diff --git a/tests/samplers/test_sampler.py b/tests/samplers/test_sampler.py
index 5173683d25a27..c72ec11b5cb0d 100644
--- a/tests/samplers/test_sampler.py
+++ b/tests/samplers/test_sampler.py
@@ -637,3 +637,72 @@ def mock_sample(probs, *args, **kwargs):
     hf_probs = torch.softmax(hf_probs, dim=-1, dtype=torch.float)
     assert torch.allclose(hf_probs, sample_probs, atol=1e-5)
     assert torch.equal(hf_probs.eq(0), sample_probs.eq(0))
+
+
+@pytest.mark.parametrize("device", CUDA_DEVICES)
+def test_sampler_repetition_penalty_mixed(device: str):
+
+    vocab_size = 8
+
+    def test_sampling_params(sampling_params: List[SamplingParams]):
+
+        seq_group_metadata_list: List[SequenceGroupMetadata] = []
+        seq_lens: List[int] = []
+        for i in range(2):
+            seq_group_metadata_list.append(
+                SequenceGroupMetadata(
+                    request_id=f"test_{i}",
+                    is_prompt=True,
+                    seq_data={0: SequenceData([1, 2, 3])},
+                    sampling_params=sampling_params[i],
+                    block_tables={0: [1]},
+                ))
+            seq_lens.append(seq_group_metadata_list[-1].seq_data[0].get_len())
+
+        sampling_metadata = SamplingMetadata.prepare(
+            seq_group_metadata_list,
+            seq_lens,
+            query_lens=seq_lens,
+            device=device,
+            pin_memory=is_pin_memory_available())
+
+        fake_logits = torch.full((2, vocab_size),
+                                 1e-2,
+                                 device=device,
+                                 dtype=torch.float16)
+
+        fake_logits[:, 5] = 1.1e-2
+        fake_logits[:, 1] = 1.2e-2
+
+        sampler = MockLogitsSampler(fake_logits)
+
+        sampler_output = sampler(logits=fake_logits,
+                                 sampling_metadata=sampling_metadata)
+
+        generated_tokens = []
+        for output in sampler_output:
+            generated_tokens.append(output.samples[0].output_token)
+
+        return generated_tokens
+
+    # one configuration is greedy with repetition_penalty
+    sampling_params_rep = SamplingParams(
+        temperature=0.0,
+        repetition_penalty=2.0,
+    )
+
+    # other configuration is sampling w/o repetition_penalty
+    sampling_params_sample = SamplingParams(
+        temperature=1.0,
+        top_k=1,
+        seed=42,
+    )
+
+    tokens1 = test_sampling_params(
+        [sampling_params_rep, sampling_params_sample])
+
+    tokens2 = test_sampling_params(
+        [sampling_params_sample, sampling_params_rep])
+
+    assert tokens1[0] == tokens2[1]
+    assert tokens1[1] == tokens2[0]