diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py
index b14b6b1c3f52..893b8d93e1ee 100644
--- a/vllm/v1/worker/gpu_model_runner.py
+++ b/vllm/v1/worker/gpu_model_runner.py
@@ -2508,6 +2508,15 @@ def _model_forward(
             **model_kwargs,
         )
 
+    def _has_prefill_tokens_scheduled(
+        self,
+        num_scheduled_tokens: np.ndarray,
+        num_reqs: int,
+    ) -> bool:
+        prompt_lens = self.input_batch.num_prompt_tokens[:num_reqs]
+        num_computed = self.input_batch.num_computed_tokens_cpu[:num_reqs]
+        return np.any((num_scheduled_tokens > 0) & (num_computed < prompt_lens))
+
     @torch.inference_mode()
     def execute_model(
         self,
@@ -2616,6 +2625,12 @@ def execute_model(
             uniform_decode = (
                 max_num_scheduled_tokens == self.uniform_decode_query_len
             ) and (num_scheduled_tokens == num_reqs * max_num_scheduled_tokens)
+            # Disable uniform decode on steps that still process prompt tokens.
+            # This makes first-step behavior consistent regardless of prompt length.
+            if self._has_prefill_tokens_scheduled(
+                num_scheduled_tokens_np, self.input_batch.num_reqs
+            ):
+                uniform_decode = False
             batch_descriptor = BatchDescriptor(
                 num_tokens=num_input_tokens,
                 uniform_decode=uniform_decode,