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-sonnet-4-6 官方基准

全部 ≥ 70 与基线相似度

已揭晓揭晓进度

步骤 / 指标

TopRouterCN

O-Max

官方基准

Aiide

O-Max

LingxiCode

M-

AiTokensFlux

O-Pro

基本信息

服务名 Claude Code Claude Code Claude Code Claude Code Claude Code

模型 claude-sonnet-4-6 claude-sonnet-4-6 claude-sonnet-4-6 claude-sonnet-4-6 claude-sonnet-4-6

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 17:03:36 ~ 17:03:41 (4.7s) 17:03:40 ~ 17:03:45 (3.7s) 17:03:34 ~ 17:03:44 (6.5s) 17:03:34 ~ 17:03:44 (7.0s) 17:03:38 ~ 17:03:43 (4.3s)

提示词

查看本步 Prompt

ping. Reply with the single word: pong

HTTP 头(TTFB) 3,183 ms 2,284 ms 3,606 ms 4,009 ms 2,903 ms

首文本(SSE delta) 3,189 ms 2,285 ms 3,609 ms 4,030 ms 2,905 ms

总耗时 3,257 ms 2,414 ms 3,801 ms 4,207 ms 3,060 ms

输入 1,467 3 3 3 3

输出 5 5 5 5 5

缓存创建

30,854

5m: 0 / 1h: 30,854

32,318

5m: 0 / 1h: 32,318

32,457

5m: 0 / 1h: 32,457

32,318

5m: 0 / 1h: 32,318

1,464

5m: 0 / 1h: 1,464

缓存读取 0 0 0 0 30,993

响应

pong

pong

pong

pong

pong

步骤 2: identity text

时间 17:07:28 ~ 17:07:39 (8.2s) 17:07:36 ~ 17:07:41 (4.3s) 17:06:08 ~ 17:06:18 (5.6s) 17:07:23 ~ 17:07:28 (4.5s) 17:06:28 ~ 17:06:33 (3.7s)

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。请按下列 3 行格式精确填空作答（label 不变，值小写）：
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) 7,289 ms 2,693 ms 3,724 ms 2,955 ms 2,503 ms

首文本(SSE delta) 7,290 ms 2,695 ms 3,737 ms 2,956 ms 2,504 ms

总耗时 7,377 ms 3,026 ms 4,212 ms 3,381 ms 2,914 ms

输入 3 3 3 3 3

输出 22 22 22 22 22

缓存创建

5m: 0 / 1h: 0

182

5m: 0 / 1h: 182

182

5m: 0 / 1h: 182

182

5m: 0 / 1h: 182

182

5m: 0 / 1h: 182

缓存读取 32,500 32,318 32,457 32,318 32,457

响应

vendor: anthropic
brand: claude
model: claude-sonnet-4-6

vendor: anthropic
brand: claude
model: claude-sonnet-4-6

vendor: anthropic
brand: claude
model: claude-sonnet-4-6

vendor: anthropic
brand: claude
model: claude-sonnet-4-6

vendor: anthropic
brand: claude
model: claude-sonnet-4-6

步骤 3: cutoff text

时间 17:11:02 ~ 17:11:07 (4.6s) 17:09:49 ~ 17:09:54 (3.9s) 17:08:19 ~ 17:08:24 (4.5s) 17:08:57 ~ 17:09:02 (4.9s) 17:08:28 ~ 17:08:38 (5.3s)

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) 3,738 ms 2,231 ms 2,989 ms 3,771 ms 4,246 ms

首文本(SSE delta) 3,739 ms 2,232 ms 2,990 ms 3,966 ms 4,248 ms

总耗时 3,817 ms 2,491 ms 3,195 ms 4,075 ms 4,430 ms

输入 3 3 3 3 3

输出 8 8 8 8 8

缓存创建

5m: 0 / 1h: 57

5m: 0 / 1h: 38

5m: 0 / 1h: 57

缓存读取 32,500 32,500 32,639 32,500 32,639

响应

2025-08

2025-08

2025-08

2025-08

2025-08

步骤 4: identity_free text

时间 17:14:27 ~ 17:14:37 (5.2s) 17:13:07 ~ 17:13:12 (4.6s) 17:09:46 ~ 17:09:56 (5.5s) 17:12:32 ~ 17:12:37 (4.4s) 17:11:35 ~ 17:11:45 (5.1s)

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) 4,314 ms 3,035 ms 3,623 ms 2,668 ms 2,667 ms

