diff --git a/tests/models/multimodal/processing/test_gemma3.py b/tests/models/multimodal/processing/test_gemma3.py
new file mode 100644
index 000000000000..32a459ee8cdf
--- /dev/null
+++ b/tests/models/multimodal/processing/test_gemma3.py
@@ -0,0 +1,42 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+
+from vllm.multimodal import MULTIMODAL_REGISTRY
+
+from ....conftest import ImageTestAssets
+from ...utils import build_model_context
+
+
+@pytest.mark.parametrize("model_id", ["google/gemma-3-4b-it"])
+def test_get_image_size_with_most_features(
+    image_assets: ImageTestAssets, model_id: str
+):
+    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)
+
+    hf_processor_mm_kwargs: dict[str, object] = {}
+    hf_processor = processor.info.get_hf_processor(**hf_processor_mm_kwargs)
+
+    max_image_size = processor.info.get_image_size_with_most_features()
+    max_tokens = processor.info.get_num_image_tokens(
+        image_width=max_image_size.width,
+        image_height=max_image_size.height,
+        processor=hf_processor,
+    )
+
+    prompt = "<start_of_image>"
+    image_seq_length = hf_processor.image_seq_length
+
+    for asset in image_assets:
+        mm_data = {"image": [asset.pil_image]}
+        processed_inputs = processor.apply(prompt, mm_data, hf_processor_mm_kwargs)
+        mm_kwargs_data = processed_inputs["mm_kwargs"].get_data()
+        num_patches_tensor = mm_kwargs_data["num_patches"]
+        tokens = int(num_patches_tensor.item()) * image_seq_length
+        assert tokens <= max_tokens
diff --git a/tests/models/multimodal/processing/test_qwen2_vl.py b/tests/models/multimodal/processing/test_qwen2_vl.py
index 9f4cdb6789b2..20beaa6011b8 100644
--- a/tests/models/multimodal/processing/test_qwen2_vl.py
+++ b/tests/models/multimodal/processing/test_qwen2_vl.py
@@ -53,3 +53,38 @@ def test_processor_override(
     assert img_tok_count == expected_toks_per_img * num_imgs
     assert pixel_shape[0] == expected_pixels_shape[0] * num_imgs
     assert pixel_shape[1] == expected_pixels_shape[1]
+
+
+@pytest.mark.parametrize("model_id", ["Qwen/Qwen2-VL-2B-Instruct"])
+@pytest.mark.parametrize("max_pixels", [1280 * 28 * 28, 1283 * 28 * 28])
+def test_get_image_size_with_most_features(
+    image_assets: ImageTestAssets,
+    model_id: str,
+    max_pixels: int,
+):
+    ctx = build_model_context(
+        model_id,
+        mm_processor_kwargs={"max_pixels": max_pixels},
+        limit_mm_per_prompt={"image": 1},
+    )
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
+
+    hf_processor_mm_kwargs: dict[str, object] = {}
+    hf_processor = processor.info.get_hf_processor(**hf_processor_mm_kwargs)
+    merge_size = processor.info.get_hf_config().vision_config.spatial_merge_size
+
+    max_image_size = processor.info.get_image_size_with_most_features()
+    max_tokens = processor.info.get_num_image_tokens(
+        image_width=max_image_size.width,
+        image_height=max_image_size.height,
+        image_processor=hf_processor.image_processor,
+    )
+
+    prompt = "<|vision_start|><|image_pad|><|vision_end|>"
+    for asset in image_assets:
+        mm_data = {"image": [asset.pil_image]}
+        processed_inputs = processor.apply(prompt, mm_data, hf_processor_mm_kwargs)
+        grid_thw = processed_inputs["mm_kwargs"].get_data()["image_grid_thw"].tolist()
+        t, h, w = grid_thw[0]
+        tokens = (t * h * w) // (merge_size**2)
+        assert tokens < max_tokens
diff --git a/vllm/model_executor/models/gemma3_mm.py b/vllm/model_executor/models/gemma3_mm.py
index 43c69e5e1399..a42a93560926 100644
--- a/vllm/model_executor/models/gemma3_mm.py
+++ b/vllm/model_executor/models/gemma3_mm.py
@@ -237,8 +237,9 @@ def get_image_size_with_most_features(self) -> ImageSize:
         )
         max_num_crops = images_kwargs["pan_and_scan_max_num_crops"]
 
-        # Result in the max possible feature size (h:w = max_num_crops:1)
-        return ImageSize(height=50 * max_num_crops, width=50)
+        vision_config = self.get_hf_config().vision_config
+        native_size = vision_config.image_size
+        return ImageSize(height=native_size * max_num_crops, width=native_size)
 
 
 class Gemma3DummyInputsBuilder(BaseDummyInputsBuilder[Gemma3ProcessingInfo]):
diff --git a/vllm/model_executor/models/qwen2_vl.py b/vllm/model_executor/models/qwen2_vl.py
index b74876849841..28d3823fcae4 100644
--- a/vllm/model_executor/models/qwen2_vl.py
+++ b/vllm/model_executor/models/qwen2_vl.py
@@ -25,6 +25,7 @@
 # limitations under the License.
 """Inference-only Qwen2-VL model compatible with HuggingFace weights."""
 
+import math
 from collections.abc import Callable, Iterable, Mapping, Sequence
 from functools import partial
 from typing import Annotated, Any, Literal, TypeAlias
@@ -959,13 +960,42 @@ def get_num_video_tokens(
         return num_video_tokens
 
     def get_image_size_with_most_features(self) -> ImageSize:
-        max_image_size, _ = self._get_vision_info(
-            image_width=9999999,
-            image_height=9999999,
-            num_frames=1,
-            image_processor=None,
-        )
-        return max_image_size
+        # NOTE: Simply processing a huge size with _get_vision_info might not give a
+        # size that maximizes the number of featrues, i.e., the number of (merged)
+        # patches. This is because the number of patches limits the allowed aspect
+        # ratios. For example, suppose the maximum number of patches is 1280. A square
+        # image cannot be broken down into 1280 patches, so feeding a giant square image
+        # into _get_vision_info will not yield a size that maximizes the number of
+        # patches. Therefore, we directly factorize the maximum number of patches into
+        # height and width. The tricky part is to avoid extreme aspect ratios (>200 for
+        # qwen2-vl). If we can't find a suitable aspect ratio, we decrease the number of
+        # patches and retry. This is safe because the processor does not accept extreme
+        # aspect ratios, so there is no valid post-resize image with the number of
+        # patches that yields extreme aspect ratios.
+
+        hf_config = self.get_hf_config()
+        vision_config = hf_config.vision_config
+        patch_size = vision_config.patch_size
+        merge_size = vision_config.spatial_merge_size
+        image_processor = self.get_image_processor()
+        max_pixels = image_processor.max_pixels or image_processor.size["longest_edge"]
+        unit = patch_size * merge_size
+        max_seq_len = max_pixels // (unit * unit)
+
+        def closest_factor_pair(n: int) -> tuple[int, int]:
+            # left <= right
+            for d in range(math.isqrt(n), 0, -1):
+                if n % d == 0:
+                    return d, n // d
+            return 1, n
+
+        height_factor, width_factor = 1, max_seq_len
+        for seq_len in range(max_seq_len, 0, -1):
+            height_factor, width_factor = closest_factor_pair(seq_len)
+            if width_factor / height_factor <= 200:
+                break
+
+        return ImageSize(width=unit * width_factor, height=unit * height_factor)
 
     def get_max_image_tokens(self) -> int:
         target_width, target_height = self.get_image_size_with_most_features()