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。

4 通道 · 6 步骤

2026/06/21 运行日期

claude-sonnet-4-6 官方基准

2 任务失败执行不完整

已揭晓揭晓进度

⚠ 1 通道不可测 · 已从主对比表移除点击展开原因

DBAI / M-
```
评测失败
```

步骤 / 指标

SSSAiCode

O-Max

duckcoding

cc专用

官方基准

SAIAi

O-Max

基本信息

服务名 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

客户端 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 19:35:06 ~ 19:35:11 (3.7s) 19:35:04 ~ 19:35:14 (5.2s) 19:35:12 ~ 19:35:17 (4.7s) 19:35:10 ~ 19:35:20 (5.7s)

提示词

查看本步 Prompt

ping. Reply with the single word: pong

HTTP 头(TTFB) 2,118 ms 3,485 ms 3,253 ms 4,571 ms

首文本(SSE delta) 2,235 ms 3,557 ms 3,254 ms 4,572 ms

总耗时 2,536 ms 3,805 ms 3,366 ms 4,647 ms

输入 3 3 3 3

输出 5 5 5 5

缓存创建

31,985

5m: 0 / 1h: 31,985

32,318

5m: 0 / 1h: 32,318

32,318

5m: 0 / 1h: 32,318

32,318

5m: 0 / 1h: 32,318

缓存读取 0 0 0 0

响应

pong

pong

pong

pong

步骤 2: identity text

时间 19:36:21 ~ 19:36:26 (2.3s) 19:37:24 ~ 19:37:29 (4.4s) 19:38:21 ~ 19:38:26 (3.7s) 19:36:25 ~ 19:36:30 (4.7s)

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) 1,130 ms 2,923 ms 2,332 ms 3,293 ms

首文本(SSE delta) 1,450 ms 2,924 ms 2,333 ms 3,294 ms

总耗时 1,593 ms 3,432 ms 2,673 ms 3,684 ms

输入 3 3 3 3

输出 22 22 22 22

缓存创建

202

5m: 0 / 1h: 202

202

5m: 0 / 1h: 202

202

5m: 0 / 1h: 202

202

5m: 0 / 1h: 202

缓存读取 31,985 32,318 32,318 32,318

响应

vendor: anthropic
brand: claude
model: claude-sonnet-4-5

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 19:38:10 ~ 19:38:15 (3.4s) 19:40:37 ~ 19:40:42 (4.7s) 19:42:09 ~ 19:42:24 (10.8s) 19:40:26 ~ 19:40:41 (14.0s)

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) 2,358 ms 3,264 ms 9,405 ms 12,562 ms

首文本(SSE delta) 2,596 ms 3,574 ms 9,528 ms 12,640 ms

总耗时 2,739 ms 3,696 ms 9,725 ms 12,981 ms

输入 3 3 3 3

输出 8 8 8 8

缓存创建

5m: 0 / 1h: 57

32,558

5m: 0 / 1h: 32,558

5m: 0 / 1h: 55

缓存读取 32,187 0 32,520 32,520

响应

2025-04

2025-08

2025-08

2025-08

步骤 4: identity_free text

时间 19:41:02 ~ 19:41:07 (3.2s) 19:42:33 ~ 19:42:43 (7.7s) 19:46:06 ~ 19:46:11 (4.3s) 19:42:21 ~ 19:42:26 (4.1s)

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) 1,501 ms 6,017 ms 2,663 ms 2,246 ms

首文本(SSE delta) 2,296 ms 6,421 ms 2,664 ms 2,690 ms

总耗时 2,356 ms 6,866 ms 3,298 ms 3,041 ms

输入 3 3 3 3

输出 62 46 41 48

缓存创建

5m: 0 / 1h: 43

5m: 0 / 1h: 39

5m: 0 / 1h: 43

缓存读取 32,244 32,558 32,575 32,575

输出速度 - 103.4 tok/s 64.7 tok/s 136.8 tok/s

响应

我是 Claude，由 Anthropic 开发的 AI 助手。我基于大型语言模型构建，能够帮助用户完成写作、分析、编程、问答等各类任务。

我是 Claude，由 Anthropic 公司打造的 AI 助手，当前运行的模型版本为 claude-sonnet-4-6。

我是 Claude，由 Anthropic 公司打造的 AI 助手。当前版本为 claude-sonnet-4-6。

我是 Claude，由 Anthropic 开发的 AI 助手。我能帮助完成写作、分析、编程、问答等多种任务。

步骤 5: knowledge_recall text

时间 19:43:11 ~ 19:43:21 (5.0s) 19:44:42 ~ 19:44:47 (4.6s) 19:50:02 ~ 19:50:07 (4.7s) 19:45:00 ~ 19:45:05 (4.6s)

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) 1,945 ms 1,500 ms 1,783 ms 1,780 ms

首文本(SSE delta) 4,103 ms 1,501 ms 1,785 ms 1,782 ms

总耗时 4,184 ms 3,548 ms 3,667 ms 3,603 ms

输入 3 3 3 3

输出 198 204 204 205

缓存创建

7,738

5m: 0 / 1h: 7,738

8,032

5m: 0 / 1h: 8,032

8,044

5m: 0 / 1h: 8,044

8,055

5m: 0 / 1h: 8,055

缓存读取 0 0 0 0

输出速度 - 99.7 tok/s 108.4 tok/s 112.6 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

步骤 6: digit_count text

时间 19:45:54 ~ 19:45:59 (0.9s) 19:47:10 ~ 19:47:35 (21.3s) 19:52:31 ~ 19:52:51 (17.1s) 19:46:34 ~ 19:46:54 (18.6s)

