diff --git a/miles/utils/data.py b/miles/utils/data.py
index 3fda3c46c0..3dfea7090a 100644
--- a/miles/utils/data.py
+++ b/miles/utils/data.py
@@ -108,11 +108,12 @@ def filter_long_prompt(origin_samples: list[Sample], tokenizer, processor, max_l
                 if len(input_ids) <= max_length:
                     filtered_samples.append(sample)
         if multimodal:
-            from miles.utils.processing_utils import process_vision_info
+            from miles.utils.processing_utils import call_processor
 
             for sample in multimodal:
-                multimodal_inputs = process_vision_info(sample.prompt, processor)
-                processor_output = processor(text=sample.prompt, **multimodal_inputs)
+                # sample.prompt is the rendered template string here, so reuse the
+                # multimodal_inputs computed from the original message list in __init__.
+                processor_output = call_processor(processor, sample.prompt, sample.multimodal_inputs)
                 input_ids = processor_output["input_ids"][0]
                 if len(input_ids) <= max_length:
                     filtered_samples.append(sample)
diff --git a/tests/fast/utils/test_data.py b/tests/fast/utils/test_data.py
new file mode 100644
index 0000000000..068a9cf38e
--- /dev/null
+++ b/tests/fast/utils/test_data.py
@@ -0,0 +1,68 @@
+from tests.ci.ci_register import register_cpu_ci
+
+register_cpu_ci(est_time=60, suite="stage-b-cpu", labels=[])
+
+import pytest
+from PIL import Image
+
+from miles.utils.data import filter_long_prompt
+from miles.utils.processing_utils import load_processor, load_tokenizer, process_vision_info
+from miles.utils.types import Sample
+
+VLM_MODEL_NAME = "Qwen/Qwen2-VL-2B-Instruct"
+
+
+@pytest.fixture(scope="module")
+def tokenizer():
+    return load_tokenizer(VLM_MODEL_NAME, trust_remote_code=True)
+
+
+@pytest.fixture(scope="module")
+def processor():
+    proc = load_processor(VLM_MODEL_NAME, trust_remote_code=True)
+    assert proc is not None
+    return proc
+
+
+def _make_vlm_sample(tokenizer, processor, with_image: bool) -> Sample:
+    # Mirrors Dataset.__init__: prompt is the rendered template string,
+    # multimodal_inputs comes from the original message list.
+    content = [{"type": "text", "text": "Describe this image in detail."}]
+    if with_image:
+        content.insert(0, {"type": "image", "image": Image.new("RGB", (64, 64), color="red")})
+    messages = [{"role": "user", "content": content}]
+    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    multimodal_inputs = process_vision_info(messages, processor) if with_image else None
+    return Sample(prompt=prompt, multimodal_inputs=multimodal_inputs)
+
+
+class TestFilterLongPromptMultimodal:
+    def test_multimodal_sample_does_not_crash_and_is_kept(self, tokenizer, processor):
+        # Regression: string prompt + images used to crash in qwen_vl_utils
+        # (TypeError: string indices must be integers).
+        sample = _make_vlm_sample(tokenizer, processor, with_image=True)
+        kept = filter_long_prompt([sample], tokenizer, processor, max_length=10_000)
+        assert kept == [sample]
+
+    def test_image_token_expansion_counts_toward_limit(self, tokenizer, processor):
+        sample = _make_vlm_sample(tokenizer, processor, with_image=True)
+        text_len = len(tokenizer(sample.prompt, add_special_tokens=False)["input_ids"])
+        full_output = processor(text=sample.prompt, **sample.multimodal_inputs)
+        full_len = len(full_output["input_ids"][0])
+        assert full_len > text_len
+
+        # A limit between the two lengths must filter the sample; if images
+        # were dropped (the old bug), the text-only count would keep it.
+        boundary = (text_len + full_len) // 2
+        assert filter_long_prompt([sample], tokenizer, processor, max_length=boundary) == []
+        assert filter_long_prompt([sample], tokenizer, processor, max_length=full_len) == [sample]
+
+    def test_mixed_batch_filters_each_partition(self, tokenizer, processor):
+        text_sample = _make_vlm_sample(tokenizer, processor, with_image=False)
+        mm_sample = _make_vlm_sample(tokenizer, processor, with_image=True)
+        long_text_sample = Sample(prompt="word " * 500)
+
+        kept = filter_long_prompt([text_sample, mm_sample, long_text_sample], tokenizer, processor, max_length=400)
+        assert text_sample in kept
+        assert mm_sample in kept
+        assert long_text_sample not in kept