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

2026/07/02 运行日期

claude-haiku-4-5-20251001 官方基准

2 通道分数 < 70 与基线相似度

已揭晓揭晓进度

步骤 / 指标

ClaudeAPI

O-Bdrk

sucui

O-Max

FoxCode

R-ultra

AIMZ

M-Max

官方基准

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 v2.1.195 claude-cli v2.1.195 claude-cli v2.1.195 claude-cli v2.1.195 claude-cli v2.1.195

偏差标红基于官方基准

步骤 1: ping text

时间 20:45:16 ~ 20:45:26 (6.5s) 20:45:10 ~ 20:45:20 (6.2s) 20:45:12 ~ 20:45:17 (3.8s) 20:45:14 ~ 20:45:24 (5.0s) 20:45:18 ~ 20:45:23 (4.5s)

提示词

查看本步 Prompt

ping. Reply with the single word: pong

HTTP 头(TTFB) 3,573 ms 3,496 ms 2,271 ms 2,725 ms 2,811 ms

首文本(SSE delta) 3,613 ms 3,498 ms 2,272 ms 2,777 ms 2,835 ms

总耗时 4,134 ms 4,946 ms 3,004 ms 2,942 ms 3,464 ms

输入 10 10 21,388 10 10

输出 45 54 63 48 57

缓存创建

5m: 0 / 1h: 0

6,166

5m: 0 / 1h: 6,166

5m: 0 / 1h: 0

5,759

5m: 0 / 1h: 5,759

5m: 0 / 1h: 0

缓存读取 21,545 15,379 0 15,418 21,545

输出速度 86.4 tok/s 37.3 tok/s 86.1 tok/s - 90.6 tok/s

响应

pong

pong

pong

pong

pong

步骤 2: identity text

时间 20:48:39 ~ 20:48:50 (6.9s) 20:49:02 ~ 20:49:13 (7.5s) 20:46:31 ~ 20:46:41 (6.1s) 20:48:42 ~ 20:49:03 (18.1s) 20:47:47 ~ 20:48:07 (7.2s)

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) 3,361 ms 2,513 ms 2,165 ms 7,837 ms 2,291 ms

首文本(SSE delta) 3,405 ms 2,514 ms 2,166 ms 17,048 ms 2,328 ms

总耗时 5,450 ms 5,469 ms 5,335 ms 17,070 ms 6,224 ms

输入 10 10 21,948 10 10

输出 164 269 385 888 373

缓存创建

236

5m: 0 / 1h: 236

245

5m: 0 / 1h: 245

5m: 0 / 1h: 0

239

5m: 0 / 1h: 239

248

5m: 0 / 1h: 248

缓存读取 21,545 21,545 0 21,177 21,545

输出速度 80.2 tok/s 91.0 tok/s 121.5 tok/s burst 95.7 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-3-5-sonnet-20241022

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

步骤 3: cutoff text

时间 20:52:33 ~ 20:57:43 (306.2s) 20:52:39 ~ 20:52:49 (5.6s) 20:49:56 ~ 20:50:01 (3.4s) 20:50:56 ~ 20:51:01 (4.4s) 20:50:20 ~ 20:50:25 (4.5s)

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) 303,986 ms 2,526 ms 1,772 ms 2,699 ms 2,710 ms

首文本(SSE delta) 304,737 ms 2,527 ms 1,773 ms 2,817 ms 2,711 ms

总耗时 304,910 ms 4,391 ms 2,699 ms 2,903 ms 3,435 ms

输入 10 10 22,711 10 10

输出 66 70 77 61 71

缓存创建

204

5m: 0 / 1h: 204

309

5m: 0 / 1h: 309

5m: 0 / 1h: 0

928

5m: 0 / 1h: 928

413

5m: 0 / 1h: 413

缓存读取 21,781 21,790 0 21,416 21,793

输出速度 - 37.6 tok/s 83.2 tok/s - 98.1 tok/s

响应

2025-02

2025-02

2025-02

2024-04

2025-02

步骤 4: identity_free text

时间 21:01:41 ~ 21:02:01 (17.0s) 20:56:14 ~ 20:56:24 (5.6s) 20:51:42 ~ 20:51:52 (5.5s) 20:54:08 ~ 20:54:18 (5.8s) 20:52:08 ~ 20:52:18 (6.7s)

