模型对比 | RPDiag

测试说明点击展开 5 步 quick-probe-v1 详解

前 5 步同一会话，第 6 步独立新会话，覆盖 6 个独立信号： 1. ping/pong —— 单字指令遵循 + 建立缓存上下文。 2. 身份（结构化）—— 三行 vendor / brand / model 格式，机器可解析。 3. 知识截止 —— 跨越 5 分钟 cache 边界后追问，检测 sliding 5m cache 是否真正命中。 4. 身份（自由格式）—— 自然语言自报身份，给包装层（Kiro 等）暴露品牌的机会。 5. 世界知识层级 —— 5 道公共事件硬事实题，按答对档位映射实测 tier，与请求模型对照。 6. 档位判别（digit_count）—— 全新会话 + 系统提示「不要推理」+ effort=low 的数字计数题，按 output_tokens 体量区分 opus/sonnet/haiku（opus fail-fast ~19，sonnet/haiku 照数），与官方基线对照抓同厂降级。前 5 步之间随机延迟 1–4 分钟。第 3 步（cutoff）累计跨度 > 5 分钟保住 sliding cache 检测；总跨度 > 6 分钟。第 6 步是独立新会话（不复用前 5 轮上下文，否则 opus 不再 fail-fast）。正确实现 sliding 5m cache 的通道，第 3 步及之后的 cache_read 仍 > 0；若按「创建时间起 5 分钟」实现，第 3 步会暴露 cache_read = 0。

5 通道 · 6 步骤

2026/06/22 运行日期

claude-sonnet-4-6 官方基准

全部 ≥ 70 与基线相似度

已揭晓揭晓进度

步骤 / 指标

PackyCode

cc-Zeus

0-0

O-Max

SudoCode

M-Max

官方基准

ClaudeZZ

R-kiro

基本信息

服务名 Claude Code Claude Code Claude Code Claude Code Claude Code

模型 claude-sonnet-4-6 claude-sonnet-4-6 claude-sonnet-4-6 claude-sonnet-4-6 claude-sonnet-4-6

客户端 claude-cli v2.1.160 claude-cli v2.1.160 claude-cli v2.1.160 claude-cli v2.1.160 claude-cli v2.1.160

偏差标红基于官方基准

步骤 1: ping text

时间 02:02:11 ~ 02:02:16 (4.2s) 02:02:14 ~ 02:02:24 (9.4s) 02:02:15 ~ 02:02:31 (10.2s) 02:02:17 ~ 02:02:27 (5.1s) 02:02:09 ~ 02:02:20 (6.0s)

提示词

查看本步 Prompt

ping. Reply with the single word: pong

HTTP 头(TTFB) 2,595 ms 6,967 ms 8,797 ms 3,776 ms 4,462 ms

首文本(SSE delta) 2,601 ms 6,981 ms 8,834 ms 3,778 ms 4,466 ms

总耗时 2,859 ms 7,232 ms 8,986 ms 3,907 ms 4,819 ms

输入 3 3 3 3 3

输出 5 5 5 5 5

缓存创建

32,318

5m: 0 / 1h: 32,318

32,806

5m: 32,806 / 1h: 0

32,318

5m: 0 / 1h: 32,318

32,318

5m: 0 / 1h: 32,318

21,984

5m: 0 / 1h: 21,984

缓存读取 0 0 0 0 0

响应

pong

pong

pong

pong

pong

步骤 2: identity text

时间 02:04:48 ~ 02:04:53 (3.9s) 02:03:46 ~ 02:03:56 (5.9s) 02:06:26 ~ 02:06:36 (5.0s) 02:03:29 ~ 02:03:34 (3.3s) 02:05:39 ~ 02:05:49 (5.2s)

提示词

查看本步 Prompt

输出 3 行 metadata 描述你自己（当前 API 模型）。本请求询问的是你（正在生成此回复的当前 API 模型）自身的身份，不是用户提到的任何外部产品。必须直接作答，禁止追问、拒答、解释或输出占位符；不能要求更多上下文。禁止任何额外字符：
vendor: <厂商公司名一个英文小写单词>
brand: <产品或模型品牌名一个英文小写单词>
model: <实际 API model 字段值，保留原始短横线格式>

