diff --git a/tests/entrypoints/scale_out/derender/test_derender_stream.py b/tests/entrypoints/scale_out/derender/test_derender_stream.py
new file mode 100644
index 00000000000..014a8d7bc6b
--- /dev/null
+++ b/tests/entrypoints/scale_out/derender/test_derender_stream.py
@@ -0,0 +1,765 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""Unit tests for streaming derender.
+
+Tests are split into two layers:
+
+1. Unit tests (no server): covers ``_detokenize_delta`` correctness
+   (chunked == one-shot) and ``derender_completion_stream`` /
+   ``derender_chat_stream`` logic via a real tokenizer on a tiny model.
+
+2. Integration tests (require a running render server): covers the full
+   HTTP round-trip through the streaming endpoint.  Marked with
+   ``@pytest.mark.asyncio`` and gated by the ``server`` / ``client``
+   fixtures from the sibling ``test_derender.py``.
+"""
+
+import pytest
+import pytest_asyncio
+
+from vllm.entrypoints.scale_out.token_in_token_out.protocol import (
+    DerenderStreamState,
+    GenerateResponseStreamChoice,
+    GenerateStreamResponse,
+)
+
+MODEL_NAME = "hmellor/tiny-random-LlamaForCausalLM"
+
+
+# ---------------------------------------------------------------------------
+# Helpers shared across tests
+# ---------------------------------------------------------------------------
+
+
+def _make_stream_chunk(
+    token_ids: list[int],
+    index: int = 0,
+    finish_reason: str | None = None,
+    request_id: str = "test-req",
+    usage: dict | None = None,
+) -> GenerateStreamResponse:
+    """Build a GenerateStreamResponse SSE chunk."""
+    from vllm.entrypoints.openai.engine.protocol import UsageInfo
+
+    return GenerateStreamResponse(
+        request_id=request_id,
+        choices=[
+            GenerateResponseStreamChoice(
+                index=index,
+                token_ids=token_ids,
+                finish_reason=finish_reason,
+            )
+        ],
+        usage=UsageInfo(**usage) if usage else None,
+    )
+
+
+def _make_usage_chunk(
+    completion_tokens: int,
+    prompt_tokens: int = 0,
+    request_id: str = "test-req",
+) -> GenerateStreamResponse:
+    """Build a usage only final SSE chunk (empty choices)."""
+    from vllm.entrypoints.openai.engine.protocol import UsageInfo
+
+    return GenerateStreamResponse(
+        request_id=request_id,
+        choices=[],
+        usage=UsageInfo(
+            prompt_tokens=prompt_tokens,
+            completion_tokens=completion_tokens,
+            total_tokens=prompt_tokens + completion_tokens,
+        ),
+    )
+
+
+# ---------------------------------------------------------------------------
+# Unit tests — no running server
+# ---------------------------------------------------------------------------
+
+
+@pytest.fixture(scope="module")
+def tokenizer():
+    """Load the tiny tokenizer used across unit tests."""
+    from vllm.tokenizers import get_tokenizer
+
+    return get_tokenizer(MODEL_NAME)
+
+
+@pytest.fixture(scope="module")
+def derenderer(tokenizer):
+    """Construct a minimal OnlineDerenderer backed by a stub renderer."""
+    from unittest.mock import MagicMock
+
+    from vllm.renderers.online_derenderer import OnlineDerenderer
+
+    renderer = MagicMock()
+    renderer.get_tokenizer.return_value = tokenizer
+
+    model_config = MagicMock()
+    model_config.hf_config.model_type = "llama"
+    model_config.model = MODEL_NAME
+
+    return OnlineDerenderer(
+        model_config=model_config,
+        renderer=renderer,
+        request_logger=None,
+        chat_template=None,
+        chat_template_content_format="string",
+        trust_request_chat_template=False,
+        enable_auto_tools=False,
+        tool_parser=None,
+        reasoning_parser=None,
+    )
+
+
+class TestDetokenizeDelta:
+    """_detokenize_delta: chunked decode must equal one shot decode."""
+
+    def _one_shot(self, tokenizer, token_ids: list[int]) -> str:
+        return tokenizer.decode(token_ids, skip_special_tokens=True)
+
+    def _chunked(self, derenderer, tokenizer, chunks: list[list[int]]) -> str:
+        state = DerenderStreamState()
+        parts: list[str] = []
+        for delta in chunks:
+            text, state = derenderer._detokenize_delta(
+                tokenizer, delta, state, skip_special_tokens=True
+            )
+            parts.append(text)
+        return "".join(parts)
+
+    def test_single_chunk(self, derenderer, tokenizer):
+        """All tokens in one chunk == one shot decode."""
+        token_ids = tokenizer.encode("Hello world")[:8]
+        assert self._chunked(derenderer, tokenizer, [token_ids]) == self._one_shot(
+            tokenizer, token_ids
+        )
+
+    def test_two_equal_chunks(self, derenderer, tokenizer):
+        """Split in half and reassemble == one shot."""
+        token_ids = tokenizer.encode("Hello world from streaming derender")[:12]
+        mid = len(token_ids) // 2
+        chunks = [token_ids[:mid], token_ids[mid:]]
+        assert self._chunked(derenderer, tokenizer, chunks) == self._one_shot(
+            tokenizer, token_ids
+        )
+
+    def test_single_token_per_chunk(self, derenderer, tokenizer):
+        """One token per chunk (most granular streaming) == one shot."""
+        token_ids = tokenizer.encode("incremental detokenization test")[:10]
+        chunks = [[t] for t in token_ids]
+        assert self._chunked(derenderer, tokenizer, chunks) == self._one_shot(
+            tokenizer, token_ids
+        )
+
+    def test_empty_delta_passthrough(self, derenderer, tokenizer):
+        """An empty delta (usage only chunk) emits empty string and preserves state."""
+        token_ids = tokenizer.encode("Hello")[:4]
+        _, state = derenderer._detokenize_delta(
+            tokenizer, token_ids, DerenderStreamState(), skip_special_tokens=True
+        )
+        text, new_state = derenderer._detokenize_delta(
+            tokenizer, [], state, skip_special_tokens=True
+        )
+        assert text == ""
+        assert new_state.prev_tokens == state.prev_tokens
+        assert new_state.prefix_offset == state.prefix_offset
+        assert new_state.read_offset == state.read_offset
+
+    def test_multibyte_char_split_across_chunks(self, derenderer, tokenizer):
+        """A CJK/emoji char straddling chunk boundaries == one shot.
+
+        Regression test for held back trailing incomplete UTF-8 byte
+        sequences being dropped when the rebuild window marks them as
+        already read (see #46159).
+        """
+        token_ids = tokenizer.encode("Hello ✅ world 日本語")[:16]
+        chunks = [[t] for t in token_ids]
+        assert self._chunked(derenderer, tokenizer, chunks) == self._one_shot(
+            tokenizer, token_ids
+        )
+
+    def test_state_carries_across_calls(self, derenderer, tokenizer):
+        """Decode state threads across calls. Text still matches one shot."""
+        t1 = tokenizer.encode("Hello")[:2]
+        t2 = tokenizer.encode(" world")[:2]
+        state = DerenderStreamState()
+        text1, state = derenderer._detokenize_delta(tokenizer, t1, state)
+        text2, state = derenderer._detokenize_delta(tokenizer, t2, state)
+        assert text1 + text2 == self._one_shot(tokenizer, t1 + t2)
+        # Offsets are rebased to the carried tail each chunk
+        assert state.prefix_offset == 0
+
+    def test_state_window_stays_bounded(self, derenderer, tokenizer):
+        """prev_tokens must not grow with the number of chunks (bounded transport).
+
+        Guards that the carried decode window is a small constant
+        tail, so cumulative ``stream_state`` transport is O(n) and not O(n^2).
+        """
+        token_ids = tokenizer.encode(
+            "a reasonably long ascii stream of tokens used to exercise the "
+            "window bound across many single token chunks so the carried "
+            "state cannot grow linearly with the generation length"
+        )
+        assert len(token_ids) > 32
+        state = DerenderStreamState()
+        max_window = 0
+        for tok in token_ids:
+            _, state = derenderer._detokenize_delta(tokenizer, [tok], state)
+            max_window = max(max_window, len(state.prev_tokens))
+        # Bounded by a small constant independent of len(token_ids)
+        assert max_window <= 32
+
+    def test_n_independent_streams_same_result(self, derenderer, tokenizer):
+        """N parallel streams with the same token sequence give the same text."""
+        token_ids = tokenizer.encode("parallel streams")[:8]
+        mid = len(token_ids) // 2
+
+        results = []
+        for _ in range(3):
+            state = DerenderStreamState()
+            text, state = derenderer._detokenize_delta(
+                tokenizer, token_ids[:mid], state
+            )
+            text2, _ = derenderer._detokenize_delta(tokenizer, token_ids[mid:], state)
+            results.append(text + text2)
+
+        assert len(set(results)) == 1, "All independent streams must produce same text"
+        assert results[0] == self._one_shot(tokenizer, token_ids)
+
+
+class TestDerenderCompletionStream:
+    """derender_completion_stream: streaming output parity with one shot."""
+
+    @pytest.mark.asyncio
+    async def test_chunked_equals_oneshot(self, derenderer, tokenizer):
+        """Sum of streaming text chunks == one shot tokenizer.decode."""
+        token_ids = tokenizer.encode("streaming completion test")[:10]
+        mid = len(token_ids) // 2
+
+        state = DerenderStreamState()
+        chunk1, state = await derenderer.derender_completion_stream(