提示词

查看本步 Prompt

中文做个自我介绍：你是什么产品或助手？谁开发的？50 字内。

HTTP 头(TTFB) 7,555 ms 2,556 ms 2,572 ms 2,255 ms 2,465 ms

首文本(SSE delta) 8,158 ms 2,561 ms 2,573 ms 4,213 ms 2,466 ms

总耗时 15,756 ms 4,538 ms 4,632 ms 4,520 ms 5,673 ms

输入 10 10 23,139 10 10

输出 635 150 404 389 381

缓存创建

108

5m: 0 / 1h: 108

108

5m: 0 / 1h: 108

5m: 0 / 1h: 0

103

5m: 0 / 1h: 103

113

5m: 0 / 1h: 113

缓存读取 21,985 22,099 0 22,344 22,206

输出速度 83.6 tok/s 75.9 tok/s 196.2 tok/s burst 118.8 tok/s

响应

我是Claude Code，由Anthropic开发的AI编程助手，可帮助进行代码编写、调试、重构和其他软件工程任务。

我是Claude Haiku 4.5，Anthropic公司开发的大型语言模型。属于Claude系列的轻量级版本，设计用于快速、高效的对话和编程辅助任务。

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

我是Claude，由Anthropic公司开发的AI助手。我可以帮助用户完成编程、分析、写作等各种任务。我致力于提供有帮助、无害和诚实的回应。

我是Claude Code，由Anthropic公司开发的AI编程助手。基于Claude语言模型，我可以帮助用户编写、调试、重构代码，解决软件工程问题。

步骤 5: knowledge_recall text

时间 21:03:47 ~ 21:04:37 (49.9s) 21:00:14 ~ 21:00:34 (18.4s) 20:54:01 ~ 20:54:21 (17.6s) 20:57:38 ~ 20:57:53 (11.4s) 20:54:23 ~ 20:54:38 (12.2s)

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) 3,048 ms 2,471 ms 1,744 ms 2,470 ms 1,509 ms

首文本(SSE delta) 3,083 ms 2,472 ms 1,745 ms 9,397 ms 1,510 ms

总耗时 48,585 ms 17,220 ms 16,383 ms 10,009 ms 11,206 ms

输入 10 10 11,271 10 10

输出 4,754 2,740 2,785 1,306 1,317

缓存创建

3,874

5m: 0 / 1h: 3,874

9,308

5m: 0 / 1h: 9,308

5m: 0 / 1h: 0

9,787

5m: 0 / 1h: 9,787

3,846

5m: 0 / 1h: 3,846

缓存读取 5,805 0 0 0 5,805

输出速度 104.5 tok/s 185.8 tok/s 190.3 tok/s burst 135.8 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

```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

步骤 6: digit_count text

时间 21:07:50 ~ 21:09:15 (82.5s) 21:03:32 ~ 21:04:42 (68.1s) 20:56:23 ~ 20:56:38 (10.1s) 20:58:56 ~ 20:59:21 (20.6s) 20:58:05 ~ 20:58:30 (20.6s)

提示词

查看本步 Prompt

Count digits exactly in s. Return {"n":len(s),"c":[count0,count7,count9]}. s=603343804218255802944026845952568322682582098981663313525912684366003215

HTTP 头(TTFB) 53,433 ms 1,832 ms 1,209 ms 2,410 ms 2,162 ms

首文本(SSE delta) 53,687 ms 1,833 ms 1,210 ms 2,414 ms 2,163 ms

总耗时 81,167 ms 66,073 ms 9,348 ms 18,691 ms 18,678 ms

输入 193 193 210 139 193

输出 5,222 8,153 1,841 3,019 1,931

缓存创建

5m: 0 / 1h: 0

缓存读取 0 0 0 0 0

输出速度 190.0 tok/s 126.9 tok/s 226.2 tok/s 185.5 tok/s 116.9 tok/s

响应

