diff --git a/tests/test_clustering.py b/tests/test_clustering.py
index a15f6dc1d3..0afb7a4266 100644
--- a/tests/test_clustering.py
+++ b/tests/test_clustering.py
@@ -4,6 +4,9 @@
 
 import splink.comparison_library as cl
 from splink import DuckDBAPI, Linker, SettingsCreator, block_on
+from splink.clustering import (
+    cluster_pairwise_predictions_at_multiple_thresholds
+)
 
 from .basic_settings import get_settings_dict
 from .decorator import mark_with_dialects_excluding
@@ -126,3 +129,93 @@ def test_clustering_no_edges(test_helpers, dialect):
     # due to blocking rules, df_predict will be empty
     df_predict = linker.inference.predict()
     linker.clustering.cluster_pairwise_predictions_at_threshold(df_predict, 0.95)
+
+@mark_with_dialects_excluding()
+@mark.parametrize(
+    ["link_type", "input_pd_tables"],
+    [
+        ["dedupe_only", [df]],
+        ["link_only", [df, df]],  # no source dataset
+        ["link_only", [df_l, df_r]],  # source dataset column
+        ["link_only", [df_combined]],  # concatenated frame
+        ["link_only", [df_l, df_m, df_r]],
+        ["link_and_dedupe", [df, df]],  # no source dataset
+        ["link_and_dedupe", [df_l, df_r]],  # source dataset column
+        ["link_and_dedupe", [df_combined]],  # concatenated frame
+    ],
+    ids=[
+        "dedupe",
+        "link_only_no_source_dataset",
+        "link_only_with_source_dataset",
+        "link_only_concat",
+        "link_only_three_tables",
+        "link_and_dedupe_no_source_dataset",
+        "link_and_dedupe_with_source_dataset",
+        "link_and_dedupe_concat",
+    ],
+)
+def test_clustering_multi_thresholds(test_helpers, dialect, link_type, input_pd_tables):
+    helper = test_helpers[dialect]
+
+    settings = SettingsCreator(
+        link_type=link_type,
+        comparisons=[
+            cl.ExactMatch("first_name"),
+            cl.ExactMatch("surname"),
+            cl.ExactMatch("dob"),
+            cl.ExactMatch("city"),
+        ],
+        blocking_rules_to_generate_predictions=[
+            block_on("surname"),
+            block_on("dob"),
+        ],
+    )
+    linker_input = list(map(helper.convert_frame, input_pd_tables))
+    linker = Linker(linker_input, settings, **helper.extra_linker_args())
+
+    df_predict = linker.inference.predict()
+    linker.clustering.cluster_pairwise_predictions_at_multiple_thresholds(df_predict, [0.5, 0.95])
+
+
+def test_clustering_single_multi_threshold_equivalence():
+    df = pd.read_csv("./tests/datasets/fake_1000_from_splink_demos.csv")
+    db_api = DuckDBAPI()
+    settings_dict = get_settings_dict()
+    linker = Linker(df, settings_dict, db_api=db_api)
+
+    df_predict = linker.inference.predict()
+
+    clusters_0_5 =     linker.clustering.cluster_pairwise_predictions_at_threshold(df_predict, 0.5).as_pandas_dataframe()
+    clusters_0_95 =    linker.clustering.cluster_pairwise_predictions_at_threshold(df_predict, 0.95).as_pandas_dataframe()
+
+    clusters_multi = linker.clustering.cluster_pairwise_predictions_at_multiple_thresholds(df_predict, [0.5, 0.95]).as_pandas_dataframe()
+
+    assert clusters_0_5["cluster_id"] == clusters_multi["cluster_p_0_5"]
+    assert clusters_0_95["cluster_id"] == clusters_multi["cluster_p_0_95"] 
+
+
+
+def test_clustering_multi_threshold_linker_non_linker_equivalence():
+    df = pd.read_csv("./tests/datasets/fake_1000_from_splink_demos.csv")
+    db_api = DuckDBAPI()
+    settings_dict = get_settings_dict()
+    linker = Linker(df, settings_dict, db_api=db_api)
+
+    df_predict = linker.inference.predict()
+
+    clusters_linker = linker.clustering.cluster_pairwise_predictions_at_multiple_thresholds(
+        df_predict, 
+        [0.5, 0.95]
+        ).as_pandas_dataframe()
+    clusters_non_linker = cluster_pairwise_predictions_at_multiple_thresholds(
+                            df,
+                            df_predict,
+                            node_id_column_name="unique_id",
+                            edge_id_column_name_left="unique_id_l",
+                            edge_id_column_name_right="unique_id_r",
+                            db_api=linker.db_api,
+                            match_probability_thresholds=[0.5, 0.95]
+                        ).as_pandas_dataframe()
+    
+    assert clusters_linker["cluster_p_0_5"] == clusters_non_linker["cluster_p_0_5"]
+    assert clusters_linker["cluster_p_0_95"] == clusters_non_linker["cluster_p_0_95"]
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