diff --git a/tests/entrypoints/scale_out/token_in_token_out/test_serving_tokens.py b/tests/entrypoints/scale_out/token_in_token_out/test_serving_tokens.py
index a1491c1c3e1b..cd0da519c1c7 100644
--- a/tests/entrypoints/scale_out/token_in_token_out/test_serving_tokens.py
+++ b/tests/entrypoints/scale_out/token_in_token_out/test_serving_tokens.py
@@ -462,3 +462,24 @@ async def test_generate_with_lora_adapter(client, tokenizer, messages):
     completions_res = completions_data["choices"][0]["message"]["content"]
 
     assert generate_res == completions_res
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("n", [2, 4])
+async def test_generate_endpoint_returns_all_n_choices(client, n):
+    # Regression test for #52398: a non-streaming /inference/v1/generate
+    # request with n > 1 must return exactly n choices. The handler previously
+    # left output_kind at the SamplingParams default (CUMULATIVE) for
+    # non-streaming requests, so sequences that finished before the final
+    # streamed step were dropped and fewer than n choices came back.
+    payload = {
+        "model": MODEL_NAME,
+        "token_ids": [9707, 1879, 11],
+        "sampling_params": {"max_tokens": 5, "temperature": 1.0, "n": n},
+        "stream": False,
+    }
+    resp = await client.post(GEN_ENDPOINT, json=payload)
+    resp.raise_for_status()
+    data = resp.json()
+    assert len(data["choices"]) == n
+    assert sorted(c["index"] for c in data["choices"]) == list(range(n))
diff --git a/vllm/entrypoints/scale_out/token_in_token_out/serving.py b/vllm/entrypoints/scale_out/token_in_token_out/serving.py
index 9e9ace877a7c..24978ffb3475 100644
--- a/vllm/entrypoints/scale_out/token_in_token_out/serving.py
+++ b/vllm/entrypoints/scale_out/token_in_token_out/serving.py
@@ -218,8 +218,14 @@ async def serve_tokens(
 
         if self.force_no_detokenize:
             sampling_params.detokenize = False
-        if request.stream:
-            sampling_params.output_kind = RequestOutputKind.DELTA
+        # Match the OpenAI serving path (CompletionRequest.to_sampling_params):
+        # non-streaming must use FINAL_ONLY. Under the SamplingParams default
+        # (CUMULATIVE) serve_tokens_full_generator keeps only the last streamed
+        # RequestOutput, and sequences that finished earlier are already pruned
+        # from it, so with n > 1 they silently never reach `choices`.
+        sampling_params.output_kind = (
+            RequestOutputKind.DELTA if request.stream else RequestOutputKind.FINAL_ONLY
+        )
 
         self._log_inputs(
             request_id,