diff --git a/tests/multimodal/test_base.py b/tests/multimodal/test_base.py
index f19a0f33fe067..e9562d2048f06 100644
--- a/tests/multimodal/test_base.py
+++ b/tests/multimodal/test_base.py
@@ -81,3 +81,15 @@ def test_multimodal_input_batch_multiple_batchable_lists():
         result,
         {"image": torch.stack([torch.stack([a, b]),
                                torch.stack([c, d])])})
+
+
+def test_multimodal_input_batch_mixed_stacking_depths():
+    a = torch.rand([1, 2, 3])
+    b = torch.rand([1, 3, 3])
+    c = torch.rand([1, 4, 3])
+
+    result = MultiModalInputs.batch([{"image": [a, b]}, {"image": [c]}])
+    assert_multimodal_inputs_equal(result, {"image": [[a, b], c.unsqueeze(0)]})
+
+    result = MultiModalInputs.batch([{"image": [a]}, {"image": [b, c]}])
+    assert_multimodal_inputs_equal(result, {"image": [a.unsqueeze(0), [b, c]]})
diff --git a/vllm/model_executor/models/utils.py b/vllm/model_executor/models/utils.py
index 6e7ee511bf27f..16565e1467e8f 100644
--- a/vllm/model_executor/models/utils.py
+++ b/vllm/model_executor/models/utils.py
@@ -1,7 +1,6 @@
 from typing import (Dict, Iterable, List, Literal, Optional, Protocol, Tuple,
                     Union, overload)
 
-import numpy as np
 import torch
 import torch.nn as nn
 from torch.func import functional_call
@@ -96,12 +95,13 @@ def flatten_bn(
 
 def _flatten_embeddings(embeddings: NestedTensors) -> torch.Tensor:
     """
-    Recursively concatenates NestedTensors along any heterogeneously sized
-    dimensions.
+    Recursively flattens and concatenates NestedTensors on all but the last
+    dimension.
     """
 
     if isinstance(embeddings, torch.Tensor):
-        return embeddings
+        # Flatten all but the last dimension.
+        return embeddings.flatten(0, -2)
 
     return torch.cat(tuple(_flatten_embeddings(t) for t in embeddings))
 
@@ -136,15 +136,13 @@ def merge_multimodal_embeddings(input_ids: torch.Tensor,
     assert isinstance(num_expected_tokens, int)
 
     flattened = _flatten_embeddings(multimodal_embeddings)
-    *dims, embed_dim = flattened.shape
-    num_multimodal_embeddings = np.prod(dims)
-    if num_multimodal_embeddings != num_expected_tokens:
+    if flattened.shape[0] != num_expected_tokens:
         expr = _embedding_count_expression(multimodal_embeddings)
         raise ValueError(
-            f"Attempted to assign {expr} = {num_multimodal_embeddings} "
+            f"Attempted to assign {expr} = {flattened.shape[0]} "
             f"multimodal tokens to {num_expected_tokens} placeholders")
 
-    inputs_embeds[mask] = flattened.view(num_expected_tokens, embed_dim)
+    inputs_embeds[mask] = flattened
     return inputs_embeds
 
 
diff --git a/vllm/multimodal/base.py b/vllm/multimodal/base.py
index c02e61596927a..17ef9938d0572 100644
--- a/vllm/multimodal/base.py
+++ b/vllm/multimodal/base.py
@@ -54,8 +54,8 @@ def _try_stack(nested_tensors: NestedTensors) -> NestedTensors:
             return nested_tensors
 
         stacked = [MultiModalInputs._try_stack(t) for t in nested_tensors]
-        if is_list_of(stacked, list):
-            # Do not stack nested lists
+        if not is_list_of(stacked, torch.Tensor, check="all"):
+            # Only tensors (not lists) can be stacked.
             return stacked
 
         tensors_ = cast(List[torch.Tensor], stacked)