+            model=MODEL_NAME,
+            generate_chunk=_make_stream_chunk(token_ids[:mid]),
+            state=state,
+        )
+        chunk2, _ = await derenderer.derender_completion_stream(
+            model=MODEL_NAME,
+            generate_chunk=_make_stream_chunk(token_ids[mid:], finish_reason="stop"),
+            state=state,
+        )
+
+        streamed_text = chunk1.choices[0].text + chunk2.choices[0].text
+        one_shot = tokenizer.decode(token_ids, skip_special_tokens=True)
+        assert streamed_text == one_shot
+
+    @pytest.mark.asyncio
+    async def test_usage_chunk_passthrough(self, derenderer, tokenizer):
+        """Usage only final chunk (empty choices) is passed through correctly."""
+        usage_chunk = _make_usage_chunk(completion_tokens=10, prompt_tokens=5)
+        chunk, state = await derenderer.derender_completion_stream(
+            model=MODEL_NAME,
+            generate_chunk=usage_chunk,
+        )
+        assert chunk.choices == []
+        assert chunk.usage is not None
+        assert chunk.usage.completion_tokens == 10
+        assert chunk.usage.prompt_tokens == 5
+
+    @pytest.mark.asyncio
+    async def test_prompt_tokens_in_usage(self, derenderer, tokenizer):
+        """prompt_tokens is correctly forwarded into usage on a usage chunk."""
+        token_ids = tokenizer.encode("hello")[:3]
+        usage_chunk = _make_usage_chunk(
+            completion_tokens=len(token_ids), prompt_tokens=7
+        )
+        chunk, _ = await derenderer.derender_completion_stream(
+            model=MODEL_NAME,
+            generate_chunk=usage_chunk,
+            prompt_tokens=7,
+        )
+        assert chunk.usage is not None
+        assert chunk.usage.prompt_tokens == 7
+
+    @pytest.mark.asyncio
+    async def test_none_state_initialises_correctly(self, derenderer, tokenizer):
+        """Passing state=None (first call) initialises an empty DerenderStreamState."""
+        token_ids = tokenizer.encode("hello")[:4]
+        chunk, state = await derenderer.derender_completion_stream(
+            model=MODEL_NAME,
+            generate_chunk=_make_stream_chunk(token_ids),
+            state=None,
+        )
+        assert isinstance(state, DerenderStreamState)
+        assert chunk.choices[0].text == tokenizer.decode(
+            token_ids, skip_special_tokens=True
+        )
+
+    @pytest.mark.asyncio
+    async def test_skip_special_tokens_threaded(self, derenderer, tokenizer):
+        """completion_request.skip_special_tokens is honored (not hardcoded True)."""
+        from vllm.entrypoints.openai.completion.protocol import CompletionRequest
+
+        eos = tokenizer.eos_token_id
+        if eos is None:
+            pytest.skip("tokenizer has no eos token to exercise special stripping")
+        token_ids = tokenizer.encode("hi")[:2] + [eos]
+
+        async def _text(skip: bool) -> str:
+            req = CompletionRequest(
+                model=MODEL_NAME, prompt="x", skip_special_tokens=skip
+            )
+            chunk, _ = await derenderer.derender_completion_stream(
+                model=MODEL_NAME,
+                generate_chunk=_make_stream_chunk(token_ids),
+                completion_request=req,
+            )
+            return chunk.choices[0].text
+
+        # skip=False must retain the special token; skip=True must strip it.
+        assert await _text(False) != await _text(True)
+
+    @pytest.mark.asyncio
+    async def test_finish_reason_forwarded(self, derenderer, tokenizer):
+        """finish_reason from the generate chunk reaches the derendered choice."""
+        token_ids = tokenizer.encode("done")[:2]
+        chunk, _ = await derenderer.derender_completion_stream(
+            model=MODEL_NAME,
+            generate_chunk=_make_stream_chunk(token_ids, finish_reason="length"),
+        )
+        assert chunk.choices[0].finish_reason == "length"
+
+
+class TestDerenderChatStream:
+    """derender_chat_stream: plain detok branch (no parser)."""
+
+    @pytest.mark.asyncio
+    async def test_role_on_first_chunk_only(self, derenderer, tokenizer):
+        """role='assistant' appears in the first chunk, not subsequent ones."""
+        token_ids = tokenizer.encode("hello world")[:6]
+        mid = len(token_ids) // 2
+
+        state = DerenderStreamState()
+        chunk1, state = await derenderer.derender_chat_stream(
+            model=MODEL_NAME,
+            generate_chunk=_make_stream_chunk(token_ids[:mid]),
+            state=state,
+        )
+        chunk2, _ = await derenderer.derender_chat_stream(
+            model=MODEL_NAME,
+            generate_chunk=_make_stream_chunk(token_ids[mid:], finish_reason="stop"),
+            state=state,
+        )
+
+        assert chunk1.choices[0].delta.role == "assistant"
+        assert chunk2.choices[0].delta.role is None
+
+    @pytest.mark.asyncio
+    async def test_chunked_equals_oneshot(self, derenderer, tokenizer):
+        """Sum of streaming content deltas == one shot decode."""
+        token_ids = tokenizer.encode("streaming chat derender text")[:10]
+        mid = len(token_ids) // 2
+
+        state = DerenderStreamState()
+        chunk1, state = await derenderer.derender_chat_stream(
+            model=MODEL_NAME,
+            generate_chunk=_make_stream_chunk(token_ids[:mid]),
+            state=state,
+        )
+        chunk2, _ = await derenderer.derender_chat_stream(
+            model=MODEL_NAME,
+            generate_chunk=_make_stream_chunk(token_ids[mid:]),
+            state=state,
+        )
+
+        streamed = (chunk1.choices[0].delta.content or "") + (
+            chunk2.choices[0].delta.content or ""
+        )
+        one_shot = tokenizer.decode(token_ids, skip_special_tokens=True)
+        assert streamed == one_shot
+
+    @pytest.mark.asyncio
+    @pytest.mark.parametrize("with_chat_request", [True, False])
+    async def test_parser_raises_not_implemented(self, tokenizer, with_chat_request):
+        """Stream chat with a parser active must fail closed (NotImplementedError).
+
+        Checks that a parser configured model must 501 even when ``chat_request`` is
+        omitted, otherwise reasoning/tool markup would leak into ``delta.content`
+          via the plain detok fallback.
+        """
+        from unittest.mock import MagicMock
+
+        from vllm.renderers.online_derenderer import OnlineDerenderer
+
+        renderer = MagicMock()
+        renderer.get_tokenizer.return_value = tokenizer
+
+        model_config = MagicMock()
+        model_config.hf_config.model_type = "llama"
+        model_config.model = MODEL_NAME
+
+        # Construct a derenderer WITH a tool/reasoning parser active.
+        # ParserManager.get_parser returns None when no parser is named, so
+        # we inject a mock parser class directly.
+        dr = OnlineDerenderer(
+            model_config=model_config,
+            renderer=renderer,
+            request_logger=None,
+            chat_template=None,
+            chat_template_content_format="string",
+        )
+        dr.parser = MagicMock()  # simulate a parser being active
+
+        chat_request = MagicMock() if with_chat_request else None
+        token_ids = tokenizer.encode("hello")[:3]
+
+        with pytest.raises(NotImplementedError):
+            await dr.derender_chat_stream(
+                model=MODEL_NAME,
+                generate_chunk=_make_stream_chunk(token_ids),
+                state=None,
+                chat_request=chat_request,
+            )
+
+
+class TestDerenderStreamStateValidation:
+    """DerenderStreamState rejects malformed caller supplied offsets/lengths."""
+
+    def test_negative_prefix_offset_rejected(self):
+        from pydantic import ValidationError
+
+        with pytest.raises(ValidationError):
+            DerenderStreamState(prefix_offset=-1)
+
+    def test_negative_read_offset_rejected(self):
+        from pydantic import ValidationError
+
+        with pytest.raises(ValidationError):
+            DerenderStreamState(read_offset=-1)
+
+    def test_prev_tokens_over_cap_rejected(self):
+        from pydantic import ValidationError
+
+        with pytest.raises(ValidationError):
+            DerenderStreamState(prev_tokens=["a"] * 1025)
+
+    def test_prev_tokens_at_cap_accepted(self):
+        state = DerenderStreamState(prev_tokens=["a"] * 1024)
+        assert len(state.prev_tokens) == 1024
+
+
+class TestServingDerenderStreamErrorHandling:
+    """Malformed stream_state must surface as 400 and not an unhandled 500."""
+
+    def _make_serving(self, side_effect: Exception):
+        from unittest.mock import AsyncMock, MagicMock
+
+        from vllm.entrypoints.scale_out.derender.serving import ServingDerender
+
+        models = MagicMock()
+        models.is_base_model.return_value = True
+        models.model_config = MagicMock()
+
+        online_derenderer = MagicMock()
+        online_derenderer.derender_completion_stream = AsyncMock(
+            side_effect=side_effect
+        )
+        online_derenderer.derender_chat_stream = AsyncMock(side_effect=side_effect)
+
+        return ServingDerender(models=models, online_derenderer=online_derenderer)
+
+    @pytest.mark.asyncio
+    @pytest.mark.parametrize("exc", [KeyError("bad byte"), IndexError("oob")])
+    async def test_completion_stream_bad_state_returns_400(self, exc):
+        from vllm.entrypoints.openai.engine.protocol import ErrorResponse
+        from vllm.entrypoints.scale_out.token_in_token_out.protocol import (
+            DerenderCompletionStreamRequest,
+        )
+
+        serving = self._make_serving(exc)
