diff --git a/spark/src/test/scala/org/apache/spark/sql/benchmark/CometExplodeBenchmark.scala b/spark/src/test/scala/org/apache/spark/sql/benchmark/CometExplodeBenchmark.scala
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+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+package org.apache.spark.sql.benchmark
+
+import java.io.File
+
+/**
+ * Benchmark to measure performance of Comet's explode operator (`CometExplodeExec`) against
+ * Spark's `GenerateExec`, across the dimensions that drive generator cost: fan-out, generator
+ * variant, element type, and the number of columns replicated alongside the generated one. To
+ * run:
+ * {{{
+ *   SPARK_GENERATE_BENCHMARK_FILES=1 make benchmark-org.apache.spark.sql.benchmark.CometExplodeBenchmark
+ * }}}
+ *
+ * `runExpressionBenchmark` reports whole-query totals, so the times below also include the
+ * Parquet scan, the result transfer, and the per-iteration query planning. That fixed cost is a
+ * large share of the total at fan-out 2, where it compresses the ratio between the two engines,
+ * and a small one at fan-out 100. Issue #5363 tracks reporting operator cost against a scan
+ * baseline instead; when that lands, this paragraph should go.
+ */
+object CometExplodeBenchmark extends CometBenchmarkBase {
+
+  private val numRows = 256 * 1024
+
+  /**
+   * A SQL expression for an array column of `len` elements of `elementExpr`, where `elementExpr`
+   * may reference the row's `id` and the element's one-based position `x`.
+   *
+   * One in ten rows holds a null array and another one in ten holds an empty array, so that
+   * `explode` and `explode_outer` are a real comparison rather than the same query twice: the
+   * outer variants emit a null row for those 20% of rows where the plain variants emit nothing.
+   *
+   * The empty array is built with `slice`, not `array()`, because `array()` types as
+   * `array<null>` and would give that row's column a different element type.
+   */
+  private def arrayColumn(elementExpr: String, len: Int): String = {
+    val full = s"transform(sequence(1, $len), x -> $elementExpr)"
+    s"""CASE
+       |  WHEN id % 10 = 0 THEN NULL
+       |  WHEN id % 10 = 1 THEN slice($full, 1, 0)
+       |  ELSE $full
+       |END AS arr""".stripMargin
+  }
+
+  /**
+   * The temp views the benchmark reads, each with the expressions that build it.
+   *
+   * Each array column gets its own view rather than sharing one wide table, so that a case is
+   * never charged for scanning an array column it does not read.
+   */
+  private val datasets: Seq[(String, Seq[String])] = Seq(
+    "arr_len2" -> Seq(arrayColumn("id + x", 2)),
+    "arr_len10" -> Seq(arrayColumn("id + x", 10)),
+    "arr_len100" -> Seq(arrayColumn("id + x", 100)),
+    "arr_str10" -> Seq(arrayColumn("concat('str_', CAST(id + x AS STRING))", 10)),
+    "arr_struct10" -> Seq(
+      arrayColumn("struct(id + x AS a, concat('s', CAST(x AS STRING)) AS b)", 10)),
+    "arr_carry" -> Seq(
+      arrayColumn("id + x", 10),
+      "id AS k",
+      "CAST(id AS STRING) AS s",
+      "id * 2 AS v"))
+
+  /** Writes `selectExprs` over `numRows` rows to Parquet and registers it as a temp view. */
+  private def createView(dir: File, name: String, selectExprs: Seq[String]): Unit = {
+    val path = s"${dir.getAbsolutePath}/$name"
+    spark.range(numRows).selectExpr(selectExprs: _*).write.parquet(path)
+    spark.read.parquet(path).createOrReplaceTempView(name)
+  }
+
+  override def runCometBenchmark(mainArgs: Array[String]): Unit = {
+    withTempPath { dir =>
+      withTempTable(datasets.map(_._1): _*) {
+        datasets.foreach { case (name, selectExprs) => createView(dir, name, selectExprs) }
+
+        // Cardinality is input rows for every case, so the numbers are per scanned row rather
+        // than per generated row. Fan-out is named in the case title: the 100-element case emits
+        // roughly 50 times as many rows as the 2-element case from the same 256K inputs.
+        runBenchmark("Explode - fan-out") {
+          Seq(2, 10, 100).foreach { len =>
+            runExpressionBenchmark(
+              s"explode array<bigint>[$len]",
+              numRows,
+              s"SELECT explode(arr) FROM arr_len$len")
+          }
+        }
+
+        runBenchmark("Explode - generator variants") {
+          Seq("explode", "posexplode", "explode_outer", "posexplode_outer").foreach { generator =>
+            runExpressionBenchmark(
+              s"$generator array<bigint>[10]",
+              numRows,
+              s"SELECT $generator(arr) FROM arr_len10")
+          }
+        }
+
+        runBenchmark("Explode - element type") {
+          Seq("bigint" -> "arr_len10", "string" -> "arr_str10", "struct" -> "arr_struct10")
+            .foreach { case (elementType, view) =>
+              runExpressionBenchmark(
+                s"explode array<$elementType>[10]",
+                numRows,
+                s"SELECT explode(arr) FROM $view")
+            }
+        }
+
+        runBenchmark("Explode - carried columns") {
+          runExpressionBenchmark("explode alone", numRows, "SELECT explode(arr) FROM arr_carry")
+          runExpressionBenchmark(
+            "explode plus 3 carried columns",
+            numRows,
+            "SELECT k, s, v, explode(arr) FROM arr_carry")
+        }
+      }
+    }
+  }
+}