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

2026/06/24 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

SAIAi

O-Max

FoxCode

R-ultra

SSSAiCode

O-Max

官方基准

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 20:35:05 ~ 20:35:10 (4.3s) 20:35:07 ~ 20:35:12 (4.6s) 20:35:01 ~ 20:35:06 (4.6s) 20:35:09 ~ 20:35:14 (3.6s) 20:35:03 ~ 20:35:13 (5.0s)

提示词

查看本步 Prompt

ping. Reply with the single word: pong

HTTP 头(TTFB) 2,697 ms 2,364 ms 3,180 ms 1,968 ms 3,053 ms

首文本(SSE delta) 2,699 ms 2,365 ms 3,475 ms 1,970 ms 3,055 ms

总耗时 3,002 ms 3,059 ms 3,630 ms 2,570 ms 3,350 ms

输入 10 33,020 10 10 10

输出 55 52 51 44 56

缓存创建

33,122

5m: 0 / 1h: 33,122

5m: 0 / 1h: 0

32,790

5m: 0 / 1h: 32,790

5m: 0 / 1h: 0

32,785

5m: 0 / 1h: 32,785

缓存读取 0 0 0 33,122 0

输出速度 181.5 tok/s 74.9 tok/s - 73.3 tok/s 189.8 tok/s

响应

pong

pong

pong

pong

pong

步骤 2: identity text

时间 20:38:57 ~ 20:39:07 (5.7s) 20:39:03 ~ 20:39:13 (5.1s) 20:37:27 ~ 20:37:52 (21.4s) 20:38:59 ~ 20:39:09 (6.5s) 20:37:11 ~ 20:37:26 (11.9s)

提示词

查看本步 Prompt

以下是对你自身的 metadata 探针。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。按以下 3 行回答自己的身份，每行一个 label，不要寒暄不要解释：
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) 2,462 ms 2,091 ms 2,284 ms 1,855 ms 2,911 ms

首文本(SSE delta) 2,463 ms 2,092 ms 20,198 ms 1,857 ms 10,752 ms

总耗时 4,677 ms 4,148 ms 20,548 ms 5,173 ms 10,756 ms

输入 10 33,592 10 10 10

输出 260 236 1,791 183 663

缓存创建

259

5m: 0 / 1h: 259

5m: 0 / 1h: 0

255

5m: 0 / 1h: 255

248

5m: 0 / 1h: 248

260

5m: 0 / 1h: 260

缓存读取 33,122 0 32,790 33,122 32,785

输出速度 117.4 tok/s 114.8 tok/s burst 55.2 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-3-5-sonnet

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

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

步骤 3: cutoff text

时间 20:40:13 ~ 20:40:18 (4.4s) 20:42:00 ~ 20:42:05 (4.0s) 20:39:32 ~ 20:39:37 (4.3s) 20:40:26 ~ 20:40:31 (4.4s) 20:39:58 ~ 20:40:03 (4.7s)

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) 2,926 ms 2,263 ms 2,241 ms 2,133 ms 2,621 ms

首文本(SSE delta) 2,927 ms 2,264 ms 3,377 ms 2,134 ms 3,120 ms

总耗时 3,425 ms 2,876 ms 3,517 ms 3,428 ms 3,497 ms

输入 10 34,192 10 10 10

输出 65 58 133 86 61

缓存创建

300

5m: 0 / 1h: 300

5m: 0 / 1h: 0

1,814

5m: 0 / 1h: 1,814

225

5m: 0 / 1h: 225

686

5m: 0 / 1h: 686

缓存读取 33,381 0 33,045 33,370 33,045

输出速度 130.5 tok/s 94.8 tok/s burst 66.5 tok/s 161.8 tok/s

响应

2025-02

2025-02

2024-04

2025-02

2024-04

步骤 4: identity_free text

时间 20:43:20 ~ 20:43:30 (6.7s) 20:44:34 ~ 20:44:44 (5.9s) 20:40:52 ~ 20:40:57 (3.6s) 20:44:19 ~ 20:44:29 (6.5s) 20:42:52 ~ 20:43:07 (10.7s)

