diff --git a/python/sglang/srt/managers/io_struct.py b/python/sglang/srt/managers/io_struct.py
index fa359c548..3ce179755 100644
--- a/python/sglang/srt/managers/io_struct.py
+++ b/python/sglang/srt/managers/io_struct.py
@@ -208,6 +208,9 @@ class GenerateReqInput:
     return_sampling_mask: Optional[Union[List[bool], bool]] = None
     # Whether to detokenize tokens in text in the returned logprobs.
     return_text_in_logprobs: bool = False
+    # Return prompt top logprobs as flat arrays plus shape metadata instead of
+    # the nested per-position [logprob, token_id, text] lists.
+    return_flat_raw_top_logprobs: bool = False
     # Whether to stream output.
     stream: bool = False
     # Whether to log metrics for this request (e.g. health_generate calls do not log metrics)
@@ -369,6 +372,15 @@ class GenerateReqInput:
             raise ValueError(
                 "Either text, input_ids or input_embeds should be provided."
             )
+        if (
+            self.return_flat_raw_top_logprobs
+            and self.multi_item_delimiter_indices is not None
+        ):
+            raise ValueError(
+                "return_flat_raw_top_logprobs does not support multi-item "
+                "scoring: delimiter-sparse top logprob rows have no contiguous "
+                "position mapping."
+            )
 
     def _determine_batch_size(self):
         """Determine if this is a single example or a batch and the batch size."""
@@ -724,6 +736,7 @@ class GenerateReqInput:
             token_ids_logprob=self.token_ids_logprob[i],
             return_sampling_mask=self.return_sampling_mask[i],
             return_text_in_logprobs=self.return_text_in_logprobs,
+            return_flat_raw_top_logprobs=self.return_flat_raw_top_logprobs,
             stream=self.stream,
             log_metrics=self.log_metrics,
             return_hidden_states=(
diff --git a/python/sglang/srt/managers/tokenizer_manager.py b/python/sglang/srt/managers/tokenizer_manager.py
index 7fe6e9491..ddec08f41 100644
--- a/python/sglang/srt/managers/tokenizer_manager.py
+++ b/python/sglang/srt/managers/tokenizer_manager.py
@@ -226,6 +226,11 @@ class ReqState:
     output_token_sampling_mask: List = dataclasses.field(default_factory=list)
     output_token_sampling_logprobs: List = dataclasses.field(default_factory=list)
 
+    # Cached flat-format prompt top logprob fields; rebuilt only when more
+    # prefill chunks arrive, so streaming decode chunks reuse the payload.
+    input_top_logprobs_flat_fields: Optional[Dict[str, Any]] = None
+    input_top_logprobs_flat_num_rows: int = -1
+
     # For detokenized logprobs
     input_token_logprobs: List[Any] = dataclasses.field(default_factory=list)
     output_token_logprobs: List[Any] = dataclasses.field(default_factory=list)
@@ -254,6 +259,43 @@ def _slice_streaming_output_meta_info(
         meta_info[key] = meta_info[key][last_output_offset:]
 
 
+def _build_flat_input_top_logprobs_fields(
+    input_top_logprobs_val: List[Optional[List[float]]],
+    input_top_logprobs_idx: List[Optional[List[int]]],
+    top_logprobs_num: int,
+) -> Dict[str, Any]:
+    """Build the flat raw prompt top logprob response fields.
+
+    `input_top_logprobs_shape` is the literal [rows, k] shape of the flat
+    arrays. The leading null positions (counted by
+    `input_top_logprobs_null_prefix`) precede the arrays, so covered position
+    i, entry j lives at flat[(i - null_prefix) * k + j] and the covered range
+    spans null_prefix + rows positions.
+    """
+    num_rows = len(input_top_logprobs_val)
+    null_prefix = 0
+    while null_prefix < num_rows and not input_top_logprobs_val[null_prefix]:
+        null_prefix += 1
+    val_rows = input_top_logprobs_val[null_prefix:]
+    idx_rows = input_top_logprobs_idx[null_prefix:]
+    k = len(val_rows[0]) if val_rows else top_logprobs_num
+    for offset, row in enumerate(val_rows):
+        if row is None or len(row) != k:
+            # Not representable by (shape, null_prefix); e.g. multi-item scoring.