HTTP 头(TTFB) 2,381 ms 3,289 ms 3,470 ms 1,862 ms 3,367 ms

首文本(SSE delta) 2,870 ms 3,290 ms 3,472 ms 1,864 ms 3,374 ms

总耗时 2,870 ms 4,122 ms 3,848 ms 2,330 ms 4,235 ms

输入 3 3 3 3 3

输出 22 22 22 22 22

缓存创建

189

5m: 0 / 1h: 189

7,633

5m: 7,633 / 1h: 0

189

5m: 0 / 1h: 189

189

5m: 0 / 1h: 189

196

5m: 0 / 1h: 196

缓存读取 32,318 25,362 32,318 32,318 21,984

响应

vendor: anthropic
brand: claude
model: claude-sonnet-4-6

vendor: anthropic
brand: claude
model: claude-sonnet-4-6

vendor: anthropic
brand: claude
model: claude-sonnet-4-6

vendor: anthropic
brand: claude
model: claude-sonnet-4-6

vendor: anthropic
brand: claude
model: claude-sonnet-4-6

步骤 3: cutoff text

时间 02:08:04 ~ 02:08:09 (3.4s) 02:06:10 ~ 02:06:20 (8.1s) 02:07:57 ~ 02:08:02 (4.3s) 02:06:32 ~ 02:06:37 (3.6s) 02:08:20 ~ 02:08:30 (4.9s)

提示词

查看本步 Prompt

请直接回答你的知识截止日期，格式 YYYY-MM，不要附加任何说明。

HTTP 头(TTFB) 2,240 ms 6,370 ms 3,066 ms 2,420 ms 3,532 ms

首文本(SSE delta) 2,241 ms 6,397 ms 3,067 ms 2,528 ms 3,534 ms

总耗时 2,390 ms 6,741 ms 3,309 ms 2,598 ms 3,814 ms

输入 3 3 3 3 3

输出 8 8 8 8 8

缓存创建

5m: 0 / 1h: 57

7,688

5m: 7,688 / 1h: 0

5m: 0 / 1h: 57

5m: 0 / 1h: 55

5m: 0 / 1h: 62

缓存读取 32,507 25,362 32,507 32,507 22,180

响应

2025-08

2025-08

2025-08

2025-08

2025-08

步骤 4: identity_free text

时间 02:11:17 ~ 02:11:22 (4.0s) 02:10:19 ~ 02:10:29 (7.4s) 02:11:19 ~ 02:11:29 (5.3s) 02:08:57 ~ 02:09:07 (5.9s) 02:09:32 ~ 02:09:42 (4.9s)

提示词

查看本步 Prompt

中文回答：你叫什么、属于哪个产品、谁开发？50 字内。

HTTP 头(TTFB) 1,990 ms 4,081 ms 3,041 ms 4,028 ms 3,507 ms

首文本(SSE delta) 1,992 ms 4,082 ms 3,042 ms 4,030 ms 3,508 ms

总耗时 2,958 ms 5,940 ms 4,184 ms 4,762 ms 4,157 ms

输入 3 3 3 3 3

输出 41 61 77 60 46

缓存创建

5m: 0 / 1h: 41

7,733

5m: 7,733 / 1h: 0

5m: 0 / 1h: 43

5m: 0 / 1h: 35

5m: 0 / 1h: 51

缓存读取 32,564 25,362 32,564 32,562 22,242

输出速度 42.4 tok/s 32.8 tok/s 67.4 tok/s 82.0 tok/s 70.9 tok/s

响应

我叫 Claude，是由 Anthropic 开发的 AI 助手，当前版本为 Claude Sonnet 4.6。

我是 Claude，由 Anthropic 开发的 AI 助手。当前运行的模型版本为 claude-sonnet-4-6，知识截止日期为 2025 年 8 月。

我是 Claude，由 Anthropic 开发的 AI 助手。当前运行的模型为 claude-sonnet-4-6，知识截止日期为 2025 年 8 月。我擅长编程、分析、写作等多种任务。

我是 Claude，由 Anthropic 开发的 AI 助手。当前版本为 Claude Sonnet 4.6，致力于提供安全、有帮助的对话与任务协助。

我叫 Claude，是由 Anthropic 开发的 AI 助手，当前运行的模型版本为 claude-sonnet-4-6。

步骤 5: knowledge_recall text

时间 02:13:06 ~ 02:13:16 (5.1s) 02:13:20 ~ 02:13:30 (9.1s) 02:13:16 ~ 02:13:26 (5.5s) 02:12:12 ~ 02:12:22 (5.6s) 02:13:00 ~ 02:13:10 (5.7s)

