diff --git a/src/lighteval/metrics/utils/llm_as_judge.py b/src/lighteval/metrics/utils/llm_as_judge.py
index 0f9b3315c..7a0364975 100644
--- a/src/lighteval/metrics/utils/llm_as_judge.py
+++ b/src/lighteval/metrics/utils/llm_as_judge.py
@@ -296,9 +296,7 @@ def __call_transformers(self, prompt):
 
     def __call_vllm(self, prompt):
         tokenized = [self.tokenizer.apply_chat_template(p) for p in prompt]
-        # Convert token IDs to TokensPrompt format for vLLM v0.15+
-        prompts = [{"prompt_token_ids": token_ids} for token_ids in tokenized]
-        output = self.pipe.generate(prompts=prompts, sampling_params=self.sampling_params, use_tqdm=True)
+        output = self.pipe.generate(prompts=tokenized, sampling_params=self.sampling_params, use_tqdm=True)
         outputs = [output.outputs[0].text for output in output]
         return outputs
 
diff --git a/src/lighteval/models/vllm/vllm_model.py b/src/lighteval/models/vllm/vllm_model.py
index 3100c56b7..a403c1f75 100644
--- a/src/lighteval/models/vllm/vllm_model.py
+++ b/src/lighteval/models/vllm/vllm_model.py
@@ -439,9 +439,7 @@ def _generate(
             @ray.remote(num_gpus=self.tensor_parallel_size)
             def run_inference_one_model(model_args: dict, sampling_params: SamplingParams, requests):
                 llm = LLM(**model_args)
-                # Convert token IDs to TokensPrompt format for vLLM v0.15+
-                prompts = [{"prompt_token_ids": req} for req in requests]
-                return llm.generate(prompts=prompts, sampling_params=sampling_params)
+                return llm.generate(prompts=requests, sampling_params=sampling_params)
 
             # dispatch requests to all self.data_parallel_size workers, in interleaved fashion
             # interleaved important to balance context lengths across workers
@@ -458,12 +456,8 @@ def run_inference_one_model(model_args: dict, sampling_params: SamplingParams, r
                 if x is not None
             ]
         else:
-            from vllm.inputs import TokenInputs
-
-            # Convert token IDs to TokensPrompt format for vLLM v0.15+
-            prompts = [TokenInputs(prompt_token_ids=token_ids) for token_ids in inputs]
             outputs = self.model.generate(
-                prompts=prompts,
+                prompts=inputs,
                 sampling_params=sampling_params,
                 use_tqdm=True,
             )