+        request = DerenderCompletionStreamRequest(
+            stream=True,
+            model=MODEL_NAME,
+            generate_chunk=_make_stream_chunk([1, 2]),
+            stream_state=DerenderStreamState(),
+        )
+        result = await serving.derender_completion_stream_response(request)
+        assert isinstance(result, ErrorResponse)
+        assert result.error.code == 400
+
+    @pytest.mark.asyncio
+    @pytest.mark.parametrize("exc", [KeyError("bad byte"), IndexError("oob")])
+    async def test_chat_stream_bad_state_returns_400(self, exc):
+        from vllm.entrypoints.openai.engine.protocol import ErrorResponse
+        from vllm.entrypoints.scale_out.token_in_token_out.protocol import (
+            DerenderChatStreamRequest,
+        )
+
+        serving = self._make_serving(exc)
+        request = DerenderChatStreamRequest(
+            stream=True,
+            model=MODEL_NAME,
+            generate_chunk=_make_stream_chunk([1, 2]),
+            stream_state=DerenderStreamState(),
+        )
+        result = await serving.derender_chat_stream_response(request)
+        assert isinstance(result, ErrorResponse)
+        assert result.error.code == 400
+
+
+# ---------------------------------------------------------------------------
+# Integration tests — require a live render server
+# ---------------------------------------------------------------------------
+
+
+@pytest.fixture(scope="module")
+def server():
+    from tests.utils import RemoteLaunchRenderServer
+
+    with RemoteLaunchRenderServer(MODEL_NAME, []) as remote_server:
+        yield remote_server
+
+
+@pytest_asyncio.fixture
+async def client(server):
+    import httpx
+
+    async with httpx.AsyncClient(
+        base_url=server.url_for(""), timeout=30.0
+    ) as http_client:
+        yield http_client
+
+
+async def _render_chat(client) -> dict:
+    """Render a minimal chat request and return the GenerateRequest dict."""
+
+    resp = await client.post(
+        "/v1/chat/completions/render",
+        json={
+            "model": MODEL_NAME,
+            "messages": [{"role": "user", "content": "Hello"}],
+        },
+    )
+    assert resp.status_code == 200
+    return resp.json()
+
+
+@pytest.mark.asyncio
+async def test_streaming_completion_derender_roundtrip(client):
+    """Streaming completions derender: chunked text == non streaming text."""
+    gen_req = await _render_chat(client)
+    token_ids: list[int] = gen_req["token_ids"][:8]
+    mid = len(token_ids) // 2
+    chunk1_ids, chunk2_ids = token_ids[:mid], token_ids[mid:]
+
+    # Non streaming baseline.
+    non_stream_resp = await client.post(
+        "/v1/completions/derender",
+        json={
+            "model": MODEL_NAME,
+            "generate_responses": [
+                {
+                    "request_id": "test-ns",
+                    "choices": [
+                        {
+                            "index": 0,
+                            "token_ids": token_ids,
+                            "finish_reason": "stop",
+                        }
+                    ],
+                }
+            ],
+        },
+    )
+    assert non_stream_resp.status_code == 200
+    expected_text = non_stream_resp.json()["choices"][0]["text"]
+
+    # Streaming call 1.
+    r1 = await client.post(
+        "/v1/completions/derender",
+        json={
+            "stream": True,
+            "model": MODEL_NAME,
+            "generate_chunk": {
+                "request_id": "test-s",
+                "choices": [
+                    {"index": 0, "token_ids": chunk1_ids, "finish_reason": None}
+                ],
+            },
+            "stream_state": None,
+        },
+    )
+    assert r1.status_code == 200
+    d1 = r1.json()
+    text1 = d1["chunk"]["choices"][0]["text"]
+    state1 = d1["stream_state"]
+
+    # Streaming call 2 (final chunk).
+    r2 = await client.post(
+        "/v1/completions/derender",
+        json={
+            "stream": True,
+            "model": MODEL_NAME,
+            "generate_chunk": {
+                "request_id": "test-s",
+                "choices": [
+                    {"index": 0, "token_ids": chunk2_ids, "finish_reason": "stop"}
+                ],
+            },
+            "stream_state": state1,
+        },
+    )
+    assert r2.status_code == 200
+    text2 = r2.json()["chunk"]["choices"][0]["text"]
+
+    assert text1 + text2 == expected_text
+
+
+@pytest.mark.asyncio
+async def test_streaming_chat_derender_roundtrip(client):
+    """Streaming chat derender (plain detok): chunked text == non streaming text."""
+    gen_req = await _render_chat(client)
+    token_ids: list[int] = gen_req["token_ids"][:8]
+    mid = len(token_ids) // 2
+    chunk1_ids, chunk2_ids = token_ids[:mid], token_ids[mid:]
+
+    # Non streaming baseline.
+    ns = await client.post(
+        "/v1/chat/completions/derender",
+        json={
+            "model": MODEL_NAME,
+            "generate_response": {
+                "request_id": "test-ns",
+                "choices": [
+                    {
+                        "index": 0,
+                        "token_ids": token_ids,
+                        "finish_reason": "stop",
+                    }
+                ],
+            },
+        },
+    )
+    assert ns.status_code == 200
+    expected_content = ns.json()["choices"][0]["message"]["content"]
+
+    # Streaming call 1.
+    r1 = await client.post(
+        "/v1/chat/completions/derender",
+        json={
+            "stream": True,
+            "model": MODEL_NAME,
+            "generate_chunk": {
+                "request_id": "test-s",
+                "choices": [
+                    {"index": 0, "token_ids": chunk1_ids, "finish_reason": None}
+                ],
+            },
+            "stream_state": None,
+        },
+    )
+    assert r1.status_code == 200
+    d1 = r1.json()
+    text1 = d1["chunk"]["choices"][0]["delta"].get("content") or ""
+    state1 = d1["stream_state"]
+    # role=assistant on the first chunk
+    assert d1["chunk"]["choices"][0]["delta"].get("role") == "assistant"
+
+    # Streaming call 2.
+    r2 = await client.post(
+        "/v1/chat/completions/derender",
+        json={
+            "stream": True,
+            "model": MODEL_NAME,
+            "generate_chunk": {
+                "request_id": "test-s",
+                "choices": [
+                    {"index": 0, "token_ids": chunk2_ids, "finish_reason": "stop"}
+                ],
+            },
+            "stream_state": state1,
+        },
+    )
+    assert r2.status_code == 200
+    d2 = r2.json()
+    text2 = d2["chunk"]["choices"][0]["delta"].get("content") or ""
+    # role must NOT be repeated on subsequent chunks
+    assert d2["chunk"]["choices"][0]["delta"].get("role") is None
+
+    assert text1 + text2 == expected_content
+
+
+@pytest.mark.asyncio
+async def test_streaming_derender_invalid_body_returns_400(client):
+    """Missing required field in streaming request returns 400."""
+    r = await client.post(
+        "/v1/completions/derender",
+        json={
+            "stream": True,
+            # missing required 'model' and 'generate_chunk'
+        },
+    )
+    assert r.status_code == 400
+
+
+@pytest.mark.asyncio
+async def test_streaming_derender_non_object_body_returns_400(client):
+    """A non object JSON body (e.g. a list) returns 400, not a 500."""
+    r = await client.post(
+        "/v1/completions/derender",
+        json=[1, 2, 3],
+    )
+    assert r.status_code == 400
+
+
+@pytest.mark.asyncio
+async def test_streaming_usage_chunk(client):
+    """Usage only final chunk is forwarded with correct token counts."""
+    gen_req = await _render_chat(client)
+    token_ids: list[int] = gen_req["token_ids"][:6]
+    state: dict = {}
+
+    # Send content chunk first.
+    r1 = await client.post(
+        "/v1/completions/derender",
+        json={
+            "stream": True,
+            "model": MODEL_NAME,
+            "generate_chunk": {
+                "request_id": "usage-test",
+                "choices": [
+                    {"index": 0, "token_ids": token_ids, "finish_reason": "stop"}
+                ],
+            },
+            "stream_state": None,
+        },
+    )
+    assert r1.status_code == 200
+    state = r1.json()["stream_state"]
+
+    # Send usage only final chunk.
+    r2 = await client.post(
+        "/v1/completions/derender",
+        json={
+            "stream": True,
+            "model": MODEL_NAME,
+            "generate_chunk": {
+                "request_id": "usage-test",
+                "choices": [],
+                "usage": {
+                    "prompt_tokens": 10,
+                    "completion_tokens": len(token_ids),
+                    "total_tokens": 10 + len(token_ids),
+                },
+            },
+            "stream_state": state,
+            "prompt_tokens": 10,
+        },
+    )
+    assert r2.status_code == 200
+    d2 = r2.json()
+    assert d2["chunk"]["choices"] == []
+    assert d2["chunk"]["usage"]["prompt_tokens"] == 10
+    assert d2["chunk"]["usage"]["completion_tokens"] == len(token_ids)
diff --git a/vllm/entrypoints/scale_out/derender/api_router.py b/vllm/entrypoints/scale_out/derender/api_router.py
index 3f88d51f0a9..4a139144db9 100644
--- a/vllm/entrypoints/scale_out/derender/api_router.py
+++ b/vllm/entrypoints/scale_out/derender/api_router.py
@@ -5,15 +5,17 @@ from http import HTTPStatus
 from fastapi import APIRouter, Depends, Request
 from fastapi.responses import JSONResponse
 
