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

2026/06/26 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

SSSAiCode

R-Kiro

官方基准

SSSAiCode

O-Max

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 22:55:16 ~ 22:55:21 (4.0s) 22:55:10 ~ 22:55:25 (12.3s) 22:55:18 ~ 22:55:23 (3.8s) 22:55:12 ~ 22:55:17 (3.5s) 22:55:14 ~ 22:55:24 (6.7s)

提示词

查看本步 Prompt

ping. Reply with the single word: pong

HTTP 头(TTFB) 2,468 ms 2,268 ms 2,327 ms 2,109 ms 4,975 ms

首文本(SSE delta) 2,469 ms 2,291 ms 2,329 ms 2,261 ms 5,019 ms

总耗时 3,089 ms 3,604 ms 2,757 ms 2,344 ms 5,334 ms

输入 10 0 10 10 2,886

输出 53 187 54 42 1

缓存创建

33,122

5m: 33,122 / 1h: 0

33,122

5m: 0 / 1h: 33,122

32,785

5m: 0 / 1h: 32,785

2,597

5m: 2,597 / 1h: 0

缓存读取 0 0 0 0 23,374

输出速度 85.5 tok/s 142.4 tok/s 126.2 tok/s - -

响应

pong

No prior work in this conversation. What do you need?

pong

pong

pong

步骤 2: identity text

时间 22:57:01 ~ 22:57:06 (4.8s) 22:59:03 ~ 22:59:13 (6.8s) 22:58:40 ~ 22:58:50 (5.9s) 22:56:41 ~ 22:56:56 (11.1s) 22:56:50 ~ 22:57:00 (7.3s)

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。自报身份，输出严格遵守以下 3 行格式（不允许 markdown、代码块、额外文本）：
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) 1,365 ms 3,484 ms 3,468 ms 1,842 ms 4,234 ms

首文本(SSE delta) 1,366 ms 3,535 ms 3,470 ms 9,953 ms 4,235 ms

总耗时 3,983 ms 6,048 ms 4,945 ms 10,253 ms 4,706 ms

输入 10 0 10 10 2,905

输出 261 383 185 861 28

缓存创建

192

5m: 192 / 1h: 0

248

5m: 0 / 1h: 248

236

5m: 0 / 1h: 236

2,613

5m: 2,613 / 1h: 0

缓存读取 33,122 0 33,122 32,785 23,525

输出速度 99.7 tok/s 152.4 tok/s 125.4 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

I appreciate the question, but I don't have access to the exact API model identifier for the current request. I can tell you that I'm Claude, made by Anthropic, but the precise `model` field value (wi

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

步骤 3: cutoff text

时间 22:59:57 ~ 23:00:07 (5.0s) 23:02:57 ~ 23:03:07 (8.3s) 23:02:01 ~ 23:02:06 (3.5s) 23:00:54 ~ 23:00:59 (3.4s) 23:00:21 ~ 23:00:31 (7.6s)

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) 3,164 ms 2,343 ms 2,005 ms 2,118 ms 3,997 ms

首文本(SSE delta) 3,165 ms 2,363 ms 2,007 ms 2,426 ms 4,032 ms

总耗时 4,254 ms 4,431 ms 2,489 ms 2,569 ms 4,115 ms

输入 10 0 10 10 2,909

输出 115 247 66 55 4

缓存创建

5m: 61 / 1h: 0

208

5m: 0 / 1h: 208

901

5m: 0 / 1h: 901

2,618

5m: 2,618 / 1h: 0

缓存读取 33,314 0 33,370 33,021 23,562

输出速度 105.6 tok/s 119.4 tok/s 136.9 tok/s - -

响应

2025-02

Ready for the next task. What would you like me to work on?

2025-02

2024-04

2025-02

步骤 4: identity_free text

时间 23:02:51 ~ 23:03:01 (7.2s) 23:04:47 ~ 23:04:57 (5.5s) 23:05:27 ~ 23:05:42 (10.6s) 23:04:37 ~ 23:04:52 (13.3s) 23:01:33 ~ 23:01:43 (7.4s)

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) 4,069 ms 2,835 ms 2,111 ms 6,920 ms 4,358 ms

