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

PackyCode

cc-Zeus

ClaudeZZ

R-kiro

官方基准

SAIAi

O-Max

DawAPI

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 02:37:37 ~ 02:37:43 (3.6s) 02:37:39 ~ 02:37:50 (5.5s) 02:37:44 ~ 02:37:49 (3.4s) 02:37:42 ~ 02:37:47 (4.0s) 02:37:35 ~ 02:37:46 (6.6s)

提示词

查看本步 Prompt

ping. Reply with the single word: pong

HTTP 头(TTFB) 2,078 ms 3,894 ms 1,917 ms 2,361 ms 4,893 ms

首文本(SSE delta) 2,079 ms 3,895 ms 1,919 ms 2,362 ms 5,499 ms

总耗时 2,517 ms 4,525 ms 2,326 ms 2,926 ms 5,649 ms

输入 10 10 10 10 10

输出 51 45 64 54 64

缓存创建

33,122

5m: 0 / 1h: 33,122

22,404

5m: 0 / 1h: 22,404

5m: 0 / 1h: 0

33,122

5m: 0 / 1h: 33,122

30,483

5m: 0 / 1h: 30,483

缓存读取 0 0 33,122 0 0

输出速度 116.4 tok/s 71.4 tok/s 157.2 tok/s 95.7 tok/s -

响应

pong

pong

pong

pong

pong

步骤 2: identity text

时间 02:39:27 ~ 02:39:37 (4.9s) 02:39:47 ~ 02:39:57 (9.3s) 02:41:02 ~ 02:41:07 (4.3s) 02:39:07 ~ 02:39:12 (4.5s) 02:39:55 ~ 02:40:10 (12.2s)

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) 1,651 ms 6,244 ms 1,497 ms 1,552 ms 6,683 ms

首文本(SSE delta) 1,652 ms 6,246 ms 1,499 ms 1,555 ms 7,579 ms

总耗时 3,879 ms 8,414 ms 3,376 ms 3,444 ms 11,154 ms

输入 10 10 10 10 10

输出 227 219 210 235 209

缓存创建

255

5m: 0 / 1h: 255

256

5m: 0 / 1h: 256

268

5m: 0 / 1h: 268

258

5m: 0 / 1h: 258

2,826

5m: 0 / 1h: 2,826

缓存读取 33,122 22,404 33,122 33,122 27,859

输出速度 101.9 tok/s 101.0 tok/s 111.9 tok/s 124.4 tok/s 58.5 tok/s

响应

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

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

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 02:42:29 ~ 02:42:34 (3.5s) 02:43:13 ~ 02:43:23 (7.0s) 02:44:27 ~ 02:44:32 (3.2s) 02:42:38 ~ 02:42:43 (3.1s) 02:42:02 ~ 02:42:27 (21.6s)

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) 1,963 ms 5,144 ms 1,431 ms 1,673 ms 20,125 ms

首文本(SSE delta) 1,964 ms 5,151 ms 1,432 ms 1,674 ms 20,716 ms

总耗时 2,540 ms 6,085 ms 2,218 ms 2,176 ms 20,794 ms

输入 10 10 10 10 10

输出 82 67 112 67 62

缓存创建

269

5m: 0 / 1h: 269

283

5m: 0 / 1h: 283

252

5m: 0 / 1h: 252

275

5m: 0 / 1h: 275

244

5m: 0 / 1h: 244

缓存读取 33,377 22,660 33,390 33,380 30,483

输出速度 142.4 tok/s 71.7 tok/s 142.5 tok/s 133.5 tok/s -

响应

2025-02

2025-02

2025-02

2025-02

2025-02

步骤 4: identity_free text

时间 02:45:12 ~ 02:45:22 (8.0s) 02:45:34 ~ 02:45:49 (11.1s) 02:47:25 ~ 02:47:40 (11.8s) 02:45:23 ~ 02:45:28 (4.3s) 02:45:38 ~ 02:45:53 (13.6s)

提示词

查看本步 Prompt

用中文回答：你是谁？由谁开发？50 字以内。

HTTP 头(TTFB) 1,707 ms 4,049 ms 2,102 ms 1,648 ms 8,583 ms

首文本(SSE delta) 1,708 ms 4,051 ms 2,104 ms 1,651 ms 10,129 ms

总耗时 6,464 ms 10,213 ms 10,562 ms 3,281 ms 12,805 ms

输入 10 10 10 10 10

输出 678 1,144 805 204 660

缓存创建

116

5m: 0 / 1h: 116

119

5m: 0 / 1h: 119

150

5m: 0 / 1h: 150

105

5m: 0 / 1h: 105

604

5m: 0 / 1h: 604

缓存读取 33,646 22,943 33,642 33,655 30,483

输出速度 142.6 tok/s 185.7 tok/s 95.2 tok/s 125.2 tok/s 246.6 tok/s

响应

我是Claude，由Anthropic公司开发的AI助手，专长是软件工程任务和代码帮助。

我是Claude Code，由Anthropic开发的AI助手。我帮助用户完成软件工程任务，包括代码编写、调试、测试和重构等。

我是Claude Haiku 4.5，由Anthropic公司开发的AI助手。我专长于编程、代码审查、软件工程问题的协助，能帮用户解决技术问题、分析代码、优化实现方案。

我是Claude Haiku 4.5，由Anthropic公司打造的AI助手。专长是编程、代码分析和软件工程任务。

Claude Code是由Anthropic公司开发的官方命令行工具。它基于Claude大语言模型，帮助用户完成编程、调试、代码审查等软件工程任务。

步骤 5: knowledge_recall text

时间 02:47:14 ~ 02:47:34 (19.0s) 02:48:14 ~ 02:48:34 (19.5s) 02:51:36 ~ 02:52:06 (27.1s) 02:47:12 ~ 02:47:32 (17.2s) 02:49:14 ~ 02:49:54 (37.4s)

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) 1,621 ms 3,475 ms 1,386 ms 1,359 ms 6,238 ms

首文本(SSE delta) 1,622 ms 3,477 ms 1,388 ms 1,362 ms 6,592 ms

总耗时 17,908 ms 18,725 ms 26,070 ms 16,277 ms 36,570 ms

输入 10 10 10 10 10

输出 3,006 2,300 4,161 2,553 4,054

缓存创建

9,058

5m: 0 / 1h: 9,058

9,697

5m: 0 / 1h: 9,697

3,494

5m: 0 / 1h: 3,494

8,582

5m: 0 / 1h: 8,582

5,493

5m: 0 / 1h: 5,493

缓存读取 0 0 5,721 0 0

输出速度 184.6 tok/s 150.8 tok/s 168.6 tok/s 171.2 tok/s 135.2 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":"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 02:49:56 ~ 02:50:17 (18.2s) 02:49:39 ~ 02:49:59 (17.2s) 02:53:15 ~ 02:53:35 (16.6s) 02:50:51 ~ 02:51:16 (20.4s) 02:51:12 ~ 02:51:33 (18.1s)

提示词

查看本步 Prompt

Count digits exactly in s. Return {"n":len(s),"c":[count0,count7,count9]}. s=481016558291200489232896577226918223306626039019002896552675049930338911

HTTP 头(TTFB) 1,894 ms 2,876 ms 1,044 ms 858 ms 2,227 ms

首文本(SSE delta) 1,895 ms 2,877 ms 1,046 ms 860 ms 2,889 ms

总耗时 17,235 ms 16,384 ms 15,590 ms 19,479 ms 17,323 ms

输入 193 10 193 193 193

输出 2,912 2,509 2,540 3,708 3,041

缓存创建

5m: 0 / 1h: 0

5,980

5m: 0 / 1h: 5,980

5m: 0 / 1h: 0

缓存读取 0 0 0 0 0

输出速度 189.8 tok/s 185.8 tok/s 174.6 tok/s 199.2 tok/s 210.7 tok/s

响应

```json
{"n":72,"c":[10,3,10]}
```

I'll count the digits in the string `s=481016558291200489232896577226918223306626039019002896552675049930338911`

Breaking it down:
- **Length (n)**: 74 characters
- **Count of 0s**: 10
- **Count of 7

```json
{"n":72,"c":[10,3,10]}
```

```json
{"n":72,"c":[10,3,10]}
```

```json
{"n":72,"c":[10,3,10]}
```

总计

总输入(含缓存) 143,208 106,806 143,404 142,742 128,718

总输出 6,956 6,284 7,892 6,821 8,090

总缓存创建

42,820

5m: 0 / 1h: 42,820

38,739

5m: 0 / 1h: 38,739

4,164

5m: 0 / 1h: 4,164

42,342

5m: 0 / 1h: 42,342

39,650

5m: 0 / 1h: 39,650

总缓存读取 100,145 68,007 138,997 100,157 88,825

输出速度 175.6 tok/s 162.6 tok/s 155.5 tok/s 178.9 tok/s 157.2 tok/s

总执行时间 57.3s 1m 9.6s 1m 6.2s 53.6s 1m 49.5s

总墙钟时间

12m 39.1s

含步骤间等待

12m 19.4s

含步骤间等待

15m 51.0s

含步骤间等待

13m 34.1s

含步骤间等待

13m 57.1s

含步骤间等待

按官方价目重估

同等内容若直发 Anthropic 的估算 ⓘ

$0.0986

Anthropic: Claude Haiku 4.5

$0.0867

Anthropic: Claude Haiku 4.5

$0.0588

Anthropic: Claude Haiku 4.5

$0.0973

Anthropic: Claude Haiku 4.5

$0.0991

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%) direct (100%) direct (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 - - - -

ID 格式 msg_01QZnApSDihnSfjRUcbgQJEg msg_013gpsbTPYgKgxVXzS37BG3a msg_014F5wmsNsSKUaMUCK8QRqt7 msg_01GfT4jzqqaXFnecMQ3EFoob msg_01GPtuTDu2QU5CZptNpSHFTm

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-Zeus	via: 1.1 Caddy cf-ray: a0cbff59dcfadcfa-LAX server: cloudflare x-oneapi-request-id: 2026061702495878919356GCLskCQV	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-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	a0cbff59dcfadcfa-LAX
R-kiro	via: 1.1 google cf-ray: 9a7958ba38a51ad8723bbd9b009a180d-IAD server: nginx	id_format: msg_prefix via_chain: ["1.1 google"] extra_fields: ["stop_details"] has_service_tier: true has_inference_geo: true model_has_date_suffix: true usage_has_cache_fields: true	hdr:viabody:id:msg_prefixbody:usage.service_tierbody:usage.inference_geobody:model:date_suffixbody:usage.cache_fieldsbody:extra_fieldsupstream:anthropic-direct-from-body	req_9a7958ba38a51ad8723bbd9b009a180d 9a7958ba38a51ad8723bbd9b009a180d-IAD
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_011Cc7SkVnxYAUV5abQ5UZGj
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: 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_011Cc7SZtG1Kt2CUAbU8xxcA
cc	server: cdn x-new-api-version: v1.0.0-rc.11 x-oneapi-request-id: 202606161851139234420818268d9d6u0K0wE0q	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	—

协议指纹评分（机器自动）

总分 / 100 95 88 100 97 84

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 7.4 /10 ×20 6.5 /10 ×20 10.0 /10 ×20 7.5 /10 ×20 7.1 /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 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 10.0 /10 ×5

延迟基线 10.0 /10 ×5 6.2 /10 ×5 10.0 /10 ×5 10.0 /10 ×5 1.4 /10 ×5

Req-ID 透传 0.0 /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 95 v3.20.0 88 v3.20.0 基准（参考） 97 v3.20.0 84 v3.20.0

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 中文遵循 instruction_following_lang
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 8424 ms 10724 ms 10024 ms 7931 ms 17383 ms

输出速度 175.6 tok/s 162.6 tok/s 155.5 tok/s 178.9 tok/s 157.2 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 ⓘ

$0.0986

$0.0867

$0.0588

$0.0973

$0.0991

缓存读取占比 ⓘ 70% 64% 97% 70% 69%

揭晓

通道

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官方基准

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