diff --git a/cpp/benchmarks/io/nvbench_helpers.hpp b/cpp/benchmarks/io/nvbench_helpers.hpp
index ebffb930c275..99aaa55d4a5a 100644
--- a/cpp/benchmarks/io/nvbench_helpers.hpp
+++ b/cpp/benchmarks/io/nvbench_helpers.hpp
@@ -137,6 +137,8 @@ NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
 
 enum class converts_strings : bool { YES, NO };
 
+enum class output_dict : bool { YES, NO };
+
 enum class uses_pandas_metadata : bool { YES, NO };
 
 NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
@@ -150,6 +152,17 @@ NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
   },
   [](auto) { return std::string{}; })
 
+NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
+  output_dict,
+  [](auto value) {
+    switch (value) {
+      case output_dict::YES: return "YES";
+      case output_dict::NO: return "NO";
+      default: return "Unknown";
+    }
+  },
+  [](auto) { return std::string{}; })
+
 NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
   uses_pandas_metadata,
   [](auto value) {
diff --git a/cpp/benchmarks/io/parquet/parquet_reader_options.cpp b/cpp/benchmarks/io/parquet/parquet_reader_options.cpp
index 07c12ab41fab..c0e114c73d6f 100644
--- a/cpp/benchmarks/io/parquet/parquet_reader_options.cpp
+++ b/cpp/benchmarks/io/parquet/parquet_reader_options.cpp
@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright (c) 2022-2026, NVIDIA CORPORATION.
+ * SPDX-FileCopyrightText: Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
  * SPDX-License-Identifier: Apache-2.0
  */
 
@@ -37,18 +37,21 @@ template <column_selection ColSelection,
           row_selection RowSelection,
           converts_strings ConvertsStrings,
           uses_pandas_metadata UsesPandasMetadata,
+          output_dict OutputDict,
           cudf::type_id Timestamp>
 void BM_parquet_read_options(nvbench::state& state,
                              nvbench::type_list<nvbench::enum_type<ColSelection>,
                                                 nvbench::enum_type<RowSelection>,
                                                 nvbench::enum_type<ConvertsStrings>,
                                                 nvbench::enum_type<UsesPandasMetadata>,
+                                                nvbench::enum_type<OutputDict>,
                                                 nvbench::enum_type<Timestamp>>)
 {
   auto const num_chunks = RowSelection == row_selection::ALL ? 1 : chunked_read_num_chunks;
 
-  auto constexpr str_to_categories = ConvertsStrings == converts_strings::YES;
-  auto constexpr uses_pd_metadata  = UsesPandasMetadata == uses_pandas_metadata::YES;
+  auto constexpr str_to_categories   = ConvertsStrings == converts_strings::YES;
+  auto constexpr output_dict_columns = OutputDict == output_dict::YES;
+  auto constexpr uses_pd_metadata    = UsesPandasMetadata == uses_pandas_metadata::YES;
 
   auto const ts_type = cudf::data_type{Timestamp};
 
@@ -84,6 +87,7 @@ void BM_parquet_read_options(nvbench::state& state,
     cudf::io::parquet_reader_options::builder(source_sink.make_source_info())
       .column_names(cols_to_read)
       .convert_strings_to_categories(str_to_categories)
+      .output_dict_columns(output_dict_columns)
       .use_pandas_metadata(uses_pd_metadata)
       .timestamp_type(ts_type);
 
@@ -139,12 +143,14 @@ NVBENCH_BENCH_TYPES(BM_parquet_read_options,
                                       row_selections,
                                       nvbench::enum_type_list<converts_strings::YES>,
                                       nvbench::enum_type_list<uses_pandas_metadata::YES>,
+                                      nvbench::enum_type_list<output_dict::NO>,
                                       nvbench::enum_type_list<cudf::type_id::EMPTY>))
   .set_name("parquet_read_row_selection")
   .set_type_axes_names({"column_selection",
                         "row_selection",
                         "str_to_categories",
                         "uses_pandas_metadata",
+                        "output_dict_columns",
                         "timestamp_type"})
   .set_min_samples(4)
   // NOTE: row_selection::ROW_GROUPS reads a fraction of row groups; non-zero
@@ -161,12 +167,14 @@ NVBENCH_BENCH_TYPES(BM_parquet_read_options,
                                       nvbench::enum_type_list<row_selection::ALL>,
                                       nvbench::enum_type_list<converts_strings::YES>,
                                       nvbench::enum_type_list<uses_pandas_metadata::YES>,
+                                      nvbench::enum_type_list<output_dict::NO>,
                                       nvbench::enum_type_list<cudf::type_id::EMPTY>))
   .set_name("parquet_read_column_selection")
   .set_type_axes_names({"column_selection",
                         "row_selection",
                         "str_to_categories",
                         "uses_pandas_metadata",
+                        "output_dict_columns",
                         "timestamp_type"})
   .set_min_samples(4)
   .add_int64_axis("row_group_size_bytes", {0})
@@ -178,13 +186,39 @@ NVBENCH_BENCH_TYPES(
                     nvbench::enum_type_list<row_selection::ALL>,
                     nvbench::enum_type_list<converts_strings::YES, converts_strings::NO>,
                     nvbench::enum_type_list<uses_pandas_metadata::YES, uses_pandas_metadata::NO>,
+                    nvbench::enum_type_list<output_dict::NO>,
                     nvbench::enum_type_list<cudf::type_id::EMPTY>))
   .set_name("parquet_read_misc_options")
   .set_type_axes_names({"column_selection",
                         "row_selection",
                         "str_to_categories",
                         "uses_pandas_metadata",
+                        "output_dict_columns",
                         "timestamp_type"})
   .set_min_samples(4)
   .add_int64_axis("row_group_size_bytes", {0})
   .add_int64_axis("row_group_size_rows", {0});
+
+// Sweep `output_dict_columns` on/off. Only flat STRING columns are dictionary-transcoded, so this
+// case reports read throughput and peak memory for both the direct transcode (YES) and the plain
+// STRING materialization (NO). Varying `row_group_size_rows` exercises the single-row-group fast
+// path (few, large row groups) versus the multi-row-group concatenate path (many, small ones).
+NVBENCH_BENCH_TYPES(BM_parquet_read_options,
+                    NVBENCH_TYPE_AXES(nvbench::enum_type_list<column_selection::ALL>,
+                                      nvbench::enum_type_list<row_selection::ALL>,
+                                      nvbench::enum_type_list<converts_strings::NO>,
+                                      nvbench::enum_type_list<uses_pandas_metadata::NO>,
+                                      nvbench::enum_type_list<output_dict::YES, output_dict::NO>,
+                                      nvbench::enum_type_list<cudf::type_id::EMPTY>))
+  .set_name("parquet_read_dict_output")
+  .set_type_axes_names({"column_selection",
+                        "row_selection",
+                        "str_to_categories",
+                        "uses_pandas_metadata",
+                        "output_dict_columns",
+                        "timestamp_type"})
+  .set_min_samples(4)
+  .add_int64_axis("row_group_size_bytes", {0})
+  // 0 == cuDF default (1,000,000 rows/RG → few, large row groups); 100,000 forces ~10x more,
+  // smaller row groups, exercising the multi-row-group concatenate path.
+  .add_int64_axis("row_group_size_rows", {0, 100'000});