diff --git a/tests/kernels/attention/test_minimax_m3_fp8_triton_indexer.py b/tests/kernels/attention/test_minimax_m3_fp8_triton_indexer.py
index 247faba17f..dcf7a6bc03 100644
--- a/tests/kernels/attention/test_minimax_m3_fp8_triton_indexer.py
+++ b/tests/kernels/attention/test_minimax_m3_fp8_triton_indexer.py
@@ -107,3 +107,37 @@ def test_fp8_index_query_and_cache_prefill_topk_matches_bf16() -> None:
         return {int(b) for b in idx[0, 0].tolist() if b >= 0}
 
     _check_selection(select(keys), select(keys.to(torch.float8_e4m3fn)))
+
+
+@pytest.mark.skipif(not FP8_CUDA_SUPPORTED, reason="CUDA FP8 support required")
+@torch.inference_mode()
+def test_bf16_query_against_fp8_cache_decode_topk_matches_bf16() -> None:
+    """A mixed pair must upcast rather than emit a mixed-dtype fp8 dot.
+
+    Production allocates index_q with the index-K cache dtype, so the scorers
+    normally see a matched pair and keep the native FP8 MMA. This pins the
+    other branch: a bf16 query against an e4m3 cache still compiles and still
+    ranks the planted blocks.
+    """
+    dev = "cuda"
+    q, keys = _make_inputs(dev)
+    block_table = torch.arange(NUM_PAGES, dtype=torch.int32, device=dev).unsqueeze(0)
+    seq_lens = torch.tensor([SEQ_LEN], dtype=torch.int32, device=dev)
+
+    def select(cache: torch.Tensor) -> set[int]:
+        idx = minimax_m3_index_decode(
+            q,  # bf16 query, deliberately not cast to the cache dtype
+            cache.view(NUM_PAGES, PAGE, HEAD_DIM).contiguous(),
+            block_table,
+            seq_lens,
+            SEQ_LEN,
+            TOPK,
+            0,
+            0,
+            HEADS,
+            1,
+            1,
+        )
+        return {int(b) for b in idx[0, 0].tolist() if b >= 0}
+
+    _check_selection(select(keys), select(keys.to(torch.float8_e4m3fn)))
diff --git a/vllm/models/minimax_m3/common/ops/index_topk.py b/vllm/models/minimax_m3/common/ops/index_topk.py
index 94b74363e7..025c3dd428 100644
--- a/vllm/models/minimax_m3/common/ops/index_topk.py
+++ b/vllm/models/minimax_m3/common/ops/index_topk.py
@@ -124,9 +124,11 @@ def _index_block_score_kernel(
         order=(1, 0),
     )
     q = tl.load(q_ptrs, boundary_check=(0,), padding_option="zero")
-    # FP8 has no mixed-dtype dot with bf16/fp32. Leave bf16 and fp32 loads
-    # in their stored dtype so fp32 pipeline tests keep full precision.
-    if q.dtype.is_fp8():
+    # A matched fp8 pair (e.g. e4m3 x e4m3) lowers to a native FP8 MMA, so
+    # upcast only when the operands differ: there is no mixed-dtype fp8 MMA,
+    # including across fp8 flavours (fp8e4nv vs fp8e4b8). bf16 and fp32 loads
+    # always stay in their stored dtype.
+    if q.dtype.is_fp8() and q.dtype != ik_cache_ptr.dtype.element_ty:
         q = q.to(tl.bfloat16)
     q_start = prefix_len + pid_q * BLOCK_SIZE_Q
 
@@ -162,7 +164,7 @@ def _index_block_score_kernel(
             + off_k[None, :] * stride_ik_pos
             + off_d[:, None] * stride_ik_d,
         )
-        if k.dtype.is_fp8():
+        if k.dtype.is_fp8() and k.dtype != q.dtype:
             k = k.to(tl.bfloat16)
         qk = tl.dot(q, k, out_dtype=tl.float32)
         # apply causal mask as needed
@@ -378,7 +380,7 @@ def _decode_index_score_kernel(
         mask=q_mask[None, :],
         other=0.0,
     )  # [D,HQ]
-    if q.dtype.is_fp8():
+    if q.dtype.is_fp8() and q.dtype != ik_cache_ptr.dtype.element_ty:
         q = q.to(tl.bfloat16)
     for blk in tl.range(chunk_start_block, chunk_end_block):
         page = tl.load(bt_row + blk).to(tl.int64)
@@ -393,10 +395,10 @@ def _decode_index_score_kernel(
             + off_k[:, None] * stride_ik_pos
             + off_d * stride_ik_d,
         )  # [N,D]
-        # Upcast only FP8 cache loads. BF16 operands keep FP8 dot
-        # instructions out of the fallback path; FP32 accumulation
-        # preserves score accuracy. BF16/FP32 loads stay as stored.
-        if k.dtype.is_fp8():
+        # Matched fp8 operands keep the native FP8 MMA; any mismatch upcasts,
+        # since no mixed-dtype fp8 MMA exists. FP32 accumulation preserves
+        # score accuracy either way. BF16/FP32 loads stay as stored.
+        if k.dtype.is_fp8() and k.dtype != q.dtype:
             k = k.to(tl.bfloat16)
         kq = tl.dot(k, q, out_dtype=tl.float32)  # [N,HQ]
         kq = tl.where(pos_mask & q_mask[None, :], kq, float("-inf"))