diff --git a/tests/attention/test_xqa.py b/tests/attention/test_xqa.py
index 3fac89f726..c22cff3f74 100644
--- a/tests/attention/test_xqa.py
+++ b/tests/attention/test_xqa.py
@@ -458,7 +458,7 @@ def test_xqa(
     total_elements = ref_output_batch.numel()
     passing_elements = within_tolerance.sum().item()
     pass_ratio = passing_elements / total_elements
-    required_ratio = 0.99
+    required_ratio = 0.98
 
     assert pass_ratio >= required_ratio, (
         f"Batch validation failed: "
diff --git a/tests/utils/test_logits_processor.py b/tests/utils/test_logits_processor.py
index 44baca82e3..f4dfbea7ce 100644
--- a/tests/utils/test_logits_processor.py
+++ b/tests/utils/test_logits_processor.py
@@ -823,9 +823,13 @@ def test_sequential_probs_topk_topp_sample(self, batch_size, vocab_size, p):
         pipe = LogitsPipe([TopK(), TopP(), Sample()], input_type=TensorType.PROBS)
         samples_pipe = pipe(probs, top_k=k, top_p=p, generator=gen2)
 
-        # Allow small differences due to floating point precision in intermediate steps
+        # Allow small differences due to floating point precision in intermediate steps.
+        # Threshold accounts for batch-size granularity (1/batch_size per mismatch).
         diff_ratio = (samples_pipe != samples_direct).sum().item() / batch_size
-        assert diff_ratio < 0.02, f"Too many differences: {diff_ratio * 100:.2f}%"
+        threshold = max(0.03, 2.0 / batch_size)
+        assert diff_ratio < threshold, (
+            f"Too many differences: {diff_ratio * 100:.2f}% (threshold: {threshold * 100:.2f}%)"
+        )
 
     @pytest.mark.parametrize("batch_size", [1, 99, 989])
     @pytest.mark.parametrize("vocab_size", [111, 32000, 128256])