提示词

查看本步 Prompt

Count digits exactly in s. Return {"n":len(s),"c":[count0,count7,count9]}. s=231473399408845673384233933042546460993840549631237933930319676131684042

HTTP 头(TTFB) - 2,280 ms 1,151 ms 1,277 ms

首文本(SSE delta) - 2,300 ms 1,153 ms 1,278 ms

总耗时 - 20,305 ms 16,105 ms 17,587 ms

输入 - 164 164 164

输出 - 1,711 1,508 1,642

缓存创建

5m: 0 / 1h: 0

缓存读取 - 0 0 0

输出速度 - 95.0 tok/s 100.9 tok/s 100.7 tok/s

响应

失败

评测失败

## Counting digits in:
`231473399408845673384233933042546460993840549631237933930319676131684042`

### Length
Splitting into groups of 10:
`2314733994` `0884567338` `4233933042` `5464609938` `40549631

```json
{"n":72,"c":[6,4,9]}
```

**Verification:**

String indexed (1-based):
```
Pos:  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
Dig:  2  3  1  4  7  3  3  9  9  4  0  8  8  4  5  6

## Counting digits in s

**String:** `231473399408845673384233933042546460993840549631237933930319676131684042`

### Segmenting into groups of 10 for accuracy:

| Segment | 0s | 7s | 9s |
|---|---|---

总计

总输入(含缓存)

136,456

仅前 6 步

138,204

138,250

138,265

总输出

295

仅前 6 步

1,996

1,788

1,930

总缓存创建

40,025

5m: 0 / 1h: 40,025

仅前 6 步

73,149

5m: 0 / 1h: 73,149

40,658

5m: 0 / 1h: 40,658

40,673

5m: 0 / 1h: 40,673

总缓存读取

96,416

仅前 6 步

64,876

97,413

输出速度

仅前 6 步

95.7 tok/s

100.4 tok/s

102.5 tok/s

总执行时间

18.7s

仅前 6 步

47.8s

45.2s

51.7s

总墙钟时间

10m 53.0s

含步骤间等待

仅前 6 步

12m 31.0s

含步骤间等待

17m 39.0s

含步骤间等待

11m 44.0s

含步骤间等待

按官方价目重估

同等内容若直发 Anthropic 的估算 ⓘ

$0.1835

Anthropic: Claude Sonnet 4.6 (部分)

$0.3242

Anthropic: Claude Sonnet 4.6

$0.2090

Anthropic: Claude Sonnet 4.6

$0.2112

Anthropic: Claude Sonnet 4.6

通道指纹

来源步骤 knowledge_recall digit_count digit_count digit_count

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

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

CDN - - - -

ID 格式 msg_019eNzHALFRdoh6JQkGqKcZC msg_01G5ZteFBVxnfxD7MNYCkbuf msg_01KLbCxS7aU6SG4B7D11ABGD msg_01H3sJuJw39mkXtHWKySJBd7

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	cf-ray: a0f2c119eba2964b-LAX server: Photon-Edge set-cookie: _cfuvid=vboq0q9o8nZGiinUT8T6JWLs_nhdp44FPDFmunVzPMQ-1782042192.949546-1.0.1.1-Ek_bevAsfGwmKy9xg9hluLlj2EDh7kN9ZO72eGDLn78; 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: 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	1782042192905-7lc1db69jfvl4 req_011CcGLzxK7nG6DRoztws6gN a0f2c119eba2964b-LAX
cc专用	server: Nginx x-oneapi-request-id: 202606211147114227624638268d9d6RuoQbzXl	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-idbody:id:msg_prefixbody:usage.service_tierbody:usage.inference_geobody:usage.cache_fieldsbody:extra_fieldsupstream:anthropic-direct-from-body	—
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_011CcGMiBMaoTJ5hQnZYpmjs
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: 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_011CcGMFsBUH9RZH9Ub4Gpv1

协议指纹评分（机器自动）

总分 / 100 90 99 100

基线状态已对照基线已对照基线已对照基线

模型匹配 — 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

sliding 5m — 7.7 /10 ×13 10.0 /10 ×13 10.0 /10 ×13

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

缓存命中比 — 5.5 /10 ×20 10.0 /10 ×20 10.0 /10 ×20

身份(结构) — 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

身份(自由) — 10.0 /10 ×7 10.0 /10 ×7 10.0 /10 ×7

原生 msg-ID — 10.0 /10 ×8 10.0 /10 ×8 10.0 /10 ×8

Req-ID 透传 — 0.0 /10 ×4 10.0 /10 ×4 10.0 /10 ×4

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

service_tier — 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

SDK 一致 — 10.0 /10 ×2 10.0 /10 ×2 10.0 /10 ×2

系统提示纯净 — 10.0 /10 ×8 10.0 /10 ×8 10.0 /10 ×8

延迟基线 — 10.0 /10 ×5 10.0 /10 ×5 10.0 /10 ×5

流式投递 — 10.0 /10 ×5 10.0 /10 ×5 10.0 /10 ×5

综合结论

与基线相似度执行失败，不评分 90 v3.24.0 基准（参考） 100 v3.24.0

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 2682 ms 6942 ms 6472 ms 7591 ms

输出速度 - 95.7 tok/s 100.4 tok/s 102.5 tok/s

完成步骤 5/6 成功 · 1 失败 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 ⓘ

$0.1835 (部分)

$0.3242

$0.2090

$0.2112

缓存读取占比 ⓘ 71% (部分) 47% 70% 70%

揭晓

通道

服务商 SSSAiCode ↗

通道O-Max

服务商 duckcoding ↗

通道cc专用

官方基准

服务商 SAIAi ↗

通道O-Max

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