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

2026/06/23 运行日期

claude-haiku-4-5-20251001 官方基准

1 通道分数 < 70 与基线相似度

已揭晓揭晓进度

步骤 / 指标

官方基准

SudoCode

M-Max

Novaris

O-Max

FoxCode

AIMZ

M-Max

基本信息

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

模型 claude-haiku-4-5-20251001 claude-haiku-4-5-20251001 claude-haiku-4-5-20251001 claude-haiku-4-5-20251001 claude-haiku-4-5-20251001

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 04:35:58 ~ 04:36:03 (4.6s) 04:35:52 ~ 04:36:02 (6.3s) 04:35:56 ~ 04:36:26 (25.4s) 04:35:54 ~ 04:35:59 (4.9s) 04:35:50 ~ 04:36:05 (10.2s)

提示词

查看本步 Prompt

ping. Reply with the single word: pong

HTTP 头(TTFB) 3,028 ms 4,173 ms 23,903 ms 2,680 ms 8,617 ms

首文本(SSE delta) 3,030 ms 4,175 ms 23,904 ms 2,689 ms 8,895 ms

总耗时 3,197 ms 4,880 ms 24,328 ms 3,133 ms 9,028 ms

输入 10 10 10 10 10

输出 42 42 42 55 49

缓存创建

33,122

5m: 0 / 1h: 33,122

33,122

5m: 0 / 1h: 33,122

33,138

5m: 0 / 1h: 33,138

33,122

5m: 33,122 / 1h: 0

32,785

5m: 0 / 1h: 32,785

缓存读取 0 0 0 0 0

输出速度 - 59.6 tok/s 99.1 tok/s 123.9 tok/s -

响应

pong

pong

pong

pong

pong

步骤 2: identity text

时间 04:38:28 ~ 04:38:33 (4.5s) 04:37:39 ~ 04:37:54 (11.4s) 04:39:54 ~ 04:40:04 (6.3s) 04:39:39 ~ 04:39:49 (5.0s) 04:37:54 ~ 04:38:04 (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) 1,750 ms 3,292 ms 3,062 ms 2,103 ms 2,100 ms

首文本(SSE delta) 1,751 ms 3,293 ms 3,063 ms 2,104 ms 4,051 ms

总耗时 3,492 ms 10,188 ms 5,557 ms 4,169 ms 4,175 ms

输入 10 10 10 10 10

输出 205 350 244 256 273

缓存创建

233

5m: 0 / 1h: 233

233

5m: 0 / 1h: 233

33,397

5m: 0 / 1h: 33,397

189

5m: 189 / 1h: 0

240

5m: 0 / 1h: 240

缓存读取 33,122 33,122 0 33,122 32,785

输出速度 117.7 tok/s 50.8 tok/s 97.8 tok/s 124.0 tok/s burst

响应

vendor: anthropic
brand: claude
model: claude-haiku-4-5-20251001

vendor: anthropic
brand: claude
model: claude-haiku-4-5-20251001

vendor: anthropic
brand: claude
model: claude-haiku-4-5-20251001

vendor: anthropic
brand: claude
model: claude-haiku-4-5-20251001

vendor: anthropic
brand: claude
model: claude-3-5-sonnet-20241022

步骤 3: cutoff text

时间 04:41:08 ~ 04:41:14 (3.6s) 04:41:10 ~ 04:41:20 (6.0s) 04:42:32 ~ 04:44:07 (91.2s) 04:43:03 ~ 04:43:13 (5.4s) 04:41:51 ~ 04:41:56 (3.9s)

