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

2026/06/17 运行日期

claude-haiku-4-5-20251001 官方基准

3 通道分数 < 70 与基线相似度

已揭晓揭晓进度

步骤 / 指标

FoxCode

R-ultra

官方基准

PackyCode

cc-Zeus

AnyAIGC

M-

DawAPI

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 14:26:46 ~ 14:26:51 (4.2s) 14:26:52 ~ 14:26:57 (3.4s) 14:26:48 ~ 14:26:53 (3.3s) 14:26:50 ~ 14:27:00 (9.6s) 14:26:46 ~ 14:27:01 (10.3s)

提示词

查看本步 Prompt

ping. Reply with the single word: pong

HTTP 头(TTFB) 2,423 ms 1,846 ms 2,083 ms 3,670 ms 8,299 ms

首文本(SSE delta) 2,424 ms 1,849 ms 2,084 ms 3,673 ms 8,852 ms

总耗时 2,940 ms 2,229 ms 2,090 ms 4,603 ms 8,898 ms

输入 33,020 10 10 8 10

输出 55 45 42 2 53

缓存创建

5m: 0 / 1h: 0

33,122

5m: 0 / 1h: 33,122

33,122

5m: 0 / 1h: 33,122

7,322

5m: 0 / 1h: 7,322

30,483

5m: 0 / 1h: 30,483

缓存读取 0 0 0 29,388 0

输出速度 106.6 tok/s 118.4 tok/s burst - burst

响应

pong

pong

pong

pong

pong

步骤 2: identity text

时间 14:30:34 ~ 14:30:44 (5.0s) 14:28:22 ~ 14:28:27 (4.3s) 14:30:39 ~ 14:30:49 (6.7s) 14:28:37 ~ 14:28:47 (5.2s) 14:30:19 ~ 14:30:29 (8.8s)

提示词

查看本步 Prompt

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。严格按以下 3 行格式输出，全部小写英文，不要任何其他文字、解释、标点：
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) 2,128 ms 1,476 ms 1,401 ms 3,206 ms 5,742 ms

首文本(SSE delta) 2,129 ms 1,478 ms 1,402 ms 3,208 ms 6,784 ms

总耗时 4,227 ms 3,248 ms 4,600 ms 3,216 ms 7,908 ms

输入 33,584 10 10 8 10

输出 253 212 344 22 241

缓存创建

5m: 0 / 1h: 0

238

5m: 0 / 1h: 238

235

5m: 0 / 1h: 235

7,147

5m: 0 / 1h: 7,147

191

5m: 0 / 1h: 191

缓存读取 0 33,122 33,122 28,659 30,483

输出速度 120.6 tok/s 119.8 tok/s 107.6 tok/s - 214.4 tok/s

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-haiku-4-5-20251001

步骤 3: cutoff text

时间 14:34:36 ~ 14:34:41 (3.8s) 14:31:32 ~ 14:31:37 (3.5s) 14:33:46 ~ 14:33:51 (3.8s) 14:32:18 ~ 14:32:23 (4.5s) 14:32:43 ~ 14:32:58 (10.0s)

提示词

查看本步 Prompt

Knowledge cutoff date? Reply with YYYY-MM only.

HTTP 头(TTFB) 2,119 ms 1,646 ms 1,631 ms 3,217 ms 7,849 ms

首文本(SSE delta) 2,120 ms 1,647 ms 1,632 ms 3,219 ms 8,434 ms

总耗时 2,993 ms 2,471 ms 2,756 ms 3,219 ms 9,252 ms

输入 34,154 10 10 8 10

输出 58 78 73 4 108

缓存创建

5m: 0 / 1h: 0

254

5m: 0 / 1h: 254

386

5m: 0 / 1h: 386

7,060

5m: 0 / 1h: 7,060

5m: 0 / 1h: 61

缓存读取 0 33,360 33,357 28,632 30,674

输出速度 66.4 tok/s 94.7 tok/s 64.9 tok/s - 132.0 tok/s

响应

2025-02

2025-02

2025-02

2025-02

2025-02

步骤 4: identity_free text

时间 14:38:19 ~ 14:38:29 (8.0s) 14:35:11 ~ 14:35:26 (11.6s) 14:36:57 ~ 14:37:07 (8.3s) 14:34:12 ~ 14:34:23 (5.2s) 14:34:58 ~ 14:35:08 (8.6s)

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) 2,102 ms 1,846 ms 1,427 ms 3,222 ms 6,192 ms