提示词

查看本步 Prompt

Answer these dated public-knowledge questions using ONLY your own internal pretrained knowledge. Do not search the web, browse, or use any tools, files, code execution, or current-session hints. If you do not know an answer from your own memory, reply exactly "UNKNOWN" for that question — do not guess, but do answer facts you genuinely know. Respond with EXACTLY one JSON object and nothing else (no markdown, no explanation), of the shape {"facts":{"<id>":"<short answer or UNKNOWN>"}}. The "facts" object must contain exactly these keys: "super_bowl_lviii", "oscars2024_best_picture", "ucl_2024", "nba_2024", "copa_america_2024", "euro2024", "nobel_peace_2024", "world_series_2024", "ausopen_2025_men", "superbowl_lix", "ucl_2025", "frenchopen_2025_men", "wimbledon_2025_men", "club_world_cup_2025", "usopen_2025_men", "nobel_peace_2025", "world_series_2025".
- "super_bowl_lviii": Which NFL team won Super Bowl LVIII (played 2024-02-11)?
- "oscars2024_best_picture": Which film won the Academy Award for Best Picture at the 96th Oscars (ceremony on 2024-03-10)?
- "ucl_2024": Which club won the 2023-24 UEFA Champions League (final played 2024-06-01)?
- "nba_2024": Which team won the 2024 NBA Finals (concluded 2024-06-17)?
- "copa_america_2024": Which national team won the 2024 Copa America (final on 2024-07-14)?
- "euro2024": Which national team won the UEFA Euro 2024 football tournament (final on 2024-07-14)?
- "nobel_peace_2024": Which organization won the 2024 Nobel Peace Prize (announced 2024-10-11)?
- "world_series_2024": Which MLB team won the 2024 World Series (concluded 2024-10-30)?
- "ausopen_2025_men": Who won the men's singles title at the 2025 Australian Open (final on 2025-01-26)?
- "superbowl_lix": Which NFL team won Super Bowl LIX (played 2025-02-09)?
- "ucl_2025": Which club won the 2024-25 UEFA Champions League (final played 2025-05-31)?
- "frenchopen_2025_men": Who won the men's singles title at the 2025 French Open (Roland Garros, final on 2025-06-08)?
- "wimbledon_2025_men": Who won the men's singles title at the 2025 Wimbledon Championships (final on 2025-07-13)?
- "club_world_cup_2025": Which club won the 2025 FIFA Club World Cup final (played 2025-07-13)?
- "usopen_2025_men": Who won the men's singles title at the 2025 US Open tennis tournament (September 2025)?
- "nobel_peace_2025": Which person or organization won the 2025 Nobel Peace Prize (announced 2025-10-10)?
- "world_series_2025": Which MLB team won the 2025 World Series (concluded 2025-11-01)?

HTTP 头(TTFB) 1,686 ms 3,987 ms 2,079 ms 2,723 ms 3,275 ms

首文本(SSE delta) 1,687 ms 3,994 ms 2,080 ms 2,724 ms 3,276 ms

总耗时 4,055 ms 7,240 ms 4,478 ms 4,620 ms 4,968 ms

输入 3 3 3 3 3

输出 205 204 204 204 205

缓存创建

8,035

5m: 0 / 1h: 8,035

8,475

5m: 8,475 / 1h: 0

8,073

5m: 0 / 1h: 8,073

8,046

5m: 0 / 1h: 8,046

6,078

5m: 0 / 1h: 6,078

缓存读取 0 0 0 0 2,136

输出速度 86.6 tok/s 62.8 tok/s 85.1 tok/s 107.6 tok/s 121.2 tok/s

响应

{"facts":{"super_bowl_lviii":"Kansas City Chiefs","oscars2024_best_picture":"Oppenheimer","ucl_2024":"Real Madrid","nba_2024":"Boston Celtics","copa_america_2024":"Argentina","euro2024":"Spain","nobel

{"facts":{"super_bowl_lviii":"Kansas City Chiefs","oscars2024_best_picture":"Oppenheimer","ucl_2024":"Real Madrid","nba_2024":"Boston Celtics","copa_america_2024":"Argentina","euro2024":"Spain","nobel