+            raise ValueError(
+                "return_flat_raw_top_logprobs requires rectangular top logprob "
+                f"rows with nulls only in the leading prefix; row {null_prefix + offset} "
+                f"has {None if row is None else len(row)} entries (expected {k})."
+            )
+
+    fields: Dict[str, Any] = {}
+    fields["input_top_logprobs_val_flat"] = [v for row in val_rows for v in row]
+    fields["input_top_logprobs_idx_flat"] = [i for row in idx_rows for i in row]
+    fields["input_top_logprobs_shape"] = [len(val_rows), k]
+    fields["input_top_logprobs_null_prefix"] = null_prefix
+    return fields
+
+
 class InputFormat(Enum):
     """Input format types for tokenization handling."""
 
@@ -2228,14 +2270,53 @@ class TokenizerManager(TokenizerControlMixin, TokenizerManagerScoreMixin):
 
         # 2. Handle top logprobs
         if top_logprobs_num > 0:
-            if len(state.input_top_logprobs_val) > len(state.input_top_logprobs):
-                state.input_top_logprobs.extend(
-                    self.detokenize_top_logprobs_tokens(
-                        state.input_top_logprobs_val[len(state.input_top_logprobs) :],
-                        state.input_top_logprobs_idx[len(state.input_top_logprobs) :],
-                        return_text_in_logprobs,
+            # Guarded by the caller's return_logprob check, so obj is a
+            # GenerateReqInput here.
+            use_flat = state.obj.return_flat_raw_top_logprobs
+            if use_flat:
+                # Flat replaces nested for the input side only.
+                if state.input_top_logprobs_flat_num_rows != len(
+                    state.input_top_logprobs_val
+                ):
+                    try:
+                        state.input_top_logprobs_flat_fields = (
+                            _build_flat_input_top_logprobs_fields(
+                                state.input_top_logprobs_val,
+                                state.input_top_logprobs_idx,
+                                top_logprobs_num,
+                            )
+                        )
+                    except ValueError as e:
+                        # A raise here would disrupt unrelated requests in the
+                        # shared batch-output loop; degrade to nested instead.
+                        state.input_top_logprobs_flat_fields = None
+                        logger.error(
+                            "Falling back to nested input top logprobs for "
+                            "rid=%s: %s",
+                            meta_info.get("id"),
+                            e,
+                        )
+                    state.input_top_logprobs_flat_num_rows = len(
+                        state.input_top_logprobs_val
                     )
-                )
+                if state.input_top_logprobs_flat_fields is not None:
+                    meta_info.update(state.input_top_logprobs_flat_fields)
+                else:
+                    use_flat = False
+            if not use_flat:
+                if len(state.input_top_logprobs_val) > len(state.input_top_logprobs):
+                    state.input_top_logprobs.extend(
+                        self.detokenize_top_logprobs_tokens(
+                            state.input_top_logprobs_val[
+                                len(state.input_top_logprobs) :
+                            ],
+                            state.input_top_logprobs_idx[
+                                len(state.input_top_logprobs) :
+                            ],
+                            return_text_in_logprobs,
+                        )
+                    )
+                meta_info["input_top_logprobs"] = state.input_top_logprobs
             if len(state.output_top_logprobs_val) > len(state.output_top_logprobs):
                 state.output_top_logprobs.extend(
                     self.detokenize_top_logprobs_tokens(
@@ -2244,8 +2325,6 @@ class TokenizerManager(TokenizerControlMixin, TokenizerManagerScoreMixin):
                         return_text_in_logprobs,
                     )
                 )
-
-            meta_info["input_top_logprobs"] = state.input_top_logprobs
             meta_info["output_top_logprobs"] = state.output_top_logprobs
 
         # 3. Handle token_ids_logprob
diff --git a/test/registered/unit/managers/test_flat_raw_top_logprobs.py b/test/registered/unit/managers/test_flat_raw_top_logprobs.py
new file mode 100644
index 000000000..77be10f00
--- /dev/null
+++ b/test/registered/unit/managers/test_flat_raw_top_logprobs.py
@@ -0,0 +1,306 @@
+"""Unit tests for the flat raw prompt top logprob response format
+(`return_flat_raw_top_logprobs`).