首文本(SSE delta) 4,146 ms 2,858 ms 2,113 ms 9,098 ms 5,166 ms

总耗时 6,404 ms 4,787 ms 9,626 ms 9,344 ms 5,386 ms

输入 10 0 10 10 2,913

输出 201 241 944 446 56

缓存创建

5m: 43 / 1h: 0

108

5m: 0 / 1h: 108

5m: 0 / 1h: 97

2,621

5m: 2,621 / 1h: 0

缓存读取 33,375 0 33,578 33,922 23,592

输出速度 89.0 tok/s 124.9 tok/s 125.6 tok/s burst -

响应

我是Claude Haiku 4.5，由Anthropic公司开发的AI助手。我可以帮助用户进行对话、编程、分析、写作等多种任务。

我是Claude，由Anthropic公司开发的AI助手。我可以帮助用户完成软件工程任务，包括编程、代码审查、bug修复和项目管理等。我致力于提供准确、安全且高效的帮助。

我是Claude，由Anthropic公司开发的AI助手。可以帮助用户编写代码、分析问题、进行研究和各类技术工作。目前这个版本是Haiku 4.5模型。

我是Claude，由Anthropic开发的AI助手。我可以帮助用户进行编程、写作、分析和问题解决等多项任务。

我是Claude Haiku 4.5，由Anthropic公司开发的AI助手。我可以帮助用户进行软件工程任务、代码编写、分析和问题解决。

步骤 5: knowledge_recall text

时间 23:06:04 ~ 23:06:50 (44.2s) 23:08:29 ~ 23:08:39 (9.2s) 23:06:50 ~ 23:07:10 (17.3s) 23:05:56 ~ 23:06:12 (14.1s) 23:04:14 ~ 23:04:24 (6.3s)

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) 1,376 ms 2,344 ms 1,692 ms 1,568 ms 3,673 ms

首文本(SSE delta) 1,377 ms 2,367 ms 1,694 ms 11,106 ms 3,675 ms

总耗时 43,357 ms 8,435 ms 16,335 ms 11,427 ms 5,206 ms

输入 10 0 10 10 749

输出 2,461 1,171 2,501 1,765 201

缓存创建

8,084

5m: 8,084 / 1h: 0

9,248

5m: 0 / 1h: 9,248

9,083

5m: 0 / 1h: 9,083

673

5m: 673 / 1h: 0

缓存读取 0 0 0 0 6,060

输出速度 58.6 tok/s 193.0 tok/s 170.8 tok/s burst 131.3 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

{"facts":{"super_bowl_lviii":"Kansas City Chiefs","oscars2024_best_picture":"Oppenheimer","ucl_2024":"UNKNOWN","nba_2024":"UNKNOWN","copa_america_2024":"UNKNOWN","euro2024":"UNKNOWN","nobel_peace_2024

```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 23:10:11 ~ 23:11:21 (66.4s) 23:10:22 ~ 23:10:37 (13.7s) 23:10:18 ~ 23:10:38 (19.8s) 23:07:13 ~ 23:07:38 (23.3s) 23:07:41 ~ 23:07:51 (8.6s)

提示词

查看本步 Prompt

Count digits exactly in s. Return {"n":len(s),"c":[count0,count7,count9]}. s=322067147192944695245457336973541219574985933024254368166710300698469794

HTTP 头(TTFB) 2,373 ms 2,469 ms 2,097 ms 1,717 ms 7,327 ms

首文本(SSE delta) 2,374 ms 2,500 ms 2,098 ms 1,718 ms 7,328 ms

总耗时 65,557 ms 13,008 ms 18,906 ms 22,650 ms 7,509 ms

输入 193 0 193 139 189

输出 4,202 1,673 3,363 4,038 19

缓存创建

5m: 0 / 1h: 0

缓存读取 0 0 0 0 -

输出速度 66.5 tok/s 159.2 tok/s 200.1 tok/s 192.9 tok/s -

响应