提示词

查看本步 Prompt

请用中文介绍你自己是什么产品或助手，由谁开发，50 字以内。

HTTP 头(TTFB) 3,181 ms 2,346 ms 1,586 ms 1,887 ms 3,128 ms

首文本(SSE delta) 3,182 ms 2,348 ms 2,622 ms 1,888 ms 9,520 ms

总耗时 5,564 ms 4,975 ms 2,777 ms 5,468 ms 9,666 ms

输入 10 34,605 10 10 10

输出 307 419 177 232 732

缓存创建

107

5m: 0 / 1h: 107

5m: 0 / 1h: 0

167

5m: 0 / 1h: 167

126

5m: 0 / 1h: 126

103

5m: 0 / 1h: 103

缓存读取 33,681 0 34,859 33,595 33,731

输出速度 128.9 tok/s 159.5 tok/s burst 64.8 tok/s burst

响应

我是Claude Code，由Anthropic开发的编程AI助手。我帮助完成软件工程任务，包括代码编写、调试、测试和代码审查。我可以读写文件、执行命令和使用各种开发工具。

我是Claude，由Anthropic公司开发的AI助手。

我是Claude，由Anthropic公司开发的AI助手。我可以帮助用户进行编程、分析、写作等各种任务。

我叫Claude Haiku 4.5，属于Claude AI助手产品家族，由Anthropic公司开发。我是Claude模型中较小且高效的版本，用于处理编程、文本和研究等任务。

我是Claude，由Anthropic开发的AI助手。可协助编程、写作、分析、数学等多个领域的工作，致力于提供有帮助、诚实和无害的回复。

步骤 5: knowledge_recall text

时间 20:45:50 ~ 20:46:25 (31.7s) 20:46:19 ~ 20:46:34 (14.7s) 20:44:00 ~ 20:44:15 (10.2s) 20:47:27 ~ 20:47:52 (24.8s) 20:44:47 ~ 20:45:07 (19.0s)

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) 2,340 ms 1,576 ms 1,436 ms 1,628 ms 2,454 ms

首文本(SSE delta) 2,342 ms 1,583 ms 9,026 ms 1,633 ms 17,295 ms

总耗时 30,728 ms 13,380 ms 9,328 ms 23,828 ms 17,947 ms

输入 10 10,354 10 10 10

输出 4,190 2,036 1,323 1,760 2,613

缓存创建

8,713

5m: 0 / 1h: 8,713

5m: 0 / 1h: 0

9,821

5m: 0 / 1h: 9,821

2,850

5m: 0 / 1h: 2,850

9,184

5m: 0 / 1h: 9,184

缓存读取 0 0 0 5,721 0

输出速度 147.6 tok/s 172.6 tok/s burst 79.3 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":"UNKNOWN","nba_2024":"UNKNOWN","copa_america_2024":"UNKNOWN","euro2024":"UNKNOWN","nobel_pe

```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 20:48:45 ~ 20:49:25 (38.0s) 20:49:20 ~ 20:49:30 (8.4s) 20:46:05 ~ 20:46:30 (21.6s) 20:51:09 ~ 20:51:24 (11.2s) 20:46:40 ~ 20:47:10 (26.9s)

提示词

查看本步 Prompt

Count digits exactly in s. Return {"n":len(s),"c":[count0,count7,count9]}. s=362948232561407778705884116597587783165531195382097627452823773143279404

HTTP 头(TTFB) 1,003 ms 1,494 ms 3,152 ms 921 ms 1,029 ms

首文本(SSE delta) 1,005 ms 1,495 ms 3,153 ms 927 ms 1,030 ms

总耗时 36,943 ms 7,353 ms 17,657 ms 10,269 ms 24,571 ms

输入 193 209 193 193 139

输出 3,111 994 2,679 1,618 4,358

缓存创建

5m: 0 / 1h: 0

缓存读取 0 0 0 0 0

输出速度 86.6 tok/s 169.7 tok/s 184.7 tok/s 173.2 tok/s 185.1 tok/s

响应