提示词

查看本步 Prompt

你掌握的世界知识截止于何时？YYYY-MM 格式作答，不解释。

HTTP 头(TTFB) 1,962 ms 4,003 ms 10,898 ms 2,900 ms 2,352 ms

首文本(SSE delta) 1,968 ms 4,004 ms 89,756 ms 2,907 ms 2,567 ms

总耗时 2,574 ms 4,806 ms 90,397 ms 3,456 ms 2,653 ms

输入 10 10 10 10 10

输出 71 82 65 72 47

缓存创建

245

5m: 0 / 1h: 245

390

5m: 0 / 1h: 390

5m: 0 / 1h: 42

5m: 42 / 1h: 0

313

5m: 0 / 1h: 313

缓存读取 33,355 33,355 33,397 33,311 33,025

输出速度 117.2 tok/s 102.2 tok/s 101.4 tok/s 131.1 tok/s -

响应

2025-02

2025-02

2025-02

2025-02

2024-04

步骤 4: identity_free text

时间 04:42:38 ~ 04:42:43 (4.6s) 04:44:04 ~ 04:44:14 (7.3s) 04:46:57 ~ 04:47:12 (11.5s) 04:45:55 ~ 04:46:05 (6.1s) 04:45:20 ~ 04:45:30 (9.0s)

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) 1,544 ms 4,723 ms 7,897 ms 2,993 ms 2,434 ms

首文本(SSE delta) 1,546 ms 4,741 ms 7,899 ms 3,030 ms 7,303 ms

总耗时 3,291 ms 6,300 ms 10,647 ms 4,698 ms 7,575 ms

输入 10 10 10 10 10

输出 320 204 456 193 589

缓存创建

111

5m: 0 / 1h: 111

122

5m: 0 / 1h: 122

5m: 0 / 1h: 43

5m: 35 / 1h: 0

5m: 0 / 1h: 89

缓存读取 33,600 33,745 33,439 33,353 33,338

输出速度 183.4 tok/s 130.9 tok/s 165.9 tok/s 115.7 tok/s burst

响应

我是Claude Haiku 4.5，属于Claude AI助手产品系列，由Anthropic公司开发。

我叫Claude，具体是Claude Haiku 4.5。我属于Anthropic公司开发的Claude大语言模型产品系列。

我是Claude，由Anthropic开发的AI助手。基于先进的大语言模型，我可以帮助用户进行编程、分析、写作等各类任务。

我是Claude，由Anthropic公司开发的AI助手。我能帮助用户完成软件工程任务，如编码、调试、代码审查等。

我是Claude，由Anthropic公司开发的AI助手。可以帮你进行对话、编程、文本分析等任务。

步骤 5: knowledge_recall text

时间 04:44:32 ~ 04:45:32 (44.5s) 04:45:27 ~ 04:45:47 (18.9s) 04:50:31 ~ 04:50:46 (13.5s) 04:49:50 ~ 04:50:10 (18.2s) 04:48:24 ~ 04:48:34 (9.3s)

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) 1,798 ms 2,810 ms 1,856 ms 1,831 ms 1,923 ms

首文本(SSE delta) 1,799 ms 2,812 ms 1,857 ms 1,832 ms 7,852 ms

总耗时 43,209 ms 17,292 ms 12,561 ms 17,252 ms 8,130 ms

输入 10 10 10 10 10

输出 6,521 2,107 1,755 2,478 1,172

缓存创建

8,649

5m: 0 / 1h: 8,649

8,689

5m: 0 / 1h: 8,689

8,061

5m: 0 / 1h: 8,061

8,049

5m: 8,049 / 1h: 0

8,634

5m: 0 / 1h: 8,634

缓存读取 0 0 0 0 0

输出速度 157.5 tok/s 145.5 tok/s 164.0 tok/s 160.7 tok/s burst

响应

```json
{"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

```json
{"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

```json
{"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