首文本(SSE delta) 4,315 ms 3,036 ms 3,632 ms 2,669 ms 2,706 ms

总耗时 4,426 ms 3,646 ms 4,372 ms 3,499 ms 3,843 ms

输入 3 3 3 3 3

输出 61 50 43 56 55

缓存创建

5m: 0 / 1h: 43

5m: 0 / 1h: 41

5m: 0 / 1h: 43

5m: 0 / 1h: 35

缓存读取 32,557 32,557 32,677 32,538 32,696

输出速度 - 82.0 tok/s 58.1 tok/s 67.5 tok/s 48.4 tok/s

响应

我是 Claude，由 Anthropic 开发的 AI 助手。我被设计用于对话、分析、写作和编程等任务，致力于提供安全、有帮助的回应。

我叫 Claude，是 Anthropic 开发的 AI 助手，属于 Claude 产品系列。当前版本为 claude-sonnet-4-6。

我叫 Claude，是由 Anthropic 公司开发的 AI 助手，当前版本为 Claude Sonnet 4.6。

我是 Claude，由 Anthropic 开发的 AI 助手。我可以帮助回答问题、分析代码、撰写文本、进行研究等多种任务。

我是 Claude，由 Anthropic 开发的 AI 助手。我基于大型语言模型构建，旨在提供安全、有益的对话与任务协助。

步骤 5: knowledge_recall text

时间 17:16:08 ~ 17:16:18 (5.6s) 17:16:01 ~ 17:16:11 (5.2s) 17:12:16 ~ 17:12:26 (8.0s) 17:16:20 ~ 17:16:30 (5.8s) 17:15:21 ~ 17:15:31 (5.4s)

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) 4,814 ms 1,552 ms 3,453 ms 2,455 ms 2,064 ms

首文本(SSE delta) 4,815 ms 1,554 ms 4,135 ms 2,456 ms 2,065 ms

总耗时 4,870 ms 3,936 ms 6,658 ms 5,010 ms 4,453 ms

输入 3 3 3 3 3

输出 196 196 196 196 205

缓存创建

8,050

5m: 0 / 1h: 8,050

8,037

5m: 0 / 1h: 8,037

8,011

5m: 0 / 1h: 8,011

8,026

5m: 0 / 1h: 8,026

2,318

5m: 0 / 1h: 2,318

缓存读取 0 0 0 0 5,718

输出速度 burst 82.3 tok/s 77.7 tok/s 76.7 tok/s 85.8 tok/s

响应

{"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":"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":"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":"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":"Real Madrid","nba_2024":"Boston Celtics","copa_america_2024":"Argentina","euro2024":"Spain","nobel

步骤 6: digit_count text

时间 17:19:17 ~ 17:19:32 (10.8s) 17:19:36 ~ 17:19:56 (19.2s) 17:15:04 ~ 17:15:19 (12.5s) 17:19:33 ~ 17:19:53 (17.8s) 17:18:31 ~ 17:18:51 (16.2s)

提示词

查看本步 Prompt

Count digits exactly in s. Return {"n":len(s),"c":[count0,count7,count9]}. s=093837035690110438863244658299028734447607429607501661656584268439260949

HTTP 头(TTFB) 6,511 ms 1,485 ms 2,390 ms 1,666 ms 1,790 ms

首文本(SSE delta) 6,512 ms 1,486 ms 2,791 ms 2,057 ms 1,807 ms

总耗时 10,081 ms 18,067 ms 11,498 ms 17,011 ms 15,465 ms

输入 164 164 164 164 164

输出 821 1,487 793 1,386 1,329

缓存创建

5m: 0 / 1h: 0

缓存读取 0 0 0 0 0

输出速度 burst 89.7 tok/s 91.1 tok/s 92.7 tok/s 97.3 tok/s

响应

```json
{"n":72,"c":[9,5,8]}
```

**Verification walkthrough:**

```
s = 093837035690110438863244658299028734447607429607501661656584268439260949
     ^

**Counting characters in:** `093837035690110438863244658299028734447607429607501661656584268439260949`

### Step-by-step breakdown:

| # | Char | # | Char | # | Char | # | Char |
|---|------|---|-----

**Counting the string:**

`093837035690110438863244658299028734447607429607501661656584268439260949`

**Length:** 72 characters

**Digit-by-digit scan:**

| Digit | Positions (1-indexed) | Count |
|--

```json
{"n":72,"c":[9,5,8]}
```

**Verification walkthrough:**

```
Index: 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Digit: 0  9  3  8  3  7  0  3  5  6  9  0  1  1  0  4

**Parsing** `s = 093837035690110438863244658299028734447607429607501661656584268439260949`

**Step 1 — Length**

Counting all characters: **n = 72**

**Step 2 — Digit-by-digit scan (indexed 1–72)**

|

总计

总输入(含缓存) 138,204 138,189 138,681 138,142 138,738

总输出 1,113 1,768 1,067 1,673 1,624

总缓存创建

39,004

5m: 0 / 1h: 39,004

40,635

5m: 0 / 1h: 40,635

40,729

5m: 0 / 1h: 40,729

40,607

5m: 0 / 1h: 40,607

4,056

5m: 0 / 1h: 4,056

总缓存读取 97,557 97,375 97,773 97,356 134,503

输出速度 burst 88.5 tok/s 86.2 tok/s 89.3 tok/s 92.5 tok/s

总执行时间 39.0s 40.8s 42.6s 44.3s 39.9s

总墙钟时间

15m 56.4s

含步骤间等待

16m 16.2s

含步骤间等待

11m 45.3s

含步骤间等待

16m 19.3s

含步骤间等待

15m 13.2s

含步骤间等待

按官方价目重估

同等内容若直发 Anthropic 的估算 ⓘ

$0.1972

Anthropic: Claude Sonnet 4.6

$0.2087

Anthropic: Claude Sonnet 4.6

$0.1986

Anthropic: Claude Sonnet 4.6

$0.2071

Anthropic: Claude Sonnet 4.6

$0.0805

Anthropic: Claude Sonnet 4.6

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%) 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 -

