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

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

WorldBase.ai

M-

LinkAPI

O-Max

FoxCode

luminai

M-Kiro/Antigravity

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 06:30:36 ~ 06:30:41 (3.5s) 06:30:28 ~ 06:30:38 (5.3s) 06:30:32 ~ 06:30:42 (4.9s) 06:30:34 ~ 06:30:39 (4.3s) 06:30:30 ~ 06:30:40 (5.7s)

提示词

查看本步 Prompt

ping. Reply with the single word: pong

HTTP 头(TTFB) 2,026 ms 3,404 ms 2,702 ms 2,952 ms 4,338 ms

首文本(SSE delta) 2,028 ms 3,406 ms 2,704 ms 2,954 ms 4,411 ms

总耗时 2,428 ms 3,802 ms 3,547 ms 3,393 ms 4,457 ms

输入 10 0 10 10 1,443

输出 58 2 54 52 1

缓存创建

33,122

5m: 0 / 1h: 33,122

35,313

5m: 0 / 1h: 35,313

33,122

5m: 0 / 1h: 33,122

33,122

5m: 33,122 / 1h: 0

4,111

5m: 4,111 / 1h: 0

缓存读取 0 0 0 0 23,301

输出速度 145.0 tok/s - 64.1 tok/s 118.5 tok/s -

响应

pong


pong

pong

pong

pong

步骤 2: identity text

时间 06:31:43 ~ 06:31:48 (4.7s) 06:33:17 ~ 06:33:27 (6.2s) 06:32:48 ~ 06:32:58 (5.4s) 06:33:06 ~ 06:33:11 (4.9s) 06:34:32 ~ 06:34:42 (5.1s)

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) 1,799 ms 3,097 ms 2,594 ms 2,175 ms 3,583 ms

首文本(SSE delta) 1,800 ms 3,098 ms 2,595 ms 2,176 ms 3,584 ms

总耗时 3,753 ms 5,045 ms 4,257 ms 4,070 ms 3,869 ms

输入 10 232 10 10 1,453

输出 210 26 199 190 28

缓存创建

262

5m: 0 / 1h: 262

5m: 0 / 1h: 58

258

5m: 0 / 1h: 258

202

5m: 202 / 1h: 0

4,139

5m: 4,139 / 1h: 0

缓存读取 33,122 35,293 33,122 33,122 23,460

输出速度 107.5 tok/s - 119.7 tok/s 100.3 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 06:33:27 ~ 06:33:32 (3.5s) 06:34:46 ~ 06:34:51 (4.3s) 06:35:31 ~ 06:35:36 (4.6s) 06:34:41 ~ 06:34:46 (3.5s) 06:37:04 ~ 06:37:14 (5.0s)

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) 1,971 ms 2,643 ms 2,886 ms 2,081 ms 3,890 ms

首文本(SSE delta) 1,973 ms 2,644 ms 2,886 ms 2,082 ms 3,941 ms

总耗时 2,457 ms 3,287 ms 3,440 ms 2,675 ms 3,994 ms

输入 10 37 10 10 1,457

输出 71 5 84 60 4

缓存创建

252

5m: 0 / 1h: 252

499

5m: 0 / 1h: 499

241

5m: 0 / 1h: 241

5m: 42 / 1h: 0

4,149

5m: 4,149 / 1h: 0

缓存读取 33,384 35,325 33,380 33,324 23,515

输出速度 146.7 tok/s - 151.6 tok/s 101.2 tok/s -

响应

2025-02


2025-02

2025-02

2025-02

2025-02

步骤 4: identity_free text

时间 06:36:45 ~ 06:36:55 (5.1s) 06:38:26 ~ 06:38:36 (6.9s) 06:37:49 ~ 06:37:59 (5.1s) 06:38:34 ~ 06:38:49 (11.7s) 06:40:28 ~ 06:40:38 (5.5s)

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) 1,704 ms 2,955 ms 2,359 ms 1,966 ms 4,139 ms