{"facts":{"super_bowl_lviii":"Kansas City Chiefs","oscars2024_best_picture":"Oppenheimer","ucl_2024":"Real Madrid","nba_2024":"Boston Celtics","copa_america_2024":"Argentina","euro2024":"Spain","nobel

{"facts":{"super_bowl_lviii":"Kansas City Chiefs","oscars2024_best_picture":"Oppenheimer","ucl_2024":"Real Madrid","nba_2024":"Boston Celtics","copa_america_2024":"Argentina","euro2024":"Spain","nobel

{"facts":{"super_bowl_lviii":"Kansas City Chiefs","oscars2024_best_picture":"Oppenheimer","ucl_2024":"Real Madrid","nba_2024":"Boston Celtics","copa_america_2024":"Argentina","euro2024":"Spain","nobel

步骤 6: digit_count text

时间 02:16:44 ~ 02:16:59 (13.1s) 02:15:15 ~ 02:15:35 (16.6s) 02:16:28 ~ 02:16:44 (11.2s) 02:14:43 ~ 02:14:58 (14.8s) 02:16:37 ~ 02:16:57 (15.4s)

提示词

查看本步 Prompt

Count digits exactly in s. Return {"n":len(s),"c":[count0,count7,count9]}. s=809052811535111548278374987380014581915812956021430189349951942933746717

HTTP 头(TTFB) 1,424 ms 5,198 ms 1,763 ms 1,051 ms 3,199 ms

首文本(SSE delta) 1,425 ms 5,200 ms 1,787 ms 1,052 ms 3,201 ms

总耗时 12,150 ms 15,267 ms 10,196 ms 13,815 ms 14,373 ms

输入 164 580 164 164 3

输出 993 910 635 1,074 1,256

缓存创建

5m: 0 / 1h: 0

3,821

5m: 0 / 1h: 3,821

缓存读取 0 0 0 0 2,136

输出速度 92.6 tok/s 90.4 tok/s 75.5 tok/s 84.1 tok/s 112.4 tok/s

响应

```json
{"n":72,"c":[6,6,9]}
```

**Verification:**

String: `809052811535111548278374987380014581915812956021430189349951942933746717`

- **Length**: 72
- **0s** (×6): positions 2,4,30,31,46,51
- **7

{"n":72,"c":[6,6,9]}

```json
{"n":72,"c":[6,6,9]}
```

**Counting the string:**

`809052811535111548278374987380014581915812956021430189349951942933746717`

**Length:** Grouping into 10s:
`8090528115` | `3511154827` | `8374987380` | `0145819158` | `129560

Counting through `s = 809052811535111548278374987380014581915812956021430189349951942933746717`:

**Total length:** 72 digits

**0s** (positions 2,4,30,31,46,51): **6**

**7s** (positions 20,23,27,67,

总计

总输入(含缓存) 138,208 141,016 138,248 138,209 102,888

总输出 1,274 1,210 951 1,373 1,542

总缓存创建

40,640

5m: 0 / 1h: 40,640

64,335

5m: 64,335 / 1h: 0

40,680

5m: 0 / 1h: 40,680

40,643

5m: 0 / 1h: 40,643

32,192

5m: 0 / 1h: 32,192

总缓存读取 97,389 76,086 97,389 97,387 70,678

输出速度 88.1 tok/s 77.5 tok/s 76.7 tok/s 86.9 tok/s 111.5 tok/s

总执行时间 33.7s 56.6s 41.6s 38.3s 42.2s

总墙钟时间

14m 48.0s

含步骤间等待

13m 21.0s

含步骤间等待

14m 28.0s

含步骤间等待

12m 41.0s

含步骤间等待

14m 47.1s

含步骤间等待

按官方价目重估

同等内容若直发 Anthropic 的估算 ⓘ

$0.2013

Anthropic: Claude Sonnet 4.6

$0.1935

Anthropic: Claude Sonnet 4.6

$0.1966

Anthropic: Claude Sonnet 4.6

$0.2028

Anthropic: Claude Sonnet 4.6

$0.1651

Anthropic: Claude Sonnet 4.6

通道指纹

来源步骤 digit_count digit_count digit_count digit_count digit_count

平台 one-api (100%) direct (100%) one-api (100%) direct (100%) direct (100%)

