diff --git a/nanobot/providers/base.py b/nanobot/providers/base.py
index a794c1adb..23a43b32d 100644
--- a/nanobot/providers/base.py
+++ b/nanobot/providers/base.py
@@ -1259,6 +1259,53 @@ class LLMProvider(ABC):
         """Call chat_stream() and convert unexpected exceptions to error responses."""
         started_at_ms = time.time_ns() // 1_000_000
         started_at_ns = time.monotonic_ns()
+        first_output_at_ns: int | None = None
+
+        def _mark_output(delta: str) -> None:
+            nonlocal first_output_at_ns
+            if delta and first_output_at_ns is None:
+                first_output_at_ns = time.monotonic_ns()
+
+        if self._llm_call_observer is not None:
+            content_callback = kwargs.get("on_content_delta")
+            if callable(content_callback):
+                typed_content_callback = cast(
+                    Callable[[str], Awaitable[None]],
+                    content_callback,
+                )
+
+                async def _timed_content_delta(delta: str) -> None:
+                    _mark_output(delta)
+                    await typed_content_callback(delta)
+
+                kwargs["on_content_delta"] = _timed_content_delta
+
+            thinking_callback = kwargs.get("on_thinking_delta")
+            if callable(thinking_callback):
+                typed_thinking_callback = cast(
+                    Callable[[str], Awaitable[None]],
+                    thinking_callback,
+                )
+
+                async def _timed_thinking_delta(delta: str) -> None:
+                    _mark_output(delta)
+                    await typed_thinking_callback(delta)
+
+                kwargs["on_thinking_delta"] = _timed_thinking_delta
+
+        def _attach_stream_timing(response: LLMResponse) -> LLMResponse:
+            if first_output_at_ns is None:
+                return response
+            finished_at_ns = time.monotonic_ns()
+            if response.ttft_ms is None:
+                response.ttft_ms = max(0, round((first_output_at_ns - started_at_ns) / 1_000_000))
+            if response.generation_ms is None:
+                response.generation_ms = max(
+                    1,
+                    round((finished_at_ns - first_output_at_ns) / 1_000_000),
+                )
+            return response
+
         try:
             provider_context = kwargs.pop("provider_context", None)
             if isinstance(provider_context, ProviderCallContext):
@@ -1284,7 +1331,7 @@ class LLMProvider(ABC):
         except Exception as exc:
             response = LLMResponse(content=f"Error calling LLM: {exc}", finish_reason="error")
         return self._observe_llm_call(
-            response,
+            _attach_stream_timing(response),
             kwargs,
             started_at_ms=started_at_ms,
             started_at_ns=started_at_ns,
diff --git a/tests/providers/test_llm_usage_observer.py b/tests/providers/test_llm_usage_observer.py
index 6c22ca5ac..0e5cbc097 100644
--- a/tests/providers/test_llm_usage_observer.py
+++ b/tests/providers/test_llm_usage_observer.py
@@ -43,6 +43,26 @@ class _BlockingProvider(LLMProvider):
         return "test-model"
 
 
+class _StreamingProvider(LLMProvider):
+    async def chat(self, **_kwargs: object) -> LLMResponse:
+        raise AssertionError("streaming path expected")
+
+    async def chat_stream(self, **kwargs: object) -> LLMResponse:
+        on_thinking_delta = kwargs.get("on_thinking_delta")
+        if callable(on_thinking_delta):
+            await on_thinking_delta("thinking")
+        on_content_delta = kwargs.get("on_content_delta")
+        if callable(on_content_delta):
+            await on_content_delta("ok")
+        return LLMResponse(
+            content="ok",
+            usage=LLMUsage.reported(input_tokens=12, output_tokens=2),
+        )
+
+    def get_default_model(self) -> str:
+        return "test-model"
+
+
 @pytest.mark.asyncio
 async def test_observer_receives_every_retry_attempt() -> None:
     provider = _SequenceProvider(
@@ -114,6 +134,40 @@ async def test_observer_failure_never_breaks_provider_call() -> None:
     assert response.content == "ok"
 
 
+@pytest.mark.asyncio
+async def test_stream_observer_records_physical_attempt_timing(monkeypatch) -> None:
+    provider = _StreamingProvider(provider_name="streaming-provider")
+    events: list[LLMCallRecord] = []
+    provider.set_llm_call_observer(events.append)
+    monotonic_values = iter(
+        [
+            1_000_000_000,
+            1_005_000_000,
+            1_012_000_000,
+            1_013_000_000,
+        ]
+    )
+    monkeypatch.setattr(
+        "nanobot.providers.base.time.monotonic_ns",
+        lambda: next(monotonic_values),
+    )
+
+    response = await provider.chat_stream_with_retry(
+        messages=[{"role": "user", "content": "hello"}],
+        on_content_delta=lambda _delta: asyncio.sleep(0),
+        on_thinking_delta=lambda _delta: asyncio.sleep(0),
+    )
+
+    assert len(events) == 1
+    usage = events[0].usage
+    assert usage is not None
+    assert usage.ttft_ms == 5
+    assert usage.generation_ms == 7
+    assert usage.timed_requests == 1
+    assert usage.measured_output_tokens == 2
+    assert response.usage == usage
+
+
 @pytest.mark.asyncio
 @pytest.mark.parametrize("stream", [False, True])
 async def test_observer_records_cancelled_provider_attempt(stream: bool) -> None: