diff --git a/vllm/model_executor/models/exaone4_5_mtp.py b/vllm/model_executor/models/exaone4_5_mtp.py
index 5252aa7a72..99bf724bda 100644
--- a/vllm/model_executor/models/exaone4_5_mtp.py
+++ b/vllm/model_executor/models/exaone4_5_mtp.py
@@ -23,8 +23,14 @@ from vllm.model_executor.models.exaone_moe_mtp import (
     ExaoneMoeMultiTokenPredictor,
 )
 
+from .interfaces import (
+    MultiModalEmbeddings,
+    SupportsMultiModal,
+    _require_is_multimodal,
+)
 from .utils import (
     AutoWeightsLoader,
+    _merge_multimodal_embeddings,
     maybe_prefix,
 )
 
@@ -85,9 +91,12 @@ class Exaone4_5MultiTokenPredictor(ExaoneMoeMultiTokenPredictor):
             config.hidden_size, eps=config.rms_norm_eps
         )
 
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
 
 @support_torch_compile
-class Exaone4_5_MTP(ExaoneMoeMTP):
+class Exaone4_5_MTP(ExaoneMoeMTP, SupportsMultiModal):
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         config = vllm_config.model_config.hf_config
         self.vllm_config = vllm_config
@@ -111,6 +120,32 @@ class Exaone4_5_MTP(ExaoneMoeMTP):
             self.unpadded_vocab_size, config.vocab_size
         )
 
+    def embed_input_ids(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: MultiModalEmbeddings | None = None,
+        *,
+        is_multimodal: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self._embed_text_input_ids(
+            input_ids,
+            self.model.embed_input_ids,
+            is_multimodal=is_multimodal,
+        )
+
+        if multimodal_embeddings is None or len(multimodal_embeddings) == 0:
+            return inputs_embeds
+
+        is_multimodal = _require_is_multimodal(is_multimodal)
+
+        inputs_embeds = _merge_multimodal_embeddings(
+            inputs_embeds=inputs_embeds,
+            multimodal_embeddings=multimodal_embeddings,
+            is_multimodal=is_multimodal,
+        )
+
+        return inputs_embeds
+
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         shared_weight_names = ["embed_tokens", "lm_head"]