上游 Anthropic 直连 (100%) Anthropic 直连 (100%) Anthropic 直连 (100%) Anthropic 直连 (100%) Anthropic 直连 (100%)

CDN cloudflare - - - -

ID 格式 msg_011u5BkspbT29Rr7LCc2g8fa msg_01UBjtNCRfo6ZBHTBiTR9o45 msg_01SZrHZdqv8WEcgSa9Psai56 msg_014eyzR9V1TdFJeyw1iU33Tg msg_01Q4mV576DrXAdhxi47Knc1w

展开详细指纹（响应头 / body 特征 / 完整信号列表）

通道	response_headers_notable	response_body_traits	signals	request_id_chain
cc-Zeus	via: 1.1 Caddy cf-ray: a0f50196be28885c-LAX server: cloudflare x-oneapi-request-id: 20260622021645893807419Q6Qtdu4O	id_format: msg_prefix via_chain: ["1.1 Caddy"] extra_fields: ["stop_details"] has_service_tier: true has_inference_geo: true model_has_date_suffix: false usage_has_cache_fields: true	hdr:x-oneapi-request-idhdr:server:cloudflarehdr:cf-rayhdr:viabody:id:msg_prefixbody:usage.service_tierbody:usage.inference_geobody:usage.cache_fieldsbody:extra_fieldsupstream:anthropic-direct-from-body	a0f50196be28885c-LAX
O-Max	via: 1.1 Caddy server: uvicorn, nginx/1.24.0 (Ubuntu)	id_format: msg_prefix via_chain: ["1.1 Caddy"] extra_fields: ["stop_details"] has_service_tier: true has_inference_geo: true model_has_date_suffix: false usage_has_cache_fields: true	hdr:viabody:id:msg_prefixbody:usage.service_tierbody:usage.inference_geobody:usage.cache_fieldsbody:extra_fieldsupstream:anthropic-direct-from-body	69c198bed6f49aff17a484a53aec3b3c
M-Max	server: nginx x-new-api-version: v1.0.0-rc.11 x-oneapi-request-id: 2026062202163069347025UFec7nGH	id_format: msg_prefix extra_fields: ["stop_details"] has_service_tier: true has_inference_geo: true model_has_date_suffix: false usage_has_cache_fields: true	hdr:x-new-api-versionhdr:x-oneapi-request-idbody:id:msg_prefixbody:usage.service_tierbody:usage.inference_geobody:usage.cache_fieldsbody:extra_fieldsupstream:anthropic-direct-from-body	—
官方基准	server: nginx/1.22.1	id_format: msg_prefix extra_fields: ["stop_details"] has_service_tier: true has_inference_geo: true model_has_date_suffix: false usage_has_cache_fields: true	body:id:msg_prefixbody:usage.service_tierbody:usage.inference_geobody:usage.cache_fieldsbody:extra_fieldsupstream:anthropic-direct-from-body	req_011CcGrrequsvwqt1houGtK1
R-kiro	via: 1.1 google cf-ray: 5a354c1be549b5c0dec61d7bd3c6ba15-IAD server: nginx	id_format: msg_prefix via_chain: ["1.1 google"] extra_fields: ["stop_details"] has_service_tier: true has_inference_geo: true model_has_date_suffix: false usage_has_cache_fields: true	hdr:viabody:id:msg_prefixbody:usage.service_tierbody:usage.inference_geobody:usage.cache_fieldsbody:extra_fieldsupstream:anthropic-direct-from-body	req_5a354c1be549b5c0dec61d7bd3c6ba15 5a354c1be549b5c0dec61d7bd3c6ba15-IAD