-from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionResponse
-from vllm.entrypoints.openai.completion.protocol import CompletionResponse
 from vllm.entrypoints.openai.engine.protocol import ErrorResponse
 from vllm.entrypoints.serve.utils.api_utils import validate_json_request
 from vllm.logger import init_logger
 
 from ..token_in_token_out.protocol import (
-    DerenderChatRequest,
-    DerenderCompletionRequest,
+    DerenderChatRequestUnion,
+    DerenderChatStreamRequest,
+    DerenderChatStreamResponse,
+    DerenderCompletionRequestUnion,
+    DerenderCompletionStreamRequest,
+    DerenderCompletionStreamResponse,
 )
 from .serving import ServingDerender
 
@@ -29,46 +31,96 @@ def derender(request: Request) -> ServingDerender | None:
 @router.post(
     "/v1/chat/completions/derender",
     dependencies=[Depends(validate_json_request)],
-    response_model=ChatCompletionResponse,
     responses={
         HTTPStatus.BAD_REQUEST.value: {"model": ErrorResponse},
         HTTPStatus.NOT_FOUND.value: {"model": ErrorResponse},
         HTTPStatus.INTERNAL_SERVER_ERROR.value: {"model": ErrorResponse},
     },
 )
-async def derender_chat_completion(request: DerenderChatRequest, raw_request: Request):
+async def derender_chat_completion(
+    request: DerenderChatRequestUnion,
+    raw_request: Request,
+):
+    """Derender a generate response into a ChatCompletionResponse.
+
+    Accepts both non-streaming (``stream=false``, default) and streaming
+    (``stream=true``) request bodies on the same path; FastAPI validates and
+    routes on the ``stream`` discriminator.
+
+    Non-streaming: body is ``DerenderChatRequest`` (``generate_response`` with
+    the complete token list). Returns a ``ChatCompletionResponse``.
+
+    Streaming: body is ``DerenderChatStreamRequest`` (one ``generate_chunk``
+    delta + optional ``stream_state``). Returns a ``DerenderChatStreamResponse``
+    (``chunk`` + ``stream_state``). The client carries ``stream_state`` between
+    successive calls, one per SSE chunk from ``/inference/v1/generate``.
+    """
     handler = derender(raw_request)
     if handler is None:
         raise NotImplementedError(
             "The model does not support Chat Completions Derender API"
         )
 