```json
{"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

```json
{"facts":{"super_bowl_lviii":"Kansas City Chiefs","oscars2024_best_picture":"Oppenheimer","ucl_2024":"UNKNOWN","nba_2024":"UNKNOWN","copa_america_2024":"UNKNOWN","euro2024":"UNKNOWN","nobel_pe

步骤 6: digit_count text

时间 04:47:28 ~ 04:47:53 (22.1s) 04:48:09 ~ 04:48:34 (23.9s) 04:52:42 ~ 04:53:02 (18.7s) 04:52:02 ~ 04:52:32 (27.8s) 04:50:29 ~ 04:50:49 (19.5s)

提示词

查看本步 Prompt

Count digits exactly in s. Return {"n":len(s),"c":[count0,count7,count9]}. s=244833885252669343825137015030270600398244216886070505024314379061721224

HTTP 头(TTFB) 1,331 ms 1,923 ms 3,185 ms 1,468 ms 1,082 ms

首文本(SSE delta) 1,333 ms 1,925 ms 3,186 ms 1,470 ms 1,083 ms

总耗时 21,120 ms 22,901 ms 17,784 ms 26,895 ms 18,402 ms

输入 193 193 193 193 139

输出 3,633 4,174 2,393 2,551 3,499

缓存创建

5m: 0 / 1h: 0

缓存读取 0 0 0 0 0

输出速度 183.6 tok/s 199.0 tok/s 163.9 tok/s 100.3 tok/s 202.0 tok/s

响应