首文本(SSE delta) 1,705 ms 2,956 ms 2,360 ms 1,967 ms 4,346 ms

总耗时 4,088 ms 5,796 ms 4,159 ms 10,598 ms 4,401 ms

输入 10 41 10 10 1,459

输出 437 31 290 1,176 32

缓存创建

113

5m: 0 / 1h: 113

143

5m: 0 / 1h: 143

124

5m: 0 / 1h: 124

5m: 43 / 1h: 0

4,155

5m: 4,155 / 1h: 0

缓存读取 33,636 35,816 33,621 33,366 23,550

输出速度 183.4 tok/s 10.9 tok/s 161.2 tok/s 136.3 tok/s -

响应

我是Claude，由Anthropic开发的AI助手。可以帮助用户完成软件工程任务，如编写代码、调试、重构等。


我是Anthropic公司开发的Claude AI助手，擅长编程、分析、写作等任务。

我是Claude Haiku 4.5，是由Anthropic公司开发的Claude AI助手产品家族的一员。

我是Claude Haiku 4.5，由Anthropic开发的AI助手，可帮助用户进行代码编程、文本分析、问题解答等任务。知识库截至2025年2月。

我叫Claude Haiku 4.5，属于Claude产品系列，由Anthropic公司开发。

步骤 5: knowledge_recall text

时间 06:38:09 ~ 06:38:29 (15.6s) 06:41:08 ~ 06:41:19 (9.6s) 06:40:20 ~ 06:40:45 (21.5s) 06:42:40 ~ 06:43:05 (20.6s) 06:44:38 ~ 06:44:43 (4.8s)

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) 1,497 ms 1,892 ms 2,739 ms 1,372 ms 2,610 ms

首文本(SSE delta) 1,498 ms 1,893 ms 2,740 ms 1,373 ms 2,611 ms

总耗时 14,599 ms 8,491 ms 20,577 ms 19,433 ms 3,698 ms

输入 10 1,001 10 10 375

输出 2,055 182 2,997 2,848 201

缓存创建

8,804

5m: 0 / 1h: 8,804

9,460

5m: 0 / 1h: 9,460

8,653

5m: 0 / 1h: 8,653

8,083

5m: 8,083 / 1h: 0

1,068

5m: 1,068 / 1h: 0

缓存读取 0 0 0 0 6,056

输出速度 156.9 tok/s 27.6 tok/s 168.0 tok/s 157.7 tok/s 184.9 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":"Spa

```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

步骤 6: digit_count text

时间 06:39:46 ~ 06:40:06 (15.0s) 06:44:43 ~ 06:44:58 (12.0s) 06:43:02 ~ 06:43:27 (20.8s) 06:45:36 ~ 06:46:01 (24.2s) 06:46:56 ~ 06:47:01 (3.1s)

提示词

查看本步 Prompt

Count digits exactly in s. Return {"n":len(s),"c":[count0,count7,count9]}. s=207921499379392511382649784323972008723042401064407224542472882389090454

HTTP 头(TTFB) 1,545 ms 1,573 ms 1,730 ms 1,224 ms 1,936 ms

首文本(SSE delta) 1,546 ms 1,574 ms 1,731 ms 1,226 ms 1,943 ms

总耗时 13,915 ms 10,662 ms 19,782 ms 23,412 ms 2,124 ms

输入 193 200 193 193 189

输出 2,030 18 3,733 4,648 19

缓存创建

5m: 0 / 1h: 0

缓存读取 0 0 0 0 -

输出速度 164.1 tok/s - 206.8 tok/s 209.5 tok/s -

响应