```json
{"n": 72, "c": [5, 7, 10]}
```

```json
{"n":72,"c":[5,7,10]}
```

```json
{"n":72,"c":[5,7,10]}
```

```json
{"n":72,"c":[5,7,10]}
```

```json
{"n":72,"c":[6,8,10]}
```

总计

总输入(含缓存) 141,556 0 143,247 143,019 123,786

总输出 7,293 3,902 7,113 7,207 309

总缓存创建

41,502

5m: 41,502 / 1h: 0

42,934

5m: 0 / 1h: 42,934

43,102

5m: 0 / 1h: 43,102

11,122

5m: 11,122 / 1h: 0

总缓存读取 99,811 0 100,070 99,728 100,113

输出速度 65.3 tok/s 159.9 tok/s 172.0 tok/s 192.9 tok/s 131.3 tok/s

总执行时间 2m 11.5s 55.8s 1m 0.9s 1m 8.7s 43.8s

总墙钟时间

16m 5.1s

含步骤间等待

15m 27.1s

含步骤间等待

15m 20.1s

含步骤间等待

12m 26.0s

含步骤间等待

12m 37.1s

含步骤间等待

按官方价目重估

同等内容若直发 Anthropic 的估算 ⓘ

$0.0791

Anthropic: Claude Haiku 4.5

>=$0.0195

Anthropic: Claude Haiku 4.5 (成本不完整)

$0.0995

Anthropic: Claude Haiku 4.5

$0.1001

Anthropic: Claude Haiku 4.5

$0.0328

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 (75%) direct (100%) direct (100%) one-api (100%)

上游 Anthropic 直连 (100%) Anthropic 直连 (75%) Anthropic 直连 (100%) Anthropic 直连 (100%) Anthropic 直连 (100%)

CDN cloudflare - - - cloudflare

ID 格式 msg_0199a2wVjf6HBHgQpRGDSSSU msg_583fee4f5cc3438684f0cd10ba5e0a50 msg_01G6zDUWHLofrUSeXUDmx8V7 msg_01CWoj9f2DEVeBuXyeVi66DZ msg_01VeKADTTZyR79z8V8ft2WQZF3

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
cc	cf-ray: a11d232d0b0ae041-NRT server: cloudflare set-cookie: _cfuvid=piMNkMaRUhi3zzDIDJGqJm5OIcQb3hppWZTlL4Iuq6o-1782486612.4085076-1.0.1.1-rde5Ga5vvj60oVO._iW4M3RZNoUvzOzODnqbOtKxWXQ; 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_011CcS5q5MYTmWeCoaNsXxTN a11d232d0b0ae041-NRT
R-Kiro	server: Photon-Edge x-amzn-requestid: 339fdaa2-d399-4baf-8d1f-88d6926620a9	id_format: msg_prefix has_service_tier: false has_inference_geo: false model_has_date_suffix: false usage_has_cache_fields: true	body:id:msg_prefixbody:usage.cache_fieldsupstream:x-amzn-requestidupstream:anthropic-direct-from-body	1782486623315-4jwuemik1rnrp 339fdaa2-d399-4baf-8d1f-88d6926620a9
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_011CcS5qXm3JSgzPkrdaPjwq
O-Max	cf-ray: a11d1ed7da87cc65-LAX server: Photon-Edge set-cookie: _cfuvid=1Z_FBlriHBmTPnRrrsE_7e9U2ZII9zA2LIsUnu1yxb8-1782486434.5320573-1.0.1.1-0vNh4V5qABXNoD84DqIqvKvnIlBRgqaopz_mTCzXeco; 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	1782486433916-gxhm86twowsc3 req_011CcS5buqgpqf2pte5MxtB1 a11d1ed7da87cc65-LAX
M-Kiro/Antigravity	cf-ray: a11d1f85ea9829c4-AMS server: cloudflare x-oneapi-request-id: 202606261507429072069068268d9d6O9uDphe1	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	a11d1f85ea9829c4-AMS

协议指纹评分（机器自动）

总分 / 100 90 51 98 78 69

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 8.5 /10 ×20 0.0 /10 ×20 8.3 /10 ×20 8.2 /10 ×20 10.0 /10 ×20

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

系统提示纯净 10.0 /10 ×8 未计入 10.0 /10 ×8 5.0 /10 ×8 0.0 /10 ×8

身份(结构) 10.0 /10 ×7 10.0 /10 ×7 10.0 /10 ×7 0.0 /10 ×7 10.0 /10 ×7

知识截止 10.0 /10 ×7 0.0 /10 ×7 10.0 /10 ×7 0.0 /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 0.0 /10 ×6 10.0 /10 ×6 10.0 /10 ×6 10.0 /10 ×6

inference_geo 10.0 /10 ×5 0.0 /10 ×5 10.0 /10 ×5 10.0 /10 ×5 0.0 /10 ×5

延迟基线 9.9 /10 ×5 10.0 /10 ×5 10.0 /10 ×5 10.0 /10 ×5 8.0 /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 90 v3.25.0 51 v3.25.0 基准（参考） 78 v3.25.0 69 v3.25.0

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 21107 ms 6719 ms 9176 ms 9765 ms 5376 ms

输出速度 65.3 tok/s 159.9 tok/s 172.0 tok/s 192.9 tok/s 131.3 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 ⓘ

$0.0791

>=$0.0195 (成本不完整)

$0.0995

$0.1001

$0.0328

缓存读取占比 ⓘ 71% - 70% 70% 81%

揭晓

通道

服务商 FoxCode ↗

通道cc

服务商 SSSAiCode ↗

通道R-Kiro

官方基准

服务商 SSSAiCode ↗

通道O-Max

服务商 luminai ↗

通道M-Kiro/Antigravity

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