fix(llm): price by call time and stop paying for thinking tokens

Two defects that only surface against the real DeepSeek API, both of which
break the cost model rather than the code:

deepseek-v4-flash enables thinking mode by default. The same single-turn
question took 13.5s with it on and 1.8s with it off, and the thinking tokens
are billed at the output rate. That misses NFR-PERF-02b (15s per turn) and
inflates every conversational call. SceneConfig now carries an explicit
`thinking` flag, all four V1 scenes set it to false, and the provider always
sends the parameter rather than relying on the upstream default. Reasoning
tokens are recorded separately in Usage.tokens_reasoning so the cost of ever
turning it back on is visible.

DeepSeek moves to peak/off-peak pricing on 2026-08-17, with peak (Beijing
09:00-12:00 and 14:00-18:00) at twice the off-peak rate. Reports are generated
mostly during peak hours, so a single blended rate would leave ob_ai_call --
the only input to V2 pricing -- off by nearly a factor of two.
PeakAwareRateTable resolves the rate at call time; cost is computed and stored
then, never recomputed later at whatever the current rate happens to be.

Also here:
- Replace the live API key in the redaction test with a synthetic one. The
  test that proves we redact secrets was itself a secret leak waiting for its
  first commit.
- Correct LLM_COST_RATES_JSON, which held placeholder rates.
- Make ids.SnowflakeGenerator take an injectable clock so rollover and
  clock-rollback are testable without patching a slots class.
- Rename exceptions to the -Error suffix (N818) and clear the remaining ruff,
  mypy and import-linter findings, including two dead lines: a duplicate
  pytestmark assignment and a no-op assert on a None return.

Gates: ruff, mypy (96 files incl. tests), 4/4 import contracts,
detect-secrets, 166 unit + 3 integration tests, migration up/down round-trip.

tests/unit/test_thinking_mode.py

@@ -0,0 +1,79 @@
+"""思考模式必须显式声明（deepseek-v4 默认开启）。
+
+保护的是两件事：漏传 ``thinking`` 会让对话延迟从约 2s 涨到 13s 以上（实测同一个单句
+问答），并且思考 token 按输出价计费 —— 一个用户看不到的字段同时打破响应时间目标
+与成本模型。
+"""
+
+from __future__ import annotations
+
+import json
+from typing import Any
+
+import httpx
+import pytest
+
+from packages.llm.providers.deepseek import DeepSeekProvider
+from packages.llm.routing import build_scene_configs
+from packages.llm.types import Message, ProviderRequest, Scene
+
+
+def _request(*, thinking: bool) -> ProviderRequest:
+    return ProviderRequest(
+        model="deepseek-v4-flash",
+        messages=(Message(role="user", content="面试穿什么"),),
+        temperature=0.7,
+        max_tokens=800,
+        timeout_seconds=30.0,
+        thinking=thinking,
+    )
+
+
+def _provider(captured: dict[str, Any]) -> DeepSeekProvider:
+    def handler(request: httpx.Request) -> httpx.Response:
+        captured.update(json.loads(request.content))
+        return httpx.Response(
+            200,
+            json={
+                "choices": [{"message": {"content": "ok"}, "finish_reason": "stop"}],
+                "usage": {
+                    "prompt_tokens": 12,
+                    "completion_tokens": 34,
+                    "completion_tokens_details": {"reasoning_tokens": 20},
+                },
+            },
+        )
+
+    return DeepSeekProvider(
+        api_key="test-key",
+        client=httpx.AsyncClient(transport=httpx.MockTransport(handler)),
+    )
+
+
+@pytest.mark.parametrize(("thinking", "expected"), [(False, "disabled"), (True, "enabled")])
+async def test_thinking_is_always_sent_explicitly(thinking: bool, expected: str) -> None:
+    captured: dict[str, Any] = {}
+    provider = _provider(captured)
+
+    await provider.complete(_request(thinking=thinking))
+
+    # 关键是这个字段一定存在：省略它就等于接受供应商的"开启"默认值
+    assert captured["thinking"] == {"type": expected}
+    await provider.aclose()
+
+
+async def test_reasoning_tokens_are_recorded_for_cost_visibility() -> None:
+    provider = _provider({})
+    response = await provider.complete(_request(thinking=True))
+
+    # 思考 token 已计入 tokens_out，单独留一份用于回答"输出成本有多少花在推理上"
+    assert response.usage.tokens_out == 34
+    assert response.usage.tokens_reasoning == 20
+    await provider.aclose()
+
+
+def test_no_v1_scene_enables_thinking_by_default() -> None:
+    configs = build_scene_configs(primary_provider="deepseek")
+    assert [s for s, c in configs.items() if c.thinking] == []
+    # 且每个场景都显式给了值，不存在"忘了配"的场景
+    assert set(configs) == set(Scene)