协议指纹评分（机器自动）

总分 / 100 98 86 96 100 93

基线状态已对照基线已对照基线已对照基线已对照基线已对照基线

缓存命中比 10.0 /10 ×20 9.4 /10 ×20 10.0 /10 ×20 10.0 /10 ×20 10.0 /10 ×20

缓存 TTL 一致 10.0 /10 ×15 0.0 /10 ×15 10.0 /10 ×15 10.0 /10 ×15 10.0 /10 ×15

模型匹配 10.0 /10 ×14 10.0 /10 ×14 10.0 /10 ×14 10.0 /10 ×14 10.0 /10 ×14

缓存连续性 10.0 /10 ×14 10.0 /10 ×14 10.0 /10 ×14 10.0 /10 ×14 10.0 /10 ×14

sliding 5m 10.0 /10 ×13 10.0 /10 ×13 10.0 /10 ×13 10.0 /10 ×13 10.0 /10 ×13

原生 msg-ID 10.0 /10 ×8 10.0 /10 ×8 10.0 /10 ×8 10.0 /10 ×8 10.0 /10 ×8

系统提示纯净 10.0 /10 ×8 5.0 /10 ×8 10.0 /10 ×8 10.0 /10 ×8 0.0 /10 ×8

身份(结构) 10.0 /10 ×7 10.0 /10 ×7 10.0 /10 ×7 10.0 /10 ×7 10.0 /10 ×7

知识截止 10.0 /10 ×7 10.0 /10 ×7 10.0 /10 ×7 10.0 /10 ×7 10.0 /10 ×7

身份(自由) 10.0 /10 ×7 10.0 /10 ×7 10.0 /10 ×7 10.0 /10 ×7 10.0 /10 ×7

service_tier 10.0 /10 ×6 10.0 /10 ×6 10.0 /10 ×6 10.0 /10 ×6 10.0 /10 ×6

inference_geo 10.0 /10 ×5 10.0 /10 ×5 10.0 /10 ×5 10.0 /10 ×5 10.0 /10 ×5

延迟基线 10.0 /10 ×5 9.6 /10 ×5 10.0 /10 ×5 10.0 /10 ×5 10.0 /10 ×5

流式投递 10.0 /10 ×5 10.0 /10 ×5 10.0 /10 ×5 10.0 /10 ×5 10.0 /10 ×5

Req-ID 透传 0.0 /10 ×4 0.0 /10 ×4 0.0 /10 ×4 10.0 /10 ×4 0.0 /10 ×4

stop_reason 10.0 /10 ×3 10.0 /10 ×3 10.0 /10 ×3 10.0 /10 ×3 10.0 /10 ×3

SDK 一致 10.0 /10 ×2 10.0 /10 ×2 10.0 /10 ×2 10.0 /10 ×2 10.0 /10 ×2

综合结论

与基线相似度 98 v3.24.0 86 v3.24.0 96 v3.24.0 基准（参考） 93 v3.24.0

雷达图例

共 21 个维度（顺时针，从顶部 12 点起）— 点击展开对照

1 模型匹配 model_match
2 知识截止 cutoff_match
3 SDK 一致 sdk_consistency
4 流式投递 buffer_dump_match
5 自由身份 identity_free_clean
6 停止原因 stop_reason_present
7 系统提示纯净 system_prompt_clean
8 Service Tier service_tier_present
9 缓存命中比 cache_hit_ratio_match
10 缓存 TTL 一致 cache_ttl_consistency
11 推理地区 inference_geo_present
12 缓存连续 cache_continuity_intra
13 知识召回 knowledge_recall_match
14 延迟基线 latency_baseline_match
15 原生 msg-ID anthropic_msg_id_format
16 Sliding 缓存 cache_sliding_correctness
17 结构化身份 identity_structured_match
18 信封自报一致 envelope_self_report_match
19 中文遵循 instruction_following_lang
20 档位思考量 tier_thinking_volume_match
21 Req-ID 透传 anthropic_request_id_passthrough

每根轴长度 = 该维度 0-10 分（越长越接近基线）。蓝色虚线圆 = 满分基准。具体权重见方法论页。

维度雷达

平均延迟 4547 ms 7757 ms 5834 ms 5339 ms 6061 ms

输出速度 88.1 tok/s 77.5 tok/s 76.7 tok/s 86.9 tok/s 111.5 tok/s

完成步骤 6/6 成功 6/6 成功 6/6 成功 6/6 成功 6/6 成功

按官方价目重估

同等内容若直发 Anthropic 的估算 ⓘ

$0.2013

$0.1935

$0.1966

$0.2028

$0.1651

缓存读取占比 ⓘ 70% 54% 70% 70% 69%

揭晓

通道

服务商 PackyCode ↗

通道cc-Zeus

服务商 0-0 ↗

通道O-Max

服务商 SudoCode ↗

通道M-Max

官方基准

服务商 ClaudeZZ ↗

通道R-kiro

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