diff --git a/benchmarks/benchmark_throughput.py b/benchmarks/benchmark_throughput.py
index 5647f913e1f9c..afa7dafc7d9ad 100644
--- a/benchmarks/benchmark_throughput.py
+++ b/benchmarks/benchmark_throughput.py
@@ -8,6 +8,7 @@
 
 import torch
 import uvloop
+from PIL import Image
 from tqdm import tqdm
 from transformers import (AutoModelForCausalLM, AutoTokenizer,
                           PreTrainedTokenizerBase)
@@ -38,12 +39,31 @@ class SampleRequest:
     multi_modal_data: Optional[MultiModalDataDict] = None
 
 
-def sample_requests(
-    dataset_path: str,
-    num_requests: int,
-    tokenizer: PreTrainedTokenizerBase,
-    fixed_output_len: Optional[int],
-) -> List[SampleRequest]:
+def _get_prompt_for_image_model(question: str, *, model: str) -> str:
+    """Prepend and append special tokens around the question to form a prompt.
+
+    Args:
+        question: The input question text to wrap with special tokens
+        model: The name of the model being used, to determine which special tokens to add
+
+    Returns:
+        The formatted prompt string with appropriate special tokens for the model
+
+    Raises:
+        ValueError: If an unsupported model name is provided
+    """
+    model = model.lower()
+    if "pixtral" in model:
+        return f"<s>[INST]{question}\n[IMG][/INST]"
+    raise ValueError(f"Unsupported model {model}")
+
+
+def sample_requests(tokenizer: PreTrainedTokenizerBase,
+                    args: argparse.Namespace) -> List[SampleRequest]:
+    dataset_path: str = args.dataset
+    num_requests: int = args.num_prompts
+    fixed_output_len: Optional[int] = args.output_len
+    model: str = args.model
     if fixed_output_len is not None and fixed_output_len < 4:
         raise ValueError("output_len too small")
 
@@ -52,16 +72,12 @@ def sample_requests(
         dataset = json.load(f)
     # Filter out the conversations with less than 2 turns.
     dataset = [data for data in dataset if len(data["conversations"]) >= 2]
-    # Only keep the first two turns of each conversation.
-    dataset = [(data["conversations"][0]["value"],
-                data["conversations"][1]["value"]) for data in dataset]
-
     # Shuffle the dataset.
     random.shuffle(dataset)
 
     # Filter out sequences that are too long or too short
     filtered_dataset: List[SampleRequest] = []
-    for i in range(len(dataset)):
+    for data in dataset:
         if len(filtered_dataset) == num_requests:
             break
 
@@ -85,13 +101,11 @@ def sample_requests(
             prompt = _get_prompt_for_image_model(question=prompt, model=model)
 
         # Tokenize the prompts and completions.
-        prompt = dataset[i][0]
         prompt_token_ids = tokenizer(prompt).input_ids
-        completion = dataset[i][1]
         completion_token_ids = tokenizer(completion).input_ids
         prompt_len = len(prompt_token_ids)
         output_len = len(completion_token_ids
-                         ) if fixed_output_len is None else fixed_output_len
+                        ) if fixed_output_len is None else fixed_output_len
         if prompt_len < 4 or output_len < 4:
             # Prune too short sequences.
             continue
@@ -119,7 +133,9 @@ def run_vllm(
     prompts: List[TextPrompt] = []
     sampling_params: List[SamplingParams] = []
     for request in requests:
-        prompts.append(TextPrompt(prompt=request.prompt))
+        prompts.append(
+            TextPrompt(prompt=request.prompt,
+                       multi_modal_data=request.multi_modal_data))
         sampling_params.append(
             SamplingParams(
                 n=n,