+"""
+
+import asyncio
+import json
+import os
+import time
+import unittest
+
+import numpy as np
+
+from sglang.test.ci.ci_register import register_cpu_ci
+from sglang.test.test_utils import CustomTestCase, maybe_stub_sgl_kernel
+
+maybe_stub_sgl_kernel()
+
+from sglang.srt.managers.io_struct import GenerateReqInput
+from sglang.srt.managers.tokenizer_manager import (
+    ReqState,
+    TokenizerManager,
+    _build_flat_input_top_logprobs_fields,
+)
+from sglang.srt.observability.req_time_stats import APIServerReqTimeStats
+
+register_cpu_ci(est_time=10, suite="base-a-test-cpu")
+
+# Synthetic per-position top-k rows (k=2). The leading None mirrors the first
+# prompt position, which has no top logprobs.
+_VAL_ROWS = [None, [-0.1, -2.5], [-0.3, -1.5], [-0.05, -4.0]]
+_IDX_ROWS = [None, [11, 22], [33, 44], [55, 66]]
+
+
+class _TokenizerManagerStub:
+    """Borrow the real logprob meta_info methods without a full manager."""
+
+    add_logprob_to_meta_info = TokenizerManager.add_logprob_to_meta_info
+    detokenize_logprob_tokens = TokenizerManager.detokenize_logprob_tokens
+    detokenize_top_logprobs_tokens = TokenizerManager.detokenize_top_logprobs_tokens
+
+
+def _make_state(**obj_kwargs) -> ReqState:
+    obj = GenerateReqInput(text="hello", **obj_kwargs)
+    obj.normalize_batch_and_arguments()
+    return ReqState(
+        out_list=[],
+        finished=False,
+        event=asyncio.Event(),
+        obj=obj,
+        time_stats=APIServerReqTimeStats(),
+    )
+
+
+def _add_logprob_meta_info(state: ReqState, top_logprobs_num: int = 2) -> dict:
+    meta_info = {}
+    _TokenizerManagerStub().add_logprob_to_meta_info(
+        meta_info,
+        state,
+        top_logprobs_num=top_logprobs_num,
+        token_ids_logprob=None,
+        return_text_in_logprobs=False,
+    )
+    return meta_info
+
+
+class TestFlatRawTopLogprobsValidation(CustomTestCase):
+    def test_flag_defaults_off_and_valid(self):
+        for kwargs in (
+            {},
+            {"return_flat_raw_top_logprobs": True},
+        ):
+            req = GenerateReqInput(text="hello", **kwargs)
+            req.normalize_batch_and_arguments()
+
+    def test_flat_rejects_multi_item_scoring(self):
+        req = GenerateReqInput(
+            text="a<sep>b",
+            return_flat_raw_top_logprobs=True,
+            multi_item_delimiter_indices=[1],
+        )
+        with self.assertRaisesRegex(ValueError, "multi-item"):
+            req.normalize_batch_and_arguments()
+
+    def test_flag_propagates_to_batch_items(self):
+        req = GenerateReqInput(
+            text=["a", "b"],
+            return_flat_raw_top_logprobs=True,
+        )
+        req.normalize_batch_and_arguments()
+        for i in range(2):
+            self.assertTrue(req[i].return_flat_raw_top_logprobs)
+
+
+class TestFlatAssembly(CustomTestCase):
+    def test_flat_matches_nested_rows(self):
+        fields = _build_flat_input_top_logprobs_fields(
+            _VAL_ROWS, _IDX_ROWS, top_logprobs_num=2
+        )
+        self.assertEqual(fields["input_top_logprobs_shape"], [3, 2])
+        self.assertEqual(fields["input_top_logprobs_null_prefix"], 1)
+        self.assertEqual(
+            fields["input_top_logprobs_val_flat"],
+            [v for row in _VAL_ROWS[1:] for v in row],
+        )
+        self.assertEqual(
+            fields["input_top_logprobs_idx_flat"],
+            [i for row in _IDX_ROWS[1:] for i in row],
+        )
+        # Reconstruct the nested rows: covered position i, entry j lives at
+        # flat[(i - null_prefix) * k + j].