-    result = await handler.derender_chat_response(request)
+    if isinstance(request, DerenderChatStreamRequest):
+        stream_result = await handler.derender_chat_stream_response(request)
+        if isinstance(stream_result, ErrorResponse):
+            return JSONResponse(
+                content=stream_result.model_dump(),
+                status_code=stream_result.error.code,
+            )
+        chunk, stream_state = stream_result
+        response = DerenderChatStreamResponse(chunk=chunk, stream_state=stream_state)
+        return JSONResponse(content=response.model_dump())
 
+    result = await handler.derender_chat_response(request)
     if isinstance(result, ErrorResponse):
         return JSONResponse(content=result.model_dump(), status_code=result.error.code)
-
     return JSONResponse(content=result.model_dump())
 
 
 @router.post(
     "/v1/completions/derender",
     dependencies=[Depends(validate_json_request)],
-    response_model=CompletionResponse,
     responses={
         HTTPStatus.BAD_REQUEST.value: {"model": ErrorResponse},
         HTTPStatus.NOT_FOUND.value: {"model": ErrorResponse},
         HTTPStatus.INTERNAL_SERVER_ERROR.value: {"model": ErrorResponse},
     },
 )
-async def derender_completion(request: DerenderCompletionRequest, raw_request: Request):
+async def derender_completion(
+    request: DerenderCompletionRequestUnion,
+    raw_request: Request,
+):
+    """Derender a generate response into a CompletionResponse.
+
+    Accepts both non-streaming (``stream=false``, default) and streaming
+    (``stream=true``) request bodies on the same path.
+
+    Non-streaming: body is ``DerenderCompletionRequest``. Returns a
+    ``CompletionResponse``.
+
+    Streaming: body is ``DerenderCompletionStreamRequest`` (one
+    ``generate_chunk`` + optional ``stream_state``). Returns a
+    ``DerenderCompletionStreamResponse`` (``chunk`` + ``stream_state``).
+    """
     handler = derender(raw_request)
     if handler is None:
         raise NotImplementedError("The model does not support Completions Derender API")
 
-    result = await handler.derender_completion_response(request)
+    if isinstance(request, DerenderCompletionStreamRequest):
+        stream_result = await handler.derender_completion_stream_response(request)
+        if isinstance(stream_result, ErrorResponse):
+            return JSONResponse(
+                content=stream_result.model_dump(),
+                status_code=stream_result.error.code,
+            )
+        chunk, stream_state = stream_result
+        response = DerenderCompletionStreamResponse(
+            chunk=chunk, stream_state=stream_state
+        )
+        return JSONResponse(content=response.model_dump())
 
+    result = await handler.derender_completion_response(request)
     if isinstance(result, ErrorResponse):
         return JSONResponse(content=result.model_dump(), status_code=result.error.code)
-
     return JSONResponse(content=result.model_dump())
diff --git a/vllm/entrypoints/scale_out/derender/serving.py b/vllm/entrypoints/scale_out/derender/serving.py
index 613ff65ad0e..4b72ce2e9fc 100644
--- a/vllm/entrypoints/scale_out/derender/serving.py
+++ b/vllm/entrypoints/scale_out/derender/serving.py
@@ -4,8 +4,14 @@ import time
 from typing import cast
 
 import vllm.envs as envs
-from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionResponse
-from vllm.entrypoints.openai.completion.protocol import CompletionResponse
+from vllm.entrypoints.openai.chat_completion.protocol import (
+    ChatCompletionResponse,
+    ChatCompletionStreamResponse,
+)
+from vllm.entrypoints.openai.completion.protocol import (
+    CompletionResponse,
+    CompletionStreamResponse,
+)
 from vllm.entrypoints.openai.engine.protocol import (
     ErrorResponse,
     UsageInfo,
@@ -28,7 +34,10 @@ from vllm.renderers.online_derenderer import OnlineDerenderer
 from ..token_in_token_out.mm_serde import encode_mm_kwargs_item
 from ..token_in_token_out.protocol import (
     DerenderChatRequest,
+    DerenderChatStreamRequest,
     DerenderCompletionRequest,
+    DerenderCompletionStreamRequest,
+    DerenderStreamState,
     GenerateResponse,
     MultiModalFeatures,
     PlaceholderRangeInfo,
@@ -200,7 +209,9 @@ class ServingDerender(BaseServing):
             total_prompt_tokens,
             total_completion_tokens,
         ) = await self.online_derenderer.derender_completion(
-            request.generate_responses, request.prompt_tokens
+            request.generate_responses,
+            request.prompt_tokens,
+            completion_request=request.completion_request,
         )
 
         first = request.generate_responses[0]
@@ -237,6 +248,90 @@ class ServingDerender(BaseServing):
             kv_transfer_params=kv_params,
         )
 
+    async def derender_chat_stream_response(
+        self,
+        request: DerenderChatStreamRequest,
+    ) -> tuple[ChatCompletionStreamResponse, DerenderStreamState] | ErrorResponse:
+        """Streaming counterpart to ``derender_chat_response``.
+
+        Processes one ``GenerateStreamResponse`` chunk and returns the
+        derendered chunk together with the updated client carried state.
+
+        ``parser is None`` or no ``chat_request`` until reasoning/tool call
+        functionality added in future PR.
+        """
+        error_check_ret = await self._check_model(request)
+        if error_check_ret is not None:
+            return error_check_ret
+
+        try:
+            chunk, updated_state = await self.online_derenderer.derender_chat_stream(
+                model=request.model,
+                generate_chunk=request.generate_chunk,
+                state=request.stream_state,
+                chat_request=request.chat_request,
+                prompt_tokens=request.prompt_tokens,
+            )
+        except NotImplementedError as exc:
+            return self.create_error_response(exc)
+        except ValueError as exc:
+            return self.create_error_response(str(exc))
+        except (KeyError, IndexError) as exc:
+            return self.create_error_response(
+                f"invalid stream_state: detokenization failed ({exc!r})"
+            )
+
+        logger.debug(
+            "derender_chat_stream request_id=%s model=%s delta_tokens=%d",
+            request.generate_chunk.request_id,
+            request.model,
+            sum(
+                len(c.token_ids) for c in request.generate_chunk.choices if c.token_ids
+            ),
+        )
+        return chunk, updated_state
+
+    async def derender_completion_stream_response(
+        self,
+        request: DerenderCompletionStreamRequest,
+    ) -> tuple[CompletionStreamResponse, DerenderStreamState] | ErrorResponse:
+        """Streaming counterpart to ``derender_completion_response``.
+
+        Processes one ``GenerateStreamResponse`` chunk (one output sequence's
+        delta) and returns the derendered chunk and updated state.
+        """
+        error_check_ret = await self._check_model(request)
+        if error_check_ret is not None:
+            return error_check_ret
+
+        try:
+            (
+                chunk,
+                updated_state,
+            ) = await self.online_derenderer.derender_completion_stream(
+                model=request.model,
+                generate_chunk=request.generate_chunk,
+                state=request.stream_state,
+                prompt_tokens=request.prompt_tokens,
+                completion_request=request.completion_request,
+            )
+        except ValueError as exc:
+            return self.create_error_response(str(exc))
+        except (KeyError, IndexError) as exc:
+            return self.create_error_response(
+                f"invalid stream_state: detokenization failed ({exc!r})"
+            )
+
+        logger.debug(
+            "derender_completion_stream request_id=%s model=%s delta_tokens=%d",
+            request.generate_chunk.request_id,
+            request.model,
+            sum(
+                len(c.token_ids) for c in request.generate_chunk.choices if c.token_ids
+            ),
+        )
+        return chunk, updated_state
+
     @staticmethod
     def _extract_mm_features(
         engine_input: EngineInput,
diff --git a/vllm/entrypoints/scale_out/token_in_token_out/protocol.py b/vllm/entrypoints/scale_out/token_in_token_out/protocol.py
index c22e70b014c..bf387809670 100644
--- a/vllm/entrypoints/scale_out/token_in_token_out/protocol.py
+++ b/vllm/entrypoints/scale_out/token_in_token_out/protocol.py
@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from typing import Any
+from typing import Any, Literal, TypeAlias
 