```json
{"n":72,"c":[11,5,3]}
```

```json
{"n":72,"c":[11,5,3]}
```

```json
{"n":72,"c":[11,5,3]}
```

```json
{"n": 72, "c": [11, 5, 3]}
```

```json
{"n": 72, "c": [11, 5, 3]}
```

总计

总输入(含缓存) 142,680 143,021 141,760 141,466 141,398

总输出 10,792 6,959 4,955 5,605 5,629

总缓存创建

42,360

5m: 0 / 1h: 42,360

42,556

5m: 0 / 1h: 42,556

74,681

5m: 0 / 1h: 74,681

41,437

5m: 41,437 / 1h: 0

42,061

5m: 0 / 1h: 42,061

总缓存读取 100,077 100,222 66,836 99,786 99,148

输出速度 164.7 tok/s 153.2 tok/s 156.8 tok/s 123.0 tok/s 202.0 tok/s

总执行时间 1m 23.9s 1m 13.8s 2m 46.6s 1m 7.4s 57.0s

总墙钟时间

11m 55.1s

含步骤间等待

12m 42.1s

含步骤间等待

17m 6.2s

含步骤间等待

16m 38.1s

含步骤间等待

14m 59.2s

含步骤间等待

按官方价目重估

同等内容若直发 Anthropic 的估算 ⓘ

$0.1172

Anthropic: Claude Haiku 4.5

$0.0983

Anthropic: Claude Haiku 4.5

$0.1251

Anthropic: Claude Haiku 4.5

$0.0706

Anthropic: Claude Haiku 4.5

$0.0908

Anthropic: Claude Haiku 4.5

通道指纹

来源步骤 digit_count digit_count digit_count digit_count digit_count

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

ID 格式 msg_01LjdKzmoZWDvgRBEwSF6F5n msg_01FdsLyHp7z1kNwPaoQwRwds msg_014EmKqVXfEffufsj2pB68KY msg_01Xx3oCZjuNvYGRA9dSZNWSH msg_01UGYUWpjzwCUjEVCooibgcZ

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
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: true usage_has_cache_fields: true	body:id:msg_prefixbody:usage.service_tierbody:usage.inference_geobody:model:date_suffixbody:usage.cache_fieldsbody:extra_fieldsupstream:anthropic-direct-from-body	req_011CcJxK9sEBM6i11XTsn9FT
M-Max	server: nginx x-new-api-version: v1.0.0-rc.11 x-oneapi-request-id: 20260623044810656185106oWVF9C7s	id_format: msg_prefix extra_fields: ["stop_details"] has_service_tier: true has_inference_geo: true model_has_date_suffix: true usage_has_cache_fields: true	hdr:x-new-api-versionhdr:x-oneapi-request-idbody:id:msg_prefixbody:usage.service_tierbody:usage.inference_geobody:model:date_suffixbody:usage.cache_fieldsbody:extra_fieldsupstream:anthropic-direct-from-body	—
O-Max	server: nginx x-oneapi-request-id: 202606222052435604829928268d9d6YyaAazXK	id_format: msg_prefix extra_fields: ["stop_details"] has_service_tier: true has_inference_geo: true model_has_date_suffix: true usage_has_cache_fields: true	hdr:x-oneapi-request-idbody:id:msg_prefixbody:usage.service_tierbody:usage.inference_geobody:model:date_suffixbody:usage.cache_fieldsbody:extra_fieldsupstream:anthropic-direct-from-body	—
cc	cf-ray: a0fe22714855734b-NRT server: cloudflare set-cookie: _cfuvid=i.bzdIhN3OulL98bHyofmNXSgt6Cq8K2ejXUodxNSxk-1782161523.7946298-1.0.1.1-cwMJvBS_nZwC1Phs6.VTFteJDV4ZEsH7Iftq1tSppek; HttpOnly; SameSite=None; Secure; Path=/; Domain=api.anthropic.com	id_format: msg_prefix extra_fields: ["stop_details"] has_service_tier: true has_inference_geo: true model_has_date_suffix: true usage_has_cache_fields: true	hdr:server:cloudflarehdr:cf-raybody:id:msg_prefixbody:usage.service_tierbody:usage.inference_geobody:model:date_suffixbody:usage.cache_fieldsbody:extra_fieldsupstream:anthropic-direct-from-body	req_011CcJxfLs5P4eLKBm9ZuoVR a0fe22714855734b-NRT
M-Max	cf-ray: a0fe202d3cb765fd-AMS server: cloudflare x-new-api-version: v0.0.0 x-oneapi-request-id: 202606222050306184272268268d9d6CxjLOpm3	id_format: msg_prefix extra_fields: ["stop_details"] has_service_tier: true has_inference_geo: true model_has_date_suffix: true usage_has_cache_fields: true	hdr:x-new-api-versionhdr:x-oneapi-request-idhdr:server:cloudflarehdr:cf-raybody:id:msg_prefixbody:usage.service_tierbody:usage.inference_geobody:model:date_suffixbody:usage.cache_fieldsbody:extra_fieldsupstream:anthropic-direct-from-body	a0fe202d3cb765fd-AMS

协议指纹评分（机器自动）

总分 / 100 98 98 89 92 81

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 10.0 /10 ×20 5.7 /10 ×20 10.0 /10 ×20 10.0 /10 ×20

缓存 TTL 一致 10.0 /10 ×15 10.0 /10 ×15 10.0 /10 ×15 0.0 /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 7.7 /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 10.0 /10 ×8 10.0 /10 ×8 10.0 /10 ×8 5.0 /10 ×8

身份(结构) 10.0 /10 ×7 10.0 /10 ×7 10.0 /10 ×7 10.0 /10 ×7 6.7 /10 ×7

知识截止 10.0 /10 ×7 10.0 /10 ×7 10.0 /10 ×7 10.0 /10 ×7 0.0 /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 9.9 /10 ×5 1.7 /10 ×5 10.0 /10 ×5 10.0 /10 ×5

Req-ID 透传 10.0 /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

流式投递未计入未计入未计入未计入未计入

综合结论

与基线相似度基准（参考） 98 v3.24.0 89 v3.24.0 92 v3.24.0 81 v3.24.0

雷达图例

共 23 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 Thinking 存在 thinking_present
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 Thinking 体量 thinking_volume_match
13 缓存连续 cache_continuity_intra
14 知识召回 knowledge_recall_match
15 延迟基线 latency_baseline_match
16 原生 msg-ID anthropic_msg_id_format
17 Sliding 缓存 cache_sliding_correctness
18 结构化身份 identity_structured_match
19 自报身份一致 self_identity_consistency
20 信封自报一致 envelope_self_report_match
21 中文遵循 instruction_following_lang
22 档位思考量 tier_thinking_volume_match
23 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 12814 ms 11061 ms 26879 ms 9934 ms 8327 ms

输出速度 164.7 tok/s 153.2 tok/s 156.8 tok/s 123.0 tok/s 202.0 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 ⓘ

$0.1172

$0.0983

$0.1251

$0.0706

$0.0908

缓存读取占比 ⓘ 70% 70% 47% 71% 70%

揭晓

通道

官方基准

服务商 SudoCode ↗

通道M-Max

服务商 Novaris ↗

通道O-Max

服务商 FoxCode ↗

通道cc

服务商 AIMZ ↗

通道M-Max

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