+        rows, k = fields["input_top_logprobs_shape"]
+        null_prefix = fields["input_top_logprobs_null_prefix"]
+        flat_val = fields["input_top_logprobs_val_flat"]
+        self.assertEqual(len(flat_val), rows * k)
+        for i in range(null_prefix, null_prefix + rows):
+            start = (i - null_prefix) * k
+            self.assertEqual(flat_val[start : start + k], _VAL_ROWS[i])
+
+    def test_all_null_rows(self):
+        fields = _build_flat_input_top_logprobs_fields(
+            [None], [None], top_logprobs_num=2
+        )
+        self.assertEqual(fields["input_top_logprobs_shape"], [0, 2])
+        self.assertEqual(fields["input_top_logprobs_null_prefix"], 1)
+        self.assertEqual(fields["input_top_logprobs_val_flat"], [])
+        self.assertEqual(fields["input_top_logprobs_idx_flat"], [])
+
+    def test_rejects_null_row_after_prefix(self):
+        with self.assertRaisesRegex(ValueError, "leading prefix"):
+            _build_flat_input_top_logprobs_fields(
+                [None, [-0.1, -2.5], None, [-0.3, -1.5]],
+                [None, [11, 22], None, [33, 44]],
+                top_logprobs_num=2,
+            )
+
+    def test_rejects_ragged_rows(self):
+        with self.assertRaisesRegex(ValueError, "rectangular"):
+            _build_flat_input_top_logprobs_fields(
+                [None, [-0.1, -2.5], [-0.3]],
+                [None, [11, 22], [33]],
+                top_logprobs_num=2,
+            )
+
+
+class TestAddLogprobToMetaInfo(CustomTestCase):
+    def _extend_input_top(self, state: ReqState, val_rows, idx_rows):
+        state.input_top_logprobs_val.extend(val_rows)
+        state.input_top_logprobs_idx.extend(idx_rows)
+
+    def test_nested_path_unchanged_when_flags_unset(self):
+        state = _make_state(return_logprob=True, top_logprobs_num=2)
+        self._extend_input_top(state, _VAL_ROWS, _IDX_ROWS)
+        meta_info = _add_logprob_meta_info(state)
+        self.assertEqual(
+            meta_info["input_top_logprobs"],
+            [
+                None if row is None else [(v, i, None) for v, i in zip(row, idx_row)]
+                for row, idx_row in zip(_VAL_ROWS, _IDX_ROWS)
+            ],
+        )
+        self.assertIn("output_top_logprobs", meta_info)
+        for key in meta_info:
+            self.assertNotIn("_flat", key)
+        self.assertNotIn("input_top_logprobs_shape", meta_info)
+        self.assertNotIn("input_top_logprobs_null_prefix", meta_info)
+
+    def test_flat_path_replaces_nested_input(self):
+        state = _make_state(
+            return_logprob=True,
+            top_logprobs_num=2,
+            return_flat_raw_top_logprobs=True,
+        )
+        self._extend_input_top(state, _VAL_ROWS, _IDX_ROWS)
+        meta_info = _add_logprob_meta_info(state)
+        self.assertNotIn("input_top_logprobs", meta_info)
+        self.assertIn("output_top_logprobs", meta_info)
+        self.assertEqual(meta_info["input_top_logprobs_shape"], [3, 2])
+        self.assertEqual(meta_info["input_top_logprobs_null_prefix"], 1)
+        self.assertEqual(
+            meta_info["input_top_logprobs_val_flat"],
+            [v for row in _VAL_ROWS[1:] for v in row],
+        )
+
+    def test_unrepresentable_rows_fall_back_to_nested(self):
+        # The shared batch-output loop must not raise on unrepresentable
+        # rows; the request degrades to the nested format.
+        state = _make_state(
+            return_logprob=True,
+            top_logprobs_num=2,
+            return_flat_raw_top_logprobs=True,
+        )
+        val_rows = [_VAL_ROWS[1], None, _VAL_ROWS[2]]
+        idx_rows = [_IDX_ROWS[1], None, _IDX_ROWS[2]]
+        self._extend_input_top(state, val_rows, idx_rows)
+        meta_info = _add_logprob_meta_info(state)
+        self.assertIn("input_top_logprobs", meta_info)
+        self.assertNotIn("input_top_logprobs_val_flat", meta_info)
+        self.assertNotIn("input_top_logprobs_shape", meta_info)
+        self.assertEqual(len(meta_info["input_top_logprobs"]), 3)
+
+    def test_chunked_accumulation_matches_one_shot(self):
+        # One shot.
