090174,
+            24.39779644056914,
+            57.50268541631618,
+            57.60172895173401,
+            80.26985885745457,
+            43.65110628848945,
+            87.50368667545843,
+            94.50203768533922,
+            35.54289690580114,
+            42.856416416096344,
+            88.58631651618441,
+            98.85772999308332,
+            25.62685451963782,
+            60.17613590317996
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [1, -1]
+      },
+      "expected": {
+        "shape": [2, 2],
+        "data": [
+          355.21942138671875,
+          185.98255920410156,
+          362.3598937988281,
+          410.3175354003906
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 4D tensor negative options.axes with options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            69.6038605453285,
+            99.1748548274599,
+            32.782341359545676,
+            8.881362932730319,
+            16.094296257493014,
+            11.806894918510213,
+            32.64223805640243,
+            43.998363476438975,
+            77.01777099153088,
+            79.79425759062173,
+            45.007949804090174,
+            24.39779644056914,
+            57.50268541631618,
+            57.60172895173401,
+            80.26985885745457,
+            43.65110628848945,
+            87.50368667545843,
+            94.50203768533922,
+            35.54289690580114,
+            42.856416416096344,
+            88.58631651618441,
+            98.85772999308332,
+            25.62685451963782,
+            60.17613590317996
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [-3, 3]
+      },
+      "expected": {
+        "shape": [1, 2, 2, 1],
+        "data": [
+          355.21942138671875,
+          185.98255920410156,
+          362.3598937988281,
+          410.3175354003906
+        ],
+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/webnn/resources/utils.js b/webnn/resources/utils.js
index 96b98404b94812..4c7b6bc6ef148e 100644
--- a/webnn/resources/utils.js
+++ b/webnn/resources/utils.js
@@ -70,9 +70,10 @@ const getExpectedDataAndType = (resources, outputName) => {
 /**
  * Get ULP tolerance of conv2d operation.
  * @param {Object} resources - Resources used for building a graph
+ * @param {String} operationName - An operation name
  * @returns {Number} A tolerance number
  */
-const getConv2dPrecisionTolerance = (resources) => {
+const getConv2dPrecisionTolerance = (resources, operationName) => {
   // number of reduced input elements multiplied by filter and summed (a sliding dot product like pooling)
   const inputNameArray = Object.keys(resources.inputs);
   const inputShape = resources.inputs[inputNameArray[0]].shape;
@@ -121,9 +122,10 @@ const getConv2dPrecisionTolerance = (resources) => {
 /**
  * Get ULP tolerance of gemm operation.
  * @param {Object} resources - Resources used for building a graph
+ * @param {String} operationName - An operation name
  * @returns {Number} A tolerance number
  */
-const getGemmPrecisionTolerance = (resources) => {
+const getGemmPrecisionTolerance = (resources, operationName) => {
   // GEMM : alpha * (A x B) + beta * C
   // An upper bound for the worst serial ordering is bounded by
   // the number of lossy operations, where matrix multiplication
@@ -150,9 +152,10 @@ const getGemmPrecisionTolerance = (resources) => {
 /**
  * Get ULP tolerance of matmul operation.
  * @param {Object} resources - Resources used for building a graph
+ * @param {String} operationName - An operation name
  * @returns {Number} A tolerance number
  */
-const getMatmulPrecisionTolerance = (resources) => {
+const getMatmulPrecisionTolerance = (resources, operationName) => {
   // Matmul : Compute the matrix product of two input tensors.
   // If a is 1-D, WebNN converts it to a 2-D tensor by prepending a 1 to its dimensions, [n] -> [1, n].
   // So we can just always check the last dimension here.
@@ -164,9 +167,10 @@ const getMatmulPrecisionTolerance = (resources) => {
 /**
  * Get ULP tolerance of averagePool2d operation.
  * @param {Object} resources - Resources used for building a graph
+ * @param {String} operationName - An operation name
  * @returns {Number} A tolerance number
  */
-const getAveragePool2dPrecisionTolerance = (resources) => {
+const getAveragePool2dPrecisionTolerance = (resources, operationName) => {
   const inputShape = resources.inputs[Object.keys(resources.inputs)[0]].shape;
   let height;
   let width;
@@ -193,15 +197,43 @@ const getAveragePool2dPrecisionTolerance = (resources) => {
 /**
  * Get ULP tolerance of softmax operation.
  * @param {Object} resources - Resources used for building a graph
+ * @param {String} operationName - An operation name
  * @returns {Number} A tolerance number
  */
-const getSoftmaxPrecisionTolerance = (resources) => {
+const getSoftmaxPrecisionTolerance = (resources, operationName) => {
   // Compute the softmax values of the 2-D input tensor along axis 1.
   const inputShape = resources.inputs[Object.keys(resources.inputs)[0]].shape;
   const tolerance = inputShape[1] * 3 + 3;
   return tolerance;
 };
 
+/**
+ * Get ULP tolerance of reduceMean, reduceProduct, reduceSum operations.
+ * @param {Object} resources - Resources used for building a graph
+ * @param {String} operationName - An operation name
+ * @returns {Number} A tolerance number
+ */
+const getReductionPrecisionTolerance = (resources, operationName) => {
+  const inputShape = resources.inputs[Object.keys(resources.inputs)[0]].shape;
+  const rank = inputShape.length;
+  const options = {...resources.options};
+  let sizes;
+  if (options && options.axes) {
+    sizes = options.axes.map(
+                (axis) => axis < 0 ? inputShape[axis + rank] : inputShape[axis]
+    );
+  } else {
+    sizes = inputShape;
+  }
+  let tolerance = sizes.reduce(
+                      (accumulator, currentValue) => accumulator * currentValue
+  );
+  if (operationName === 'reduceMean') {
+    tolerance += 2;
+  }
+  return tolerance;
+};
+
 // Refer to precision metrics on https://github.com/webmachinelearning/webnn/issues/265#issuecomment-1256242643
 const PrecisionMetrics = {
   batchNormalization: {ULP: {float32: 6, float16: 6}},
@@ -235,6 +267,13 @@ const PrecisionMetrics = {
   averagePool2d: {ULP: {float32: getAveragePool2dPrecisionTolerance, float16: getAveragePool2dPrecisionTolerance}},
   maxPool2d: {ULP: {float32: 0, float16: 0}},
   // End Pooling operations
+  // Begin Reduction operations
+  reduceMax: {ULP: {float32: 0, float16: 0}},
+  reduceMean: {ULP: {float32: getReductionPrecisionTolerance, float16: getReductionPrecisionTolerance}},
+  reduceMin: {ULP: {float32: 0, float16: 0}},
+  reduceProduct: {ULP: {float32: getReductionPrecisionTolerance, float16: getReductionPrecisionTolerance}},
+  reduceSum: {ULP: {float32: getReductionPrecisionTolerance, float16: getReductionPrecisionTolerance}},
+  // End Reduction operations
   relu: {ULP: {float32: 0, float16: 0}},
   reshape: {ULP: {float32: 0, float16: 0}},
   sigmoid: {ULP: {float32: 32+2, float16: 3}}, // float32 (leaving a few ULP for roundoff)
@@ -259,7 +298,7 @@ const getPrecisonTolerance = (operationName, metricType, resources) => {
   let tolerance = PrecisionMetrics[operationName][metricType][precisionType];
   // If the tolerance is dynamic, then evaluate the function to get the value.
   if (tolerance instanceof Function) {
-    tolerance = tolerance(resources);
+    tolerance = tolerance(resources, operationName);
   }
   return tolerance;
 };