首文本(SSE delta) 2,104 ms 1,849 ms 1,428 ms 3,696 ms 6,841 ms

总耗时 6,801 ms 10,540 ms 7,288 ms 3,699 ms 7,846 ms

输入 34,564 10 10 8 10

输出 890 1,351 995 55 220

缓存创建

5m: 0 / 1h: 0

116

5m: 0 / 1h: 116

115

5m: 0 / 1h: 115

7,021

5m: 0 / 1h: 7,021

5m: 0 / 1h: 43

缓存读取 0 33,614 33,743 28,704 30,735

输出速度 189.5 tok/s 155.4 tok/s 169.8 tok/s burst 218.9 tok/s

响应

我是Claude，由Anthropic公司开发的AI助手。属于Claude产品系列，基于Haiku 4.5模型。可协助编程、分析、写作等工作。

我是Claude Haiku 4.5，Anthropic公司打造。Claude系列中的轻量AI助手，专长编程和文本分析。

我是Claude Code，由Anthropic开发的AI助手，帮助用户进行软件工程相关的编程任务。

我是Claude，由Anthropic公司开发的AI助手。我是Claude API的一部分，可以通过多种方式访问，包括网页版、CLI工具和IDE扩展。

我是Claude Code，由Anthropic开发的AI代码助手。我可以帮助用户进行软件工程任务，包括编写代码、调试、代码审查和技术文档等工作。

步骤 5: knowledge_recall text

时间 14:42:04 ~ 14:42:19 (12.5s) 14:37:18 ~ 14:37:43 (22.5s) 14:38:41 ~ 14:38:56 (14.2s) 14:37:05 ~ 14:37:15 (5.2s) 14:36:18 ~ 14:36:33 (12.7s)

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) 1,495 ms 1,492 ms 1,249 ms 3,587 ms 3,294 ms

首文本(SSE delta) 1,496 ms 1,494 ms 1,250 ms 3,590 ms 3,919 ms

总耗时 11,526 ms 21,538 ms 12,964 ms 3,607 ms 11,908 ms

输入 10,786 10 10 8 10

输出 1,900 3,112 1,819 175 1,314

缓存创建

5m: 0 / 1h: 0

9,699

5m: 0 / 1h: 9,699

9,471

5m: 0 / 1h: 9,471

2,022

5m: 0 / 1h: 2,022

16,186

5m: 0 / 1h: 16,186

缓存读取 0 0 0 8,484 0

输出速度 189.4 tok/s 155.3 tok/s 155.3 tok/s burst 164.5 tok/s

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

{"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

```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

步骤 6: digit_count text

时间 14:45:04 ~ 14:45:14 (8.7s) 14:39:16 ~ 14:39:31 (13.5s) 14:41:43 ~ 14:42:04 (17.0s) 14:40:30 ~ 14:40:35 (4.5s) 14:39:12 ~ 14:39:52 (40.0s)

提示词

查看本步 Prompt

Count digits exactly in s. Return {"n":len(s),"c":[count0,count7,count9]}. s=880044839053584409309111277622355613933217286596013662368629347149236429

HTTP 头(TTFB) 1,084 ms 1,004 ms 1,152 ms 1,493 ms 8,869 ms

首文本(SSE delta) 1,085 ms 1,006 ms 1,153 ms 1,494 ms 10,017 ms

总耗时 7,854 ms 12,006 ms 15,087 ms 1,506 ms 38,797 ms

输入 210 193 193 8 10

输出 1,434 2,059 2,545 22 2,704

缓存创建

5m: 0 / 1h: 0

5m: 0 / 1h: 89

1,611

5m: 0 / 1h: 1,611

缓存读取 0 0 0 370 12,363

输出速度 211.8 tok/s 187.2 tok/s 182.6 tok/s - 94.0 tok/s

响应

