diff --git a/tests/v1/e2e/test_context_length.py b/tests/v1/e2e/test_context_length.py
new file mode 100644
index 000000000000..0ac40bec35fe
--- /dev/null
+++ b/tests/v1/e2e/test_context_length.py
@@ -0,0 +1,63 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Tests for vLLM `vllm/v1/engine/processor.Processor._validate_model_input()`
+handling of maximum context length for decoder models.
+
+This test ensures:
+- A prompt that is one token shorter than the model's maximum context length
+  can be processed successfully when requesting one additional token.
+- A prompt that reaches the model's maximum context length throws a
+  `ValueError` when requesting at least one additional token.
+"""
+
+import pytest
+
+from tests.conftest import VllmRunner
+from tests.utils import create_new_process_for_each_test
+
+
+@create_new_process_for_each_test()
+@pytest.mark.parametrize("model, max_model_len", [("JackFram/llama-160m", 2048)])
+@pytest.mark.parametrize(
+    "prompt_len, max_tokens",
+    [
+        (2047, 1),  # prompt_len = max_model_len - 1 -> allowed
+        (2048, 1),  # prompt_len = max_model_len -> not allowed
+    ],
+)
+def test_decoder_max_context_length_validation(
+    model: str,
+    max_model_len: int,
+    vllm_runner: type[VllmRunner],
+    prompt_len: int,
+    max_tokens: int,
+) -> None:
+    """Check vLLM decoder model input validation for edge cases where
+    the prompt length is (almost) equal to the max model length."""
+
+    prompt_ids = [[43] * prompt_len]
+
+    with vllm_runner(
+        model_name=model,
+        tokenizer_name=model,
+        max_model_len=max_model_len,
+        max_num_seqs=1,
+        tensor_parallel_size=1,
+    ) as vllm_model:
+        if prompt_len + max_tokens <= max_model_len:
+            # Should succeed as constraints are met
+            vllm_model.generate_greedy(prompt_ids, max_tokens)
+        else:
+            # Should raise the ValueError defined in
+            # vllm/v1/engine/processor.Processor_validate_model_input()
+            expected_msg = (
+                f"The decoder prompt (length {prompt_len}) plus the number of "
+                f"requested output tokens (at least 1) is longer than "
+                f"the maximum model length of {max_model_len}. "
+                "Make sure that `max_model_len` is no smaller than the number of "
+                "text tokens (prompt + requested output tokens)."
+            )
+            with pytest.raises(ValueError) as excinfo:
+                vllm_model.generate_greedy(prompt_ids, max_tokens)
+            assert expected_msg in str(excinfo.value)
diff --git a/vllm/v1/engine/processor.py b/vllm/v1/engine/processor.py
index f2d992403e1a..69509d5d4712 100644
--- a/vllm/v1/engine/processor.py
+++ b/vllm/v1/engine/processor.py
@@ -575,6 +575,21 @@ class Processor:
             # check that chunked prefill does not truncate them
             # max_batch_len = self.scheduler_config.max_num_batched_tokens
 
+        if (
+            prompt_len == max_prompt_len
+            and prompt_type == "decoder"
+            and not model_config.is_multimodal_model
+        ):
+            suggestion = (
+                "Make sure that `max_model_len` is no smaller than the "
+                "number of text tokens (prompt + requested output tokens)."
+            )
+            raise ValueError(
+                f"The {prompt_type} prompt (length {prompt_len}) plus the number of "
+                f"requested output tokens (at least 1) is longer than the maximum "
+                f"model length of {max_prompt_len}. {suggestion}"
+            )
+
     def stat_mm_cache(self) -> MultiModalCacheStats | None:
         return self.input_preprocessor.stat_mm_cache()