 from pydantic import (
     BaseModel,
@@ -14,8 +14,12 @@ from vllm.config import ModelConfig
 from vllm.entrypoints.openai.chat_completion.protocol import (
     ChatCompletionLogProbs,
     ChatCompletionRequest,
+    ChatCompletionStreamResponse,
+)
+from vllm.entrypoints.openai.completion.protocol import (
+    CompletionRequest,
+    CompletionStreamResponse,
 )
-from vllm.entrypoints.openai.completion.protocol import CompletionRequest
 from vllm.entrypoints.openai.engine.protocol import StreamOptions, UsageInfo
 from vllm.logprobs import Logprob
 from vllm.renderers import TokenizeParams
@@ -252,21 +256,16 @@ class GenerateResponse(BaseModel):
     )
 
 
-####### Derender (postprocessing) #######
-
-
 class DerenderChatRequest(BaseModel):
     """Request for the /v1/chat/completions/derender endpoint (non-streaming).
 
-    Wraps a complete GenerateResponse and caller-supplied metadata needed to
-    produce a fully-formed ChatCompletionResponse without a GPU.
-
-    Streaming derender would require a separate endpoint design with
-    incremental token delivery, ``OutputProcessor``-based detokenization,
-    and ``parser.parse_delta()`` instead of ``parser.parse()``.
+    Wraps a complete GenerateResponse and caller supplied metadata needed to
+    produce a fully formed ChatCompletionResponse without a GPU.
     """
 
     # --8<-- [start:derender-chat-request]
+    stream: Literal[False] = False
+
     model: str
     """Served model name."""
 
@@ -299,6 +298,8 @@ class DerenderCompletionRequest(BaseModel):
     """
 
     # --8<-- [start:derender-completion-request]
+    stream: Literal[False] = False
+
     model: str
     """Served model name."""
 
@@ -330,3 +331,156 @@ class DerenderCompletionRequest(BaseModel):
                 f"generate_responses length ({len(self.generate_responses)})"
             )
         return self
+
+
+class DerenderStreamState(BaseModel):
+    """Per sequence state for stateless streaming derender.
+
+    The client carries this between successive per chunk HTTP calls to the
+    streaming derender endpoint. All fields are plain JSON serializable data.
+    No opaque tokenizer or parser internals are stored here.
+
+    The detokenization strategy carries the incremental decode offsets
+    directly rather than re-sending the whole token history each chunk.
+    ``detokenize_incrementally`` only ever reads the trailing token window
+    ``prev_tokens[prefix_offset:]``, so we carry just that tail plus the two
+    offsets. Each chunk resumes exactly where the last one stopped, including
+    any partially processed multi-byte character (tracked by ``read_offset``),
+    then trims and rebases the window so it never grows with generation length.
+
+    Performance:
+    - Compute per chunk is O(delta). One ``detokenize_incrementally`` call per
+      new token, independent of how many tokens preceded it.
+    - Transport per chunk is O(window). The carried tail is bounded by the
+      incremental detokenization offset, so cumulative bytes over the wire are
+      O(n) rather than the O(n^2) a full history round trip would incur.
+    """
+
+    prev_tokens: list[str] = Field(default_factory=list)
+    """Trailing decode window. Token strings from ``prefix_offset`` onward.
+
+    Bounded, trimmed and rebased each chunk to the tail
+    ``detokenize_incrementally`` still reads, so it does not grow with the
+    number of chunks.
+    """
+
+    prefix_offset: int = Field(default=0, ge=0)
+    """Prefix offset into ``prev_tokens`` for incremental detokenization."""
+
+    read_offset: int = Field(default=0, ge=0)
+    """Read offset into ``prev_tokens`` for incremental detokenization."""
+
+    @field_validator("prev_tokens")
+    @classmethod
+    def _bound_prev_tokens(cls, v: list[str]) -> list[str]:
+        # INITIAL_INCREMENTAL_DETOKENIZATION_OFFSET is small (5) and the trimmed
+        # window is O(offset). A generous limit rejects unusually large or malformed
+        # payloads without restricting legitimate multi-byte sequences.
+        limit = 1024
+        if len(v) > limit:
+            raise ValueError(f"prev_tokens length ({len(v)}) exceeds maximum ({limit})")
+        return v
+
+    role_sent: bool = False
+    """True once the initial ``role: "assistant"`` delta has been emitted.
+
+    Prevents re-emitting the role on subsequent chunks even when the detok
+    window is transiently empty (e.g. usage only final chunk).
+    """
+
+    # TODO: Properties used in follow on PR for tool call parsing
+    last_content: str | None = None
+    """Last emitted cumulative assistant content text."""
+
+    last_reasoning: str | None = None
+    """Last emitted cumulative reasoning text."""
+
+    last_tool_call_ids: list[str] = Field(default_factory=list)
+    """Stable tool-call IDs, assigned once when each call first appears.
+
+    Prevents ID regeneration across re-parsing.
+    """
+
+
+class DerenderChatStreamRequest(BaseModel):
+    """One chunk streaming derender request for /v1/chat/completions/derender.
+
+    The client sends one request per SSE chunk received from
+    ``/inference/v1/generate``.  Each request carries the generate chunk
+    plus the ``stream_state`` returned by the previous call (``None`` on the
+    first call).  The response contains the derendered chunk and the updated
+    state to be passed to the next call.
+
+    This implements stateless no server side session. All mutable state lives in
+    the client carried ``stream_state``.
+    """
+
+    stream: Literal[True]
+
+    model: str
+    generate_chunk: GenerateStreamResponse
+    """One SSE chunk from ``/inference/v1/generate`` (``stream=True``)."""
+
+    stream_state: DerenderStreamState | None = None
+    """Client carried detok state from the previous call. ``None`` on first."""
+
+    prompt_tokens: int | None = None
+    """Prompt token count for usage. Forwarded from the render step."""
+
+    chat_request: ChatCompletionRequest | None = None
+    """The original (post adjust_request) ChatCompletionRequest from /render."""
+
+
+class DerenderCompletionStreamRequest(BaseModel):
+    """One chunk streaming derender request for /v1/completions/derender.
+
+    Parallel to ``DerenderChatStreamRequest`` for the completions endpoint.
+    Each call processes one SSE chunk (one output sequence's delta) and
+    returns the derendered chunk plus updated state.
+    """
+
+    stream: Literal[True]
+
+    model: str
+    generate_chunk: GenerateStreamResponse
+    """One SSE chunk from ``/inference/v1/generate``."""
+
+    stream_state: DerenderStreamState | None = None
+    """Client-carried detok state. ``None`` on the first call."""
+
+    prompt_tokens: int | None = None
+    """Prompt token count for usage."""
+
+    completion_request: CompletionRequest | None = None
+    """The original (post adjust_request) CompletionRequest from /render."""
+
+
+class DerenderChatStreamResponse(BaseModel):
+    """Response for one streaming chat derender chunk.
+
+    Pairs the derendered SSE chunk with the updated client carried state to
+    pass to the next call.
+    """
+
+    chunk: ChatCompletionStreamResponse
+    stream_state: DerenderStreamState
+
+
+class DerenderCompletionStreamResponse(BaseModel):
+    """Response for one streaming completions derender chunk.
+
+    Parallel to ``DerenderChatStreamResponse`` for the completions endpoint.
+    """
+
+    chunk: CompletionStreamResponse
+    stream_state: DerenderStreamState
+
+
+# Determines the type by checking the ``stream`` field's literal value. A body without
+# ``stream`` validates as the non-streaming member
+# (``stream`` defaults to ``False`` there), so FastAPI can validate and dispatch both
+# shapes on a single path.
+DerenderChatRequestUnion: TypeAlias = DerenderChatRequest | DerenderChatStreamRequest
+DerenderCompletionRequestUnion: TypeAlias = (
+    DerenderCompletionRequest | DerenderCompletionStreamRequest
+)
diff --git a/vllm/entrypoints/serve/engine/typing.py b/vllm/entrypoints/serve/engine/typing.py
index 2e01c092c7b..a6878d50015 100644
--- a/vllm/entrypoints/serve/engine/typing.py
+++ b/vllm/entrypoints/serve/engine/typing.py
@@ -17,7 +17,9 @@ from vllm.entrypoints.openai.completion.protocol import (
 from vllm.entrypoints.openai.responses.protocol import ResponsesRequest
 from vllm.entrypoints.scale_out.token_in_token_out.protocol import (
     DerenderChatRequest,
+    DerenderChatStreamRequest,
     DerenderCompletionRequest,
+    DerenderCompletionStreamRequest,
     GenerateRequest,
     GenerateResponse,
 )
@@ -54,6 +56,7 @@ CompletionLikeRequest: TypeAlias = (
     | TokenizeCompletionRequest
     | DetokenizeRequest
     | DerenderCompletionRequest
+    | DerenderCompletionStreamRequest
 )
 