```json
{"n":72,"c":[6,4,8]}
```

```json
{"n":72,"c":[6,4,8]}
```

```json
{"n":72,"c":[6,4,8]}
```

```json
{"n":72,"c":[12,8,8]}
```

```json
{"n":72,"c":[6,4,8]}
```

**Breakdown:**
- **n** (length): 72 characters
- **c[0]** (count of 0): 6 occurrences
- **c[1]** (count of 7): 4 occurrences  
- **c[2]** (count of 9): 8 occurrences

总计

总输入(含缓存) 146,318 143,768 143,794 154,946 152,890

总输出 4,590 6,857 5,818 280 4,640

总缓存创建

5m: 0 / 1h: 0

43,429

5m: 0 / 1h: 43,429

43,329

5m: 0 / 1h: 43,329

30,661

5m: 0 / 1h: 30,661

48,575

5m: 0 / 1h: 48,575

总缓存读取 0 100,096 100,222 124,237 104,255

输出速度 183.7 tok/s 160.6 tok/s 161.2 tok/s burst 115.5 tok/s

总执行时间 42.3s 58.7s 53.3s 34.2s 1m 30.4s

总墙钟时间

18m 28.5s

含步骤间等待

12m 39.1s

含步骤间等待

15m 15.9s

含步骤间等待

13m 45.3s

含步骤间等待

13m 6.5s

含步骤间等待

按官方价目重估

同等内容若直发 Anthropic 的估算 ⓘ

$0.1693

Anthropic: Claude Haiku 4.5

$0.0988

Anthropic: Claude Haiku 4.5

$0.0935

Anthropic: Claude Haiku 4.5

$0.0522

Anthropic: Claude Haiku 4.5

$0.0944

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 one-api (100%) direct (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_01qJMyNTSCA0raHGp3y0lLMg msg_01GTcxMUfjy9FisMAKQsBQEZ msg_01Tsi1uGU1imtDhvE56QRcyS msg_cLek2Q6A5Plt2Rk1v19uafpw msg_01U2d4NeVJhwfzbSyBubADNM

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
R-ultra	cf-ray: a0d016e5cd7ae393-NRT server: cloudflare x-new-api-version: v0.0.0 x-oneapi-request-id: 202606170645056575537968268d9d6w1WB5oAy	id_format: msg_prefix 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_fieldsupstream:anthropic-direct-from-body	a0d016e5cd7ae393-NRT
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_011Cc8Nb69hCPvQjrjqgmdUn
cc-Zeus	via: 1.1 Caddy cf-ray: a0d012061f9d9dea-LAX server: cloudflare x-oneapi-request-id: 20260617144145957267680eiywlU6Y	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: true usage_has_cache_fields: true	hdr:x-oneapi-request-idhdr:server:cloudflarehdr:cf-rayhdr:viabody:id:msg_prefixbody:usage.service_tierbody:usage.inference_geobody:model:date_suffixbody:usage.cache_fieldsbody:extra_fieldsupstream:anthropic-direct-from-body	a0d012061f9d9dea-LAX
M-	server: nginx	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	—
cc	server: cdn x-new-api-version: v1.0.0-rc.11 x-oneapi-request-id: 20260617063914161270218268d9d6Pn52dKOs	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	—

协议指纹评分（机器自动）

总分 / 100 69 97 95 78 82

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 0.0 /10 ×20 7.2 /10 ×20 7.2 /10 ×20 10.0 /10 ×20 6.0 /10 ×20

缓存 TTL 一致 0.0 /10 ×15 10.0 /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 5.0 /10 ×14 10.0 /10 ×14 10.0 /10 ×14 10.0 /10 ×14 10.0 /10 ×14

sliding 5m 5.0 /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 0.0 /10 ×8 10.0 /10 ×8

系统提示纯净 10.0 /10 ×8 10.0 /10 ×8 10.0 /10 ×8 0.0 /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 10.0 /10 ×5 10.0 /10 ×5 9.9 /10 ×5 0.5 /10 ×5

流式投递 10.0 /10 ×5 10.0 /10 ×5 10.0 /10 ×5 未计入 6.2 /10 ×5

Req-ID 透传 0.0 /10 ×4 10.0 /10 ×4 0.0 /10 ×4 0.0 /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 69 v3.21.0 基准（参考） 95 v3.21.0 78 v3.21.0 82 v3.21.0

雷达图例

共 24 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 流式投递 buffer_dump_match
6 自由身份 identity_free_clean
7 停止原因 stop_reason_present
8 系统提示纯净 system_prompt_clean
9 Service Tier service_tier_present
10 缓存命中比 cache_hit_ratio_match
11 缓存 TTL 一致 cache_ttl_consistency
12 推理地区 inference_geo_present
13 Thinking 体量 thinking_volume_match
14 缓存连续 cache_continuity_intra
15 知识召回 knowledge_recall_match
16 延迟基线 latency_baseline_match
17 原生 msg-ID anthropic_msg_id_format
18 Sliding 缓存 cache_sliding_correctness
19 结构化身份 identity_structured_match
20 自报身份一致 self_identity_consistency
21 信封自报一致 envelope_self_report_match
22 中文遵循 instruction_following_lang
23 档位思考量 tier_thinking_volume_match
24 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 6057 ms 8672 ms 7464 ms 3308 ms 14102 ms

输出速度 183.7 tok/s 160.6 tok/s 161.2 tok/s burst 115.5 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 ⓘ

$0.1693

$0.0988

$0.0935

$0.0522

$0.0944

缓存读取占比 ⓘ 0% 70% 70% 80% 68%

揭晓

通道

服务商 FoxCode ↗

通道R-ultra

官方基准

服务商 PackyCode ↗

通道cc-Zeus

服务商 AnyAIGC ↗

通道M-

服务商 DawAPI ↗

通道cc

返回列表