diff --git a/eval/task.py b/eval/task.py
index 70962115..204c2a12 100644
--- a/eval/task.py
+++ b/eval/task.py
@@ -15,6 +15,9 @@
 from lm_eval.api.instance import Instance
 from lm_eval.api.model import LM
 
+# Safety buffer for vLLM max_gen_toks calculation
+VLLM_SAFETY_BUFFER_TOKENS = 16
+
 
 class BaseBenchmark(ABC):
     """Abstract base class for implementing LLM evaluation benchmarks."""
@@ -53,7 +56,37 @@ def _normalize_model_args(self, model: LM, instances: List[Instance]) -> List[In
                     if "4o" in model.model:
                         instance.args[1]["max_tokens"] = min(max_new_tokens, 16384)
                 elif isinstance(model, lm_eval_models.vllm_causallms.VLLM):
-                    instance.args[1]["max_gen_toks"] = max_new_tokens
+                    try:
+                        # Get prompt from instance.args[0] (the templated string)
+                        prompt = instance.args[0]
+                        prompt_length = len(model.tokenizer.encode(prompt))
+
+                        # Get max model length from vLLM engine
+                        max_model_len = model.model.llm_engine.model_config.max_model_len
+
+                        # Check if prompt itself exceeds model capacity
+                        if prompt_length > max_model_len:
+                            self.logger.warning(
+                                f"Prompt length ({prompt_length}) exceeds model max length ({max_model_len}). "
+                                f"Prompt will be truncated with no room for generation."
+                            )
+
+                        # Calculate max allowed generation tokens (16 token safety buffer)
+                        max_allowed = max_model_len - prompt_length - VLLM_SAFETY_BUFFER_TOKENS
+                        capped_max_new_tokens = min(max_new_tokens, max(1, max_allowed))
+
+                        if capped_max_new_tokens < max_new_tokens:
+                            self.logger.warning(
+                                f"max_new_tokens ({max_new_tokens}) capped to {capped_max_new_tokens} "
+                                f"(prompt: {prompt_length} tokens, model max: {max_model_len})"
+                            )
+
+                        instance.args[1]["max_gen_toks"] = capped_max_new_tokens
+                    except Exception as e:
+                        self.logger.warning(
+                            f"Failed to calculate max_gen_toks for vLLM, using original value: {e}"
+                        )
+                        instance.args[1]["max_gen_toks"] = max_new_tokens
                 else:  # Huggingface
                     instance.args[1]["max_new_tokens"] = max_new_tokens
         return instances