diff --git a/tests/models/multimodal/processing/test_gemma4.py b/tests/models/multimodal/processing/test_gemma4.py
index 808fab6a030f..bd1f2bb86779 100644
--- a/tests/models/multimodal/processing/test_gemma4.py
+++ b/tests/models/multimodal/processing/test_gemma4.py
@@ -12,6 +12,60 @@
 GEMMA4_MODEL_ID = "google/gemma-4-E2B-it"
 
 
+@pytest.mark.parametrize(
+    "image_width,image_height,max_soft_tokens",
+    [
+        # Production repro: a 3x900 image (extreme aspect ratio) made the
+        # prompt-side estimator return 289 while the HF Gemma 4 image
+        # processor's vision tower output capped at 280, producing the
+        # "Attempted to assign 280 multimodal tokens to 289 placeholders"
+        # mismatch that crashed EngineCore.
+        (900, 3, 280),
+        (3, 900, 280),
+        # Same pathology should hold for the video-frame budget (70 tokens).
+        (900, 3, 70),
+        # And for any other supported budget.
+        (4000, 2, 1120),
+    ],
+)
+@pytest.mark.parametrize("model_id", [GEMMA4_MODEL_ID])
+def test_compute_num_soft_tokens_does_not_exceed_max_soft_tokens(
+    model_id: str,
+    image_width: int,
+    image_height: int,
+    max_soft_tokens: int,
+):
+    """Regression for the Gemma 3/4 multimodal crash.
+
+    `_compute_num_soft_tokens` must never return a value larger than
+    `max_soft_tokens`. The HF Gemma 4 image processor clamps its vision
+    tower output to that value; if the prompt-side estimator returns more,
+    the prompt has more `image` placeholder tokens than the encoder will
+    fill, and `_merge_multimodal_embeddings` raises `ValueError` deep in
+    the model forward.
+    """
+    ctx = build_model_context(
+        model_id,
+        mm_processor_kwargs={"do_pan_and_scan": True},
+        limit_mm_per_prompt={"image": 1},
+    )
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
+
+    num_soft_tokens = processor.info._compute_num_soft_tokens(
+        image_width=image_width,
+        image_height=image_height,
+        max_soft_tokens=max_soft_tokens,
+    )
+
+    assert num_soft_tokens <= max_soft_tokens, (
+        f"_compute_num_soft_tokens returned {num_soft_tokens} for "
+        f"image_width={image_width}, image_height={image_height}, "
+        f"max_soft_tokens={max_soft_tokens} — exceeds the cap that the HF "
+        f"image processor enforces on its vision tower output. This is "
+        f"the placeholder/encoder count mismatch that crashes EngineCore."
+    )
+
+
 @pytest.mark.parametrize("model_id", [GEMMA4_MODEL_ID])
 def test_limit_mm_per_prompt(
     image_assets: ImageTestAssets,
diff --git a/vllm/model_executor/models/gemma4_mm.py b/vllm/model_executor/models/gemma4_mm.py
index cdc54609a652..9b2c54e27354 100644
--- a/vllm/model_executor/models/gemma4_mm.py
+++ b/vllm/model_executor/models/gemma4_mm.py
@@ -265,7 +265,14 @@ def _compute_num_soft_tokens(
         target_h = max(unit, int(math.floor(image_height * scale / unit)) * unit)
         target_w = max(unit, int(math.floor(image_width * scale / unit)) * unit)
         num_patches = (target_h // patch_size) * (target_w // patch_size)
-        return num_patches // (pooling_kernel_size**2)
+        # Clamp to ``max_soft_tokens``: extreme aspect ratios (e.g. 3x900)
+        # cause the floor() above to round one dim up to ``unit`` while the
+        # other scales freely, which over-shoots ``max_patches``. The HF
+        # Gemma 4 image processor caps its vision-tower output at
+        # ``max_soft_tokens``, so without this clamp the prompt-side
+        # placeholder count exceeds the encoder output and
+        # ``_merge_multimodal_embeddings`` crashes.
+        return min(num_patches // (pooling_kernel_size**2), max_soft_tokens)
 
     def get_image_repl(
         self,