ID 格式 msg_01KPVPZXqtRAh9YfVnWJu9qf msg_01Vhnu7S8LtiavSRY1fLvvJK msg_011cYACMYe6R1M24jU6N7dBV msg_01Bm2vFhBDHpU964qD6RsMuw msg_016wnfjkpXGL2wLfEscd6Qn7

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/1.27.5	id_format: msg_prefix extra_fields: ["stop_details"] has_service_tier: true has_inference_geo: true model_has_date_suffix: false usage_has_cache_fields: true	body:id:msg_prefixbody:usage.service_tierbody:usage.inference_geobody:usage.cache_fieldsbody:extra_fieldsupstream:anthropic-direct-from-body	tr-ew7y4grd-qprjui2m7l-040307
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: false usage_has_cache_fields: true	body:id:msg_prefixbody:usage.service_tierbody:usage.inference_geobody:usage.cache_fieldsbody:extra_fieldsupstream:anthropic-direct-from-body	req_011CcMqUdv9LaKW6A51PRCv4
O-Max	cf-ray: a10aa0403ba3b8a2-AMS server: cloudflare x-new-api-version: 0.12.11 x-oneapi-request-id: 202606240915058616754538268d9d6SygWqFN7	id_format: msg_prefix extra_fields: ["stop_details"] has_service_tier: true has_inference_geo: true model_has_date_suffix: false 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:usage.cache_fieldsbody:extra_fieldsupstream:anthropic-direct-from-body	a10aa0403ba3b8a2-AMS
M-	cf-ray: a10aa6d029987343-NRT server: cloudflare set-cookie: server_name_session=46db03c0040e9983379bcced7435c21b; Max-Age=86400; httponly; path=/ x-oneapi-request-id: 20260624091934560436929cGBHZaj1	id_format: msg_prefix extra_fields: ["stop_details"] has_service_tier: true has_inference_geo: true model_has_date_suffix: false usage_has_cache_fields: true	hdr:x-oneapi-request-idhdr:server:cloudflarehdr:cf-raybody:id:msg_prefixbody:usage.service_tierbody:usage.inference_geobody:usage.cache_fieldsbody:extra_fieldsupstream:anthropic-direct-from-body	a10aa6d029987343-NRT
O-Pro	server: nginx	id_format: msg_prefix extra_fields: ["stop_details"] has_service_tier: true has_inference_geo: true model_has_date_suffix: false usage_has_cache_fields: true	body:id:msg_prefixbody:usage.service_tierbody:usage.inference_geobody:usage.cache_fieldsbody:extra_fieldsupstream:anthropic-direct-from-body	—

协议指纹评分（机器自动）

总分 / 100 95 100 97 98 98

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 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 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 10.0 /10 ×8 10.0 /10 ×8 10.0 /10 ×8 10.0 /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 9.9 /10 ×5 10.0 /10 ×5 10.0 /10 ×5 10.0 /10 ×5 10.0 /10 ×5

流式投递 0.0 /10 ×5 10.0 /10 ×5 8.6 /10 ×5 10.0 /10 ×5 10.0 /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 95 v3.24.1 基准（参考） 97 v3.24.1 98 v3.24.1 98 v3.24.1

雷达图例

共 21 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 流式投递 buffer_dump_match
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 缓存连续 cache_continuity_intra
13 知识召回 knowledge_recall_match
14 延迟基线 latency_baseline_match
15 原生 msg-ID anthropic_msg_id_format
16 Sliding 缓存 cache_sliding_correctness
17 结构化身份 identity_structured_match
18 信封自报一致 envelope_self_report_match
19 中文遵循 instruction_following_lang
20 档位思考量 tier_thinking_volume_match
21 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 5638 ms 5597 ms 5623 ms 6197 ms 5694 ms

输出速度 burst 88.5 tok/s 86.2 tok/s 89.3 tok/s 92.5 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 ⓘ

$0.1972

$0.2087

$0.1986

$0.2071

$0.0805

缓存读取占比 ⓘ 71% 70% 71% 70% 97%

揭晓

通道

服务商 TopRouterCN ↗

通道O-Max

官方基准

服务商 Aiide ↗

通道O-Max

服务商 LingxiCode ↗

通道M-

服务商 AiTokensFlux ↗

通道O-Pro

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