diff --git a/python/sglang/srt/managers/tokenizer_manager.py b/python/sglang/srt/managers/tokenizer_manager.py
index 3a81a3636..b0416a065 100644
--- a/python/sglang/srt/managers/tokenizer_manager.py
+++ b/python/sglang/srt/managers/tokenizer_manager.py
@@ -565,14 +565,24 @@ class TokenizerManager:
         self, obj: Union[GenerateReqInput, EmbeddingReqInput], input_ids: List[int]
     ) -> None:
         """Validates that the input token count and the requested token count doesn't exceed the model's context length."""
+        # FIXME: unify the length validation logic with the one in the scheduler.
+        _max_req_len = self.context_len - 1
 
         input_token_num = len(input_ids) if input_ids is not None else 0
-        # Check if input alone exceeds context length
         if input_token_num >= self.context_len:
-            raise ValueError(
-                f"The input ({input_token_num} tokens) is longer than the "
-                f"model's context length ({self.context_len} tokens)."
-            )
+            if self.server_args.allow_auto_truncate:
+                logger.warning(
+                    f"The input ({input_token_num} tokens) is longer than the "
+                    f"model's context length ({self.context_len} tokens). "
+                    "Truncating the input."
+                )
+                input_ids = input_ids[:_max_req_len]
+                input_token_num = len(input_ids)
+            else:
+                raise ValueError(
+                    f"The input ({input_token_num} tokens) is longer than the "
+                    f"model's context length ({self.context_len} tokens)."
+                )
 
         if isinstance(obj, EmbeddingReqInput) and self.is_generation:
             raise ValueError(
@@ -584,17 +594,27 @@ class TokenizerManager:
         max_new_tokens = obj.sampling_params.get("max_new_tokens")
         if (
             max_new_tokens is not None
-            and (max_new_tokens + input_token_num) >= self.context_len
+            and (max_new_tokens + input_token_num) >= _max_req_len
         ):
-            total_tokens = max_new_tokens + input_token_num
-            error_msg = (
-                f"Requested token count exceeds the model's maximum context length "
-                f"of {self.context_len} tokens. You requested a total of {total_tokens} "
-                f"tokens: {input_token_num} tokens from the input messages and "
-                f"{max_new_tokens} tokens for the completion. Please reduce the number "
-                f"of tokens in the input messages or the completion to fit within the limit."
-            )
-            raise ValueError(error_msg)
+            if self.server_args.allow_auto_truncate:
+                logger.warning(
+                    f"Requested token count ({input_token_num} input + {max_new_tokens} new) "
+                    f"exceeds the model's context length ({self.context_len} tokens). "
+                    "Truncating max_new_tokens."
+                )
+                obj.sampling_params["max_new_tokens"] = max(
+                    0, _max_req_len - input_token_num
+                )
+            else:
+                total_tokens = max_new_tokens + input_token_num
+                error_msg = (
+                    f"Requested token count exceeds the model's maximum context length "
+                    f"of {self.context_len} tokens. You requested a total of {total_tokens} "
+                    f"tokens: {input_token_num} tokens from the input messages and "
+                    f"{max_new_tokens} tokens for the completion. Please reduce the number "
+                    f"of tokens in the input messages or the completion to fit within the limit."
+                )
+                raise ValueError(error_msg)
 
         if isinstance(obj, GenerateReqInput):
             if (