diff --git a/tests/spec_decode/test_utils.py b/tests/spec_decode/test_utils.py
index 06780d4b8cd01..195fce64822bd 100644
--- a/tests/spec_decode/test_utils.py
+++ b/tests/spec_decode/test_utils.py
@@ -4,10 +4,12 @@
 import torch
 
 from vllm.model_executor.layers.rejection_sampler import RejectionSampler
+from vllm.model_executor.layers.sampler import _get_ranks
 from vllm.model_executor.layers.typical_acceptance_sampler import (
     TypicalAcceptanceSampler)
 from vllm.sequence import SequenceGroupMetadata, get_all_seq_ids
-from vllm.spec_decode.util import split_batch_by_proposal_len
+from vllm.spec_decode.util import (get_sampled_token_logprobs,
+                                   split_batch_by_proposal_len)
 
 
 def test_get_all_seq_ids():
@@ -126,3 +128,20 @@ def mock_spec_decode_sampler(acceptance_sampler_method):
         return sampler
     else:
         raise ValueError(f"Invalid sampler name {acceptance_sampler_method}")
+
+
+def test_get_sampled_token_logprobs():
+    """Verify get_sampled_token_logprobs returns consistent rankings 
+    with regular get_ranks when probabilities match exactly.
+    """
+    logprob_tensor = torch.tensor(
+        [[[-.1, -.1]] * 2])  # shape (num_steps, batch_size, vocab_size)
+    sampled_token_tensor = torch.tensor([[1,
+                                          0]])  # shape (num_steps, batch_size)
+    ranks_spec_dec, _ = get_sampled_token_logprobs(logprob_tensor,
+                                                   sampled_token_tensor)
+
+    ranks_regular = _get_ranks(logprob_tensor.reshape((2, -1)),
+                               sampled_token_tensor.reshape(-1))
+
+    assert torch.equal(ranks_spec_dec.reshape(-1), ranks_regular)
diff --git a/vllm/spec_decode/util.py b/vllm/spec_decode/util.py
index d18ee47e23a5c..5d5f8767e5b6d 100644
--- a/vllm/spec_decode/util.py
+++ b/vllm/spec_decode/util.py
@@ -43,8 +43,8 @@ def get_sampled_token_logprobs(
                                        sampled_token_ids, ]
     expanded_selected_logprobs = selected_logprobs.unsqueeze(-1).expand(
         -1, -1, vocab_size)
-    sampled_token_ids_ranks = (logprob_tensor >=
-                               expanded_selected_logprobs).sum(-1)
+    sampled_token_ids_ranks = (logprob_tensor >
+                               expanded_selected_logprobs).sum(-1).add_(1)
 
     return sampled_token_ids_ranks, selected_logprobs