diff --git a/eval/task.py b/eval/task.py
index 70962115..10f949f6 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 max_gen_toks calculation
+SAFETY_BUFFER_TOKENS = 16
+
 
 class BaseBenchmark(ABC):
     """Abstract base class for implementing LLM evaluation benchmarks."""
@@ -46,6 +49,42 @@ def _normalize_model_args(self, model: LM, instances: List[Instance]) -> List[In
                     _ = instance.args[1].pop("seed") if "seed" in instance.args[1] else None
             if "max_new_tokens" in instance.args[1]:
                 max_new_tokens = instance.args[1].pop("max_new_tokens")
+                
+                max_model_len = None
+                if isinstance(model, lm_eval_models.vllm_causallms.VLLM):
+                    max_model_len = model.model.llm_engine.model_config.max_model_len
+                elif isinstance(model, lm_eval_models.huggingface.HFLM):
+                    max_model_len = model.model.config.max_position_embeddings
+
+                if max_model_len is not None:
+                    try:
+                        # Get prompt from instance.args[0] (the templated string)
+                        prompt = instance.args[0]
+                        prompt_length = len(model.tokenizer.encode(prompt))
+
+                        # 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 - 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})"
+                            )
+
+                        max_new_tokens = capped_max_new_tokens
+                    except Exception as e:
+                        self.logger.warning(
+                            f"Failed to calculate max_new_tokens, using original value: {e}"
+                        )
+                        
                 if isinstance(model, lm_eval_models.openai_completions.OpenAIChatCompletion) or isinstance(
                     model, lm_eval_models.openai_completions.OpenAICompletionsAPI
                 ):
@@ -53,9 +92,10 @@ 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
+                    instance.args[1]["max_gen_toks"] = int(max_new_tokens)
                 else:  # Huggingface
-                    instance.args[1]["max_new_tokens"] = max_new_tokens
+                    instance.args[1]["max_new_tokens"] = int(max_new_tokens)
+
         return instances
 
     def _prepare_messages(