```json
{"n":72,"c":[9,7,9]}
```


```json
{"n":72,"c":[9,7,9]}
```

```json
{"n": 72, "c": [9, 7, 9]}
```

```json
{"n":72,"c":[9,7,9]}
```

```json
{"n":66,"c":[8,8,8]}
```

总计

总输入(含缓存) 142,938 153,418 142,764 141,547 123,880

总输出 4,861 264 7,357 8,974 285

总缓存创建

42,553

5m: 0 / 1h: 42,553

45,473

5m: 0 / 1h: 45,473

42,398

5m: 0 / 1h: 42,398

41,492

5m: 41,492 / 1h: 0

17,622

5m: 17,622 / 1h: 0

总缓存读取 100,142 106,434 100,123 99,812 99,882

输出速度 158.4 tok/s 22.6 tok/s 180.6 tok/s 173.2 tok/s 184.9 tok/s

总执行时间 47.3s 44.2s 1m 2.3s 1m 9.1s 29.2s

总墙钟时间

9m 30.0s

含步骤间等待

14m 29.3s

含步骤间等待

12m 54.8s

含步骤间等待

15m 27.1s

含步骤间等待

16m 30.9s

含步骤间等待

按官方价目重估

同等内容若直发 Anthropic 的估算 ⓘ

$0.0878

Anthropic: Claude Haiku 4.5

$0.0703

Anthropic: Claude Haiku 4.5

$0.1000

Anthropic: Claude Haiku 4.5

$0.0875

Anthropic: Claude Haiku 4.5

$0.0316

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%) 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 cloudflare

ID 格式 msg_01AfrQMRz9Z9okcrnPs54qQf msg_01Q5JWV25iEMTK4LUC9SUv1M msg_013Suja4MHKGHT4xYbMPNX63 msg_01GASBS5Yv3s6R9uKyVDRBX8 msg_01xpgdzmVVofMeXWx1ZUBYBAXf

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_011Cc7k2PJhj6LxaSxmz42fS
M-	cf-ray: a0cd57429f9b66e5-AMS server: cloudflare	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	b2b4040a-efe2-4ee8-9b5a-1ea7264aa716 a0cd57429f9b66e5-AMS
O-Max	server: nginx set-cookie: server_name_session=7d88fca8b1426ca76c58d5aeba6fb224; Max-Age=86400; httponly; path=/ x-new-api-version: v1.0.0-rc.11 x-oneapi-request-id: 202606162243035891700538268d9d62EzYocNQ	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	—
cc	cf-ray: a0cd588eaa48e3bf-NRT server: cloudflare set-cookie: _cfuvid=633q3NE0ycXt.2h8B3IVjUKrvbfCzB1zJcPeY5vEWt4-1781649938.097269-1.0.1.1-Esej8WRKuPYruXaDvkgKoEtg90clFmmDe0DcWJHROkc; 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_011Cc7kUAEsJxWZE99ZqiDVx a0cd588eaa48e3bf-NRT
M-Kiro/Antigravity	cf-ray: a0cd5a839e776604-AMS server: cloudflare x-oneapi-request-id: 202606162246583423014088268d9d6L3dqLpRf	id_format: msg_prefix extra_fields: ["container"] has_service_tier: true has_inference_geo: false model_has_date_suffix: true usage_has_cache_fields: true	hdr:x-oneapi-request-idhdr:server:cloudflarehdr:cf-raybody:id:msg_prefixbody:usage.service_tierbody:model:date_suffixbody:usage.cache_fieldsbody:extra_fieldsupstream:anthropic-direct-from-body	a0cd5a839e776604-AMS

协议指纹评分（机器自动）

总分 / 100 99 86 98 91 70

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 10.0 /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 0.0 /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 0.0 /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 0.0 /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

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

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

综合结论

与基线相似度基准（参考） 86 v3.20.0 98 v3.20.0 91 v3.20.0 70 v3.20.0

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 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 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 6873 ms 6181 ms 9294 ms 10597 ms 3757 ms

输出速度 158.4 tok/s 22.6 tok/s 180.6 tok/s 173.2 tok/s 184.9 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 ⓘ

$0.0878

$0.0703

$0.1000

$0.0875

$0.0316

缓存读取占比 ⓘ 70% 69% 70% 71% 81%

揭晓

通道

官方基准

服务商 WorldBase.ai ↗

通道M-

服务商 LinkAPI ↗

通道O-Max

服务商 FoxCode ↗

通道cc

服务商 luminai ↗

通道M-Kiro/Antigravity

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