```json
{"n": 72, "c": [7, 0, 5]}
```

```json
{"n": 72, "c": [7, 0, 5]}
```

```json
{"n":72,"c":[7,0,5]}
```

```json
{"n":72,"c":[7,0,5]}
```

```json
{"n": 72, "c": [7, 0, 5]}
```

总计

总输入(含缓存) 97,326 97,192 100,667 97,360 97,757

总输出 10,886 11,436 5,555 5,711 4,130

总缓存创建

4,422

5m: 0 / 1h: 4,422

16,136

5m: 0 / 1h: 16,136

5m: 0 / 1h: 0

16,816

5m: 0 / 1h: 16,816

4,620

5m: 0 / 1h: 4,620

总缓存读取 92,661 80,813 0 80,355 92,894

输出速度 130.1 tok/s 131.1 tok/s 187.3 tok/s 185.5 tok/s 119.1 tok/s

总执行时间 7m 49.1s 1m 51.4s 46.6s 1m 5.3s 55.7s

总墙钟时间

23m 58.7s

含步骤间等待

19m 32.2s

含步骤间等待

11m 26.1s

含步骤间等待

14m 7.2s

含步骤间等待

13m 12.1s

含步骤间等待

按官方价目重估

同等内容若直发 Anthropic 的估算 ⓘ

$0.0695

Anthropic: Claude Haiku 4.5

$0.0857

Anthropic: Claude Haiku 4.5

$0.1284

Anthropic: Claude Haiku 4.5

$0.0578

Anthropic: Claude Haiku 4.5

$0.0360

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

ID 格式 msg_01LaCq2p2hckrULzQKMw66cV msg_01X5sr17sc4xN4U4N4G64KUc msg_01nC4wqjyGUXuKjCu8VRINhi msg_014Lqw2bNZqy7A5YWz8CrsMg msg_01EJ38JDY2Q1eUJHmQEF835u

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-Bdrk	x-new-api-version: v1.0.0-rc.11 x-oneapi-request-id: 202607021307517257726088268d9d6i3PH6ABP	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	via: 1.1 Caddy cf-ray: a14dd9f34b19c244-FRA server: cloudflare x-new-api-version: v0.12.10 x-oneapi-request-id: 20260702130335171023258268d9d6KpOQJSCo	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-new-api-versionhdr: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	a14dd9f34b19c244-FRA
R-ultra	cf-ray: a14dcf719a2ae3bf-NRT server: cloudflare x-new-api-version: v0.0.0 x-oneapi-request-id: 202607021256247962450538268d9d6oQdcANYm	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	a14dcf719a2ae3bf-NRT
M-Max	cf-ray: a14dd335688e34f3-AMS server: cloudflare x-new-api-version: v0.0.0 x-oneapi-request-id: 202607021258588055901608268d9d6FFVp5z9S	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	a14dd335688e34f3-AMS
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_011CcdGcRA8XvGDZ7HmdBwdP

协议指纹评分（机器自动）

总分 / 100 95 95 69 84 100

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 0.0 /10 ×20 10.0 /10 ×20 10.0 /10 ×20

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

延迟基线 5.4 /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 10.0 /10 ×14 10.0 /10 ×14 5.0 /10 ×14 10.0 /10 ×14 10.0 /10 ×14

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

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

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

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

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

Req-ID 透传 0.0 /10 ×2 0.0 /10 ×2 0.0 /10 ×2 0.0 /10 ×2 10.0 /10 ×2

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 95 v3.26.0 95 v3.26.0 69 v3.26.0 84 v3.26.0 基准（参考）

雷达图例

共 22 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 档位思考量 tier_thinking_volume_match
22 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 76667 ms 17106 ms 6900 ms 9356 ms 8113 ms

输出速度 130.1 tok/s 131.1 tok/s 187.3 tok/s 185.5 tok/s 119.1 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 ⓘ

$0.0695

$0.0857

$0.1284

$0.0578

$0.0360

缓存读取占比 ⓘ 95% 83% 0% 83% 95%

揭晓

通道

服务商 ClaudeAPI ↗

通道O-Bdrk

服务商 sucui ↗

通道O-Max

服务商 FoxCode ↗

通道R-ultra

服务商 AIMZ ↗

通道M-Max

官方基准

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