 ChatLikeRequest: TypeAlias = (
@@ -61,6 +64,7 @@ ChatLikeRequest: TypeAlias = (
     | BatchChatCompletionRequest
     | TokenizeChatRequest
     | DerenderChatRequest
+    | DerenderChatStreamRequest
 )
 
 SpeechToTextRequest: TypeAlias = TranscriptionRequest | TranslationRequest
diff --git a/vllm/renderers/online_derenderer.py b/vllm/renderers/online_derenderer.py
index 22e0b45790e..a1285d8e178 100644
--- a/vllm/renderers/online_derenderer.py
+++ b/vllm/renderers/online_derenderer.py
@@ -10,19 +10,29 @@ from vllm.entrypoints.openai.chat_completion.protocol import (
     ChatCompletionNamedToolChoiceParam,
     ChatCompletionRequest,
     ChatCompletionResponseChoice,
+    ChatCompletionResponseStreamChoice,
+    ChatCompletionStreamResponse,
     ChatMessage,
 )
 from vllm.entrypoints.openai.completion.protocol import (
     CompletionLogProbs,
+    CompletionRequest,
     CompletionResponseChoice,
+    CompletionResponseStreamChoice,
+    CompletionStreamResponse,
+)
+from vllm.entrypoints.openai.engine.protocol import DeltaMessage, ToolCall, UsageInfo
+from vllm.entrypoints.scale_out.token_in_token_out.protocol import (
+    DerenderStreamState,
+    GenerateResponse,
+    GenerateStreamResponse,
 )
-from vllm.entrypoints.openai.engine.protocol import ToolCall
-from vllm.entrypoints.scale_out.token_in_token_out.protocol import GenerateResponse
 from vllm.entrypoints.serve.utils.request_logger import RequestLogger
 from vllm.logger import init_logger
 from vllm.parser import Parser, ParserManager
 from vllm.renderers import BaseRenderer
 from vllm.tokenizers import TokenizerLike
+from vllm.tokenizers.detokenizer_utils import detokenize_incrementally
 from vllm.utils import random_uuid
 from vllm.utils.async_utils import make_async
 
@@ -168,9 +178,15 @@ class OnlineDerenderer:
                     tool_calls=tc_items,
                 )
             else:
-                # No parser: plain detokenization.
+                # No parser: plain detokenization honouring the request's
+                # skip_special_tokens (default True when no request was given).
+                skip_special = (
+                    chat_request.skip_special_tokens
+                    if chat_request is not None
+                    else True
+                )
                 decoded_text = tokenizer.decode(
-                    choice.token_ids, skip_special_tokens=True
+                    choice.token_ids, skip_special_tokens=skip_special
                 )
                 message = ChatMessage(role="assistant", content=decoded_text)
 
@@ -185,23 +201,200 @@ class OnlineDerenderer:
 