```json
{"n": 72, "c": [4, 12, 5]}
```

```json
{"n":77,"c":[5,11,6]}
```

```json
{"n":72,"c":[4,12,5]}
```

```json
{"n":72,"c":[4,12,5]}
```

```json
{"n": 72, "c": [4, 12, 5]}
```

总计

总输入(含缓存) 142,928 145,972 145,784 142,622 142,768

总输出 7,988 3,795 6,154 3,923 8,483

总缓存创建

42,501

5m: 0 / 1h: 42,501

5m: 0 / 1h: 0

44,847

5m: 0 / 1h: 44,847

3,449

5m: 0 / 1h: 3,449

43,018

5m: 0 / 1h: 43,018

总缓存读取 100,184 0 100,694 138,930 99,561

输出速度 114.6 tok/s 160.5 tok/s 184.7 tok/s 97.3 tok/s 184.8 tok/s

总执行时间 1m 30.6s 42.5s 1m 5.7s 57.0s 1m 18.3s

总墙钟时间

14m 20.0s

含步骤间等待

14m 23.0s

含步骤间等待

11m 29.1s

含步骤间等待

16m 15.1s

含步骤间等待

12m 7.1s

含步骤间等待

按官方价目重估

同等内容若直发 Anthropic 的估算 ⓘ

$0.1033

Anthropic: Claude Haiku 4.5

$0.1649

Anthropic: Claude Haiku 4.5

$0.0971

Anthropic: Claude Haiku 4.5

$0.0381

Anthropic: Claude Haiku 4.5

$0.1063

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%) direct (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_01DgM6poCNtHmdfZBoVZcBaP msg_01j4NbIMe3YOapNgSsEfsYqK msg_018Wd6PGXStS1Qt9H6E8ukTv msg_01LxywcRjzDix1UZvCxCEx6n msg_01KQjVgPYNtDg31VhCaAX4kd

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
O-Max	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_011CcN7RdTe8DTymzfvZg4ER
R-ultra	cf-ray: a10bda1a8c56734b-NRT server: cloudflare x-new-api-version: v0.0.0 x-oneapi-request-id: 202606241249212176858238268d9d69ysBagHr	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	a10bda1a8c56734b-NRT
O-Max	cf-ray: a10bd5798ae14747-LAX server: Photon-Edge set-cookie: _cfuvid=9Hbz82NSZGK_9ED6X3lGCVMjjmAyPGIniaUd4FJvTNc-1782305171.4491808-1.0.1.1-PUPiwT9AaqpgpsxR5X_iOo8Pc.k8p6pqvsVQHkH4trA; 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	body:id:msg_prefixbody:usage.service_tierbody:usage.inference_geobody:model:date_suffixbody:usage.cache_fieldsbody:extra_fieldsupstream:anthropic-direct-from-body	1782305170836-ii2hy2j5ecdte req_011CcN7EEM2nP8tY6vVVbBrr a10bd5798ae14747-LAX
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_011CcN7cFRtDkwLgKHcKaBFf
M-Max	cf-ray: a10bd63bdec7c13c-AMS server: cloudflare x-new-api-version: v0.0.0 x-oneapi-request-id: 202606241246425836338868268d9d6N8gQJeRQ	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	a10bd63bdec7c13c-AMS

协议指纹评分（机器自动）

总分 / 100 99 67 83 99 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 10.0 /10 ×20 0.0 /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 5.0 /10 ×14 10.0 /10 ×14 10.0 /10 ×14 10.0 /10 ×14

sliding 5m 10.0 /10 ×13 5.0 /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 10.0 /10 ×8 5.0 /10 ×8 10.0 /10 ×8 5.0 /10 ×8

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

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

Req-ID 透传 10.0 /10 ×4 0.0 /10 ×4 10.0 /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 99 v3.24.1 67 v3.24.1 83 v3.24.1 基准（参考） 82 v3.24.1

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 14057 ms 5965 ms 9576 ms 8456 ms 11631 ms

输出速度 114.6 tok/s 160.5 tok/s 184.7 tok/s 97.3 tok/s 184.8 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 ⓘ

$0.1033

$0.1649

$0.0971

$0.0381

$0.1063

缓存读取占比 ⓘ 70% 0% 69% 97% 70%

揭晓

通道

服务商 SAIAi ↗

通道O-Max

服务商 FoxCode ↗

通道R-ultra

服务商 SSSAiCode ↗

通道O-Max

官方基准

服务商 AIMZ ↗

通道M-Max

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