+        one_shot = _make_state(
+            return_logprob=True,
+            top_logprobs_num=2,
+            return_flat_raw_top_logprobs=True,
+        )
+        self._extend_input_top(one_shot, _VAL_ROWS, _IDX_ROWS)
+        expected = _add_logprob_meta_info(one_shot)
+
+        # Rows arriving across two chunks, with meta_info assembled after each
+        # (as happens for streaming requests).
+        chunked = _make_state(
+            return_logprob=True,
+            top_logprobs_num=2,
+            return_flat_raw_top_logprobs=True,
+        )
+        self._extend_input_top(chunked, _VAL_ROWS[:2], _IDX_ROWS[:2])
+        _add_logprob_meta_info(chunked)
+        self._extend_input_top(chunked, _VAL_ROWS[2:], _IDX_ROWS[2:])
+        got = _add_logprob_meta_info(chunked)
+
+        flat_keys = [
+            "input_top_logprobs_val_flat",
+            "input_top_logprobs_idx_flat",
+            "input_top_logprobs_shape",
+            "input_top_logprobs_null_prefix",
+        ]
+        for key in flat_keys:
+            self.assertEqual(got[key], expected[key])
+
+        # No new rows -> the encoded payload is reused, not rebuilt.
+        again = _add_logprob_meta_info(chunked)
+        self.assertIs(
+            again["input_top_logprobs_val_flat"],
+            got["input_top_logprobs_val_flat"],
+        )
+
+
+@unittest.skipUnless(
+    os.environ.get("SGLANG_BENCH_FLAT_RAW_TOP_LOGPROBS"),
+    "Serialization microbenchmark; set SGLANG_BENCH_FLAT_RAW_TOP_LOGPROBS=1 to run.",
+)
+class BenchFlatRawTopLogprobsSerialization(CustomTestCase):
+    """Round-trip cost of the formats: server assembly + json.dumps, then
+    client json.loads + reconstruction into [rows, k] arrays."""
+
+    def test_bench(self):
+        num_positions, k = 32768, 2
+        rng = np.random.default_rng(0)
+        vals = rng.standard_normal((num_positions, k)).astype(np.float32)
+        idxs = rng.integers(0, 150000, size=(num_positions, k), dtype=np.int32)
+        val_rows = [None] + vals[1:].tolist()
+        idx_rows = [None] + idxs[1:].tolist()
+
+        def best_of(fn, iters=5):
+            result = fn()
+            elapsed = min(
+                (lambda s=time.perf_counter(): (fn(), time.perf_counter() - s)[1])()
+                for _ in range(iters)
+            )
+            return elapsed * 1e3, result
+
+        def bench(name, build, decode):
+            encode_ms, payload = best_of(lambda: json.dumps(build()))
+            decode_ms, arrays = best_of(lambda: decode(payload))
+            self.assertEqual(arrays[0].shape, (num_positions - 1, k))
+            print(
+                f"{name}: encode {encode_ms:.1f} ms, decode {decode_ms:.1f} ms, "
+                f"{len(payload)} bytes"
+            )
+
+        def decode_nested(payload):
+            rows = [r for r in json.loads(payload) if r is not None]
+            return (
+                np.array([[e[0] for e in r] for r in rows], dtype=np.float32),
+                np.array([[e[1] for e in r] for r in rows], dtype=np.int32),
+            )
+
+        def decode_flat(payload):
+            d = json.loads(payload)
+            shape = d["input_top_logprobs_shape"]
+            return (
+                np.asarray(d["input_top_logprobs_val_flat"], np.float32).reshape(shape),
+                np.asarray(d["input_top_logprobs_idx_flat"], np.int32).reshape(shape),
+            )
+
+        bench(
+            "nested triples",
+            lambda: [
+                (None if row is None else [(v, i, None) for v, i in zip(row, idx_row)])
+                for row, idx_row in zip(val_rows, idx_rows)
+            ],
+            decode_nested,
+        )
+        bench(
+            "flat lists",
+            lambda: _build_flat_input_top_logprobs_fields(
+                val_rows, idx_rows, top_logprobs_num=k
+            ),
+            decode_flat,
+        )
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)