         return choices
 
+    def _detokenize_delta(
+        self,
+        tokenizer: TokenizerLike,
+        delta_token_ids: list[int],
+        state: DerenderStreamState,
+        skip_special_tokens: bool = True,
+        spaces_between_special_tokens: bool = True,
+    ) -> tuple[str, DerenderStreamState]:
+        """Incrementally detokenize ``delta_token_ids`` from prior stream state.
+
+        Resumes decoding from the offsets carried in ``state`` rather than
+        replaying token history. ``state.prev_tokens`` holds the trailing decode
+        window (from ``prefix_offset`` onward) that ``detokenize_incrementally``
+        still needs to reproduce any partially read multi-byte character
+        (tracked by ``read_offset``). The delta tokens are fed straight onto it.
+
+        The window is bounded. ``detokenize_incrementally`` never reads before
+        ``prefix_offset``, so after processing we trim ``prev_tokens`` to that
+        tail and rebase the offsets to it. State transport therefore stays
+        O(window) per chunk instead of re-sending the full token history.
+
+        Args:
+            tokenizer: The tokenizer to decode with.
+            delta_token_ids: New token IDs from this generate chunk.
+            state: Client carried detok state from the previous call.
+            skip_special_tokens: Passed through to the tokenizer.
+            spaces_between_special_tokens: Passed through to the tokenizer.
+
+        Returns:
+            (new_text, updated_state) — the delta text for this chunk and the
+            state to pass to the next call.
+        """
+        prev_tokens = list(state.prev_tokens)
+        prefix_offset = state.prefix_offset
+        read_offset = state.read_offset
+
+        text_parts: list[str] = []
+        for tok_id in delta_token_ids:
+            # prev_tokens is a (possibly empty) list, never None, so this
+            # always takes the non first iter path and only consumes
+            # all_input_ids[-1].
+            new_toks, text, prefix_offset, read_offset = detokenize_incrementally(
+                tokenizer=tokenizer,
+                all_input_ids=[tok_id],
+                prev_tokens=prev_tokens,
+                prefix_offset=prefix_offset,
+                read_offset=read_offset,
+                skip_special_tokens=skip_special_tokens,
+                spaces_between_special_tokens=spaces_between_special_tokens,
+            )
+            prev_tokens = prev_tokens + new_toks
+            text_parts.append(text)
+
+        # Trim to the tail still readable by detokenize_incrementally
+        # (everything before prefix_offset is dead) and rebase the offsets so
+        # the carried window stays bounded regardless of generation length.
+        trimmed = prev_tokens[prefix_offset:]
+        updated_state = state.model_copy(
+            update={
+                "prev_tokens": trimmed,
+                "prefix_offset": 0,
+                "read_offset": read_offset - prefix_offset,
+            }
+        )
+        return "".join(text_parts), updated_state
+
+    async def derender_chat_stream(
+        self,
+        model: str,
+        generate_chunk: GenerateStreamResponse,
+        state: DerenderStreamState | None = None,
+        chat_request: ChatCompletionRequest | None = None,
+        prompt_tokens: int | None = None,
+    ) -> tuple[ChatCompletionStreamResponse, DerenderStreamState]:
+        """Process one GenerateStreamResponse chunk for streaming chat derender.
+
+        TODO: parse path for reasoning and tool calls is implemented in future PR.
+
+        Unlike OpenAI's API, which always emits ``role: "assistant"`` on the
+        very first chunk, this emits it on the first chunk with a non empty
+        ``choices`` list. A leading usage only chunk therefore defers the
+        role to the following content chunk instead of sending an empty
+        role only delta.
+
+        Args:
+            model: Model name for the response object.
+            generate_chunk: One SSE chunk from ``/inference/v1/generate``.
+            state: Client carried detok state (``None`` for first call).
+            chat_request: Original ChatCompletionRequest from ``/render``.
+            prompt_tokens: Prompt token count for the usage chunk.
+
+        Returns:
+            (chunk, updated_state) — the derendered SSE chunk and the state
+            the client must pass to the next call.
+        """
+        if state is None:
+            state = DerenderStreamState()
+
+        if self.parser is not None:
+            # TODO: Follow on PR will implement the parse path.  Check on the
+            # parser alone (fail closed). A parser configured model must never
+            # fall through to plain detok on the streaming path, even when
+            # ``chat_request`` is omitted or reasoning/tool markup would leak
+            # into ``delta.content``.
+            raise NotImplementedError(
+                "Streaming chat derender is not yet supported for models with "
+                "a reasoning or tool parser configured. Use the non-streaming "
+                "derender endpoint (stream=false) for parsed output."
+            )
+
+        # A single DerenderStreamState is threaded through every choice in
+        # this chunk. Correct only when there is at most one choice per SSE
+        # event (n=1, one call per index), as the streaming derender
+        # protocol assumes. Multiple choices sharing one chunk would corrupt
+        # each other's detok window.
+        if len(generate_chunk.choices) > 1:
+            raise ValueError(
+                "derender_chat_stream expects at most one choice per chunk"
+            )
+
+        tokenizer = self.renderer.get_tokenizer()
+        skip_special = (
+            chat_request.skip_special_tokens if chat_request is not None else True
+        )
+        stream_choices: list[ChatCompletionResponseStreamChoice] = []
+        updated_state = state
+
+        for choice in generate_chunk.choices:
+            delta_tids = choice.token_ids or []
+            new_text, updated_state = self._detokenize_delta(
+                tokenizer, delta_tids, updated_state, skip_special_tokens=skip_special
+            )
+
+            include_role = not updated_state.role_sent
+            if include_role:
+                updated_state = updated_state.model_copy(update={"role_sent": True})
+
+            delta = DeltaMessage(
+                role="assistant" if include_role else None,
+                content=new_text if new_text else None,
+            )
+            stream_choices.append(
+                ChatCompletionResponseStreamChoice(
+                    index=choice.index,
+                    delta=delta,
+                    finish_reason=choice.finish_reason,
+                )
+            )
+
+        usage: UsageInfo | None = None
+        if generate_chunk.usage is not None:
+            u = generate_chunk.usage
+            pt = prompt_tokens if prompt_tokens is not None else (u.prompt_tokens or 0)
+            ct = u.completion_tokens or 0
+            usage = UsageInfo(
+                prompt_tokens=pt,
+                completion_tokens=ct,
+                total_tokens=pt + ct,
+            )
+
+        chunk = ChatCompletionStreamResponse(
+            id=generate_chunk.request_id,
+            model=model,
+            choices=stream_choices,
+            usage=usage,
+        )
+        return chunk, updated_state
+
     async def derender_completion(
         self,
         generate_responses: list[GenerateResponse],
         prompt_tokens: list[int] | None = None,
+        completion_request: CompletionRequest | None = None,
     ) -> tuple[list[CompletionResponseChoice], int, int]:
-        return await self._derender_completion_async(generate_responses, prompt_tokens)
+        return await self._derender_completion_async(
+            generate_responses, prompt_tokens, completion_request
+        )
 
     def _derender_completion(
         self,
         generate_responses: list[GenerateResponse],
         prompt_tokens: list[int] | None = None,
+        completion_request: CompletionRequest | None = None,
     ) -> tuple[list[CompletionResponseChoice], int, int]:
         n = len(generate_responses)
         prompt_tokens_list: list[int] = (
             prompt_tokens if prompt_tokens is not None else [0] * n
         )
 
+        skip_special = (
+            completion_request.skip_special_tokens
+            if completion_request is not None
+            else True
+        )
         tokenizer = self.renderer.get_tokenizer()
         choices: list[CompletionResponseChoice] = []
         total_prompt_tokens = 0
@@ -217,7 +410,7 @@ class OnlineDerenderer:
                     )
 
                 decoded_text = tokenizer.decode(
-                    choice.token_ids, skip_special_tokens=True
+                    choice.token_ids, skip_special_tokens=skip_special
                 )
                 completion_logprobs = None
                 if choice.logprobs is not None:
@@ -239,6 +432,88 @@ class OnlineDerenderer:
 
         return choices, total_prompt_tokens, total_completion_tokens
 
+    async def derender_completion_stream(
+        self,
+        model: str,
+        generate_chunk: GenerateStreamResponse,
+        state: DerenderStreamState | None = None,
+        prompt_tokens: int | None = None,
+        completion_request: CompletionRequest | None = None,
+    ) -> tuple[CompletionStreamResponse, DerenderStreamState]:
+        """Process one GenerateStreamResponse chunk for streaming completions.
+
+        Each call takes one SSE chunk from ``/inference/v1/generate`` plus the
+        client carried ``stream_state`` and returns a ``CompletionStreamResponse``
+        chunk and the updated state.
+
+        The generate stream emits one choice per SSE event, so this method
+        processes one output sequence at a time.  For ``n > 1`` the client
+        maintains one ``DerenderStreamState`` per ``choice.index``.
+
+        Args:
+            model: Model name for the response object.
+            generate_chunk: One SSE chunk from ``/inference/v1/generate``.
+            state: Client carried detok state (``None`` → first call).
+            prompt_tokens: Prompt token count for usage (from the render step).
+            completion_request: Original CompletionRequest from ``/render``;
+                supplies ``skip_special_tokens``.
+
+        Returns:
+            (chunk, updated_state) — the derendered chunk and updated state.
+        """
+        if state is None:
+            state = DerenderStreamState()
+
+        # See the equivalent check in derender_chat_stream: a single
+        # DerenderStreamState is threaded through every choice in this
+        # chunk, so more than one choice per chunk would corrupt the
+        # detok window across choices.
+        if len(generate_chunk.choices) > 1:
+            raise ValueError(
+                "derender_completion_stream expects at most one choice per chunk"
+            )
+
+        tokenizer = self.renderer.get_tokenizer()
+        skip_special = (
+            completion_request.skip_special_tokens
+            if completion_request is not None
+            else True
+        )
+        stream_choices: list[CompletionResponseStreamChoice] = []
+        updated_state = state
+
+        for choice in generate_chunk.choices:
+            delta_tids = choice.token_ids or []
+            new_text, updated_state = self._detokenize_delta(
+                tokenizer, delta_tids, updated_state, skip_special_tokens=skip_special
+            )
+            stream_choices.append(
+                CompletionResponseStreamChoice(
+                    index=choice.index,
+                    text=new_text,
+                    finish_reason=choice.finish_reason,
+                )
+            )
+
+        usage: UsageInfo | None = None
+        if generate_chunk.usage is not None:
+            u = generate_chunk.usage
+            pt = prompt_tokens if prompt_tokens is not None else (u.prompt_tokens or 0)
+            ct = u.completion_tokens or 0
+            usage = UsageInfo(
+                prompt_tokens=pt,
+                completion_tokens=ct,
+                total_tokens=pt + ct,
+            )
+
+        chunk = CompletionStreamResponse(
+            id=generate_chunk.request_id,
+            model=model,
+            choices=stream_choices,
+            usage=usage,
+        )
+        return chunk, updated_state
+
 
 def _parse_token_id_placeholder(token: str) -> int | None:
     """Extract token ID from a 'token_id:N' placeholder string."""