diff --git a/tests/v1/e2e/test_spec_decode.py b/tests/v1/e2e/test_spec_decode.py
index 9ed9cd7950a9..681933899d30 100644
--- a/tests/v1/e2e/test_spec_decode.py
+++ b/tests/v1/e2e/test_spec_decode.py
@@ -124,9 +124,9 @@ def test_ngram_correctness(
 
 
 @pytest.mark.parametrize(
-    ["model_setup", "mm_enabled"],
+    ["model_setup", "mm_enabled", "chunked_prefill_enabled"],
     [
-        (("eagle3", "Qwen/Qwen3-8B", "AngelSlim/Qwen3-8B_eagle3", 1), False),
+        (("eagle3", "Qwen/Qwen3-8B", "AngelSlim/Qwen3-8B_eagle3", 1), False, False),
         pytest.param(
             (
                 "eagle3",
@@ -135,6 +135,7 @@ def test_ngram_correctness(
                 1,
             ),
             False,
+            False,
             marks=pytest.mark.skip(
                 reason="Skipping due to its head_dim not being a a multiple of 32"
             ),
@@ -147,6 +148,7 @@ def test_ngram_correctness(
                 1,
             ),
             False,
+            True,
         ),
         (
             (
@@ -156,6 +158,7 @@ def test_ngram_correctness(
                 1,
             ),
             False,
+            False,
         ),
         pytest.param(
             (
@@ -165,6 +168,7 @@ def test_ngram_correctness(
                 4,
             ),
             False,
+            False,
             marks=large_gpu_mark(min_gb=80),
         ),  # works on 4x H100
         pytest.param(
@@ -175,6 +179,7 @@ def test_ngram_correctness(
                 4,
             ),
             True,
+            True,
             marks=large_gpu_mark(min_gb=80),
         ),  # works on 4x H100
         (
@@ -185,6 +190,7 @@ def test_ngram_correctness(
                 1,
             ),
             False,
+            False,
         ),
     ],
     ids=[
@@ -203,6 +209,7 @@ def test_eagle_correctness(
     sampling_config: SamplingParams,
     model_setup: tuple[str, str, str, int],
     mm_enabled: bool,
+    chunked_prefill_enabled: bool,
     attn_backend: str,
 ):
     if attn_backend == "TREE_ATTN":
@@ -239,9 +246,13 @@ def test_eagle_correctness(
             m.setenv("VLLM_ROCM_USE_AITER", "1")
 
         method, model_name, spec_model_name, tp_size = model_setup
+        max_model_len = 2048
+        max_num_batched_tokens = max_model_len
+        if chunked_prefill_enabled:
+            max_num_batched_tokens = 128
 
         ref_llm = LLM(
-            model=model_name, max_model_len=2048, tensor_parallel_size=tp_size
+            model=model_name, max_model_len=max_model_len, tensor_parallel_size=tp_size
         )
         ref_outputs = ref_llm.chat(test_prompts, sampling_config)
         del ref_llm
@@ -256,9 +267,11 @@ def test_eagle_correctness(
                 "method": method,
                 "model": spec_model_name,
                 "num_speculative_tokens": 3,
-                "max_model_len": 2048,
+                "max_model_len": max_num_batched_tokens,
             },
-            max_model_len=2048,
+            max_model_len=max_model_len,
+            max_num_batched_tokens=max_num_batched_tokens,
+            enable_chunked_prefill=chunked_prefill_enabled,
         )
         spec_outputs = spec_llm.chat(test_prompts, sampling_config)
         matches = 0