diff --git a/fastembed/rerank/cross_encoder/onnx_text_model.py b/fastembed/rerank/cross_encoder/onnx_text_model.py
index 6768d002..da854772 100644
--- a/fastembed/rerank/cross_encoder/onnx_text_model.py
+++ b/fastembed/rerank/cross_encoder/onnx_text_model.py
@@ -1,7 +1,7 @@
 import os
 from multiprocessing import get_all_start_methods
 from pathlib import Path
-from typing import Any, Iterable, Sequence, Type
+from typing import Any, Iterable, Sequence, Type, cast
 
 import numpy as np
 from tokenizers import Encoding
@@ -118,8 +118,13 @@ def _rerank_pairs(
         is_small = False
 
         if isinstance(pairs, tuple):
-            pairs = [pairs]
-            is_small = True
+            # a single (query, document) pair is a tuple of two strings,
+            # while a tuple of pairs is just another sequence of pairs
+            if len(pairs) == 2 and all(isinstance(item, str) for item in pairs):
+                pairs = [cast(tuple[str, str], pairs)]
+                is_small = True
+            else:
+                pairs = list(pairs)
 
         if isinstance(pairs, list):
             if len(pairs) < batch_size:
diff --git a/tests/test_text_cross_encoder.py b/tests/test_text_cross_encoder.py
index 85988ca8..bb3b98a9 100644
--- a/tests/test_text_cross_encoder.py
+++ b/tests/test_text_cross_encoder.py
@@ -135,6 +135,27 @@ def test_rerank_pairs_parallel(model_cache, model_name: str) -> None:
         ), f"Model: {model_name}, Scores (Parallel): {scores_parallel}, Expected: {canonical_scores}"
 
 
+@pytest.mark.parametrize("model_name", ["Xenova/ms-marco-MiniLM-L-6-v2"])
+def test_rerank_pairs_tuple_input(model_cache, model_name: str) -> None:
+    with model_cache(model_name) as model:
+        query = "What is the capital of France?"
+        documents = ["Paris is the capital of France.", "Berlin is the capital of Germany."]
+        pairs = [(query, doc) for doc in documents]
+        expected = np.array(list(model.rerank_pairs(pairs)))
+
+        # a tuple of pairs is an iterable of pairs, not a single pair
+        scores_tuple = np.array(list(model.rerank_pairs(tuple(pairs))))
+        assert np.allclose(scores_tuple, expected, atol=1e-5)
+
+        three_pairs = tuple(pairs + [pairs[0]])
+        scores_three = np.array(list(model.rerank_pairs(three_pairs)))
+        assert np.allclose(scores_three, np.append(expected, expected[0]), atol=1e-5)
+
+        # a lone (query, document) pair is still accepted
+        scores_single = np.array(list(model.rerank_pairs(pairs[0])))
+        assert np.allclose(scores_single, expected[:1], atol=1e-5)
+
+
 @pytest.mark.parametrize("model_name", ["Xenova/ms-marco-MiniLM-L-6-v2"])
 def test_token_count(model_cache, model_name: str) -> None:
     with model_cache(model_name) as model: