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

2026/06/23 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

ClaudeAPI

O-Bdrk

FoxCode

R-ultra

官方基准

LingxiCode

M-

WorldBase.ai

M-

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 21:15:15 ~ 21:17:01 (101.3s) 21:15:11 ~ 21:15:21 (5.0s) 21:15:17 ~ 21:15:27 (6.6s) 21:15:09 ~ 21:15:24 (14.3s) 21:15:13 ~ 21:15:28 (10.6s)

提示词

查看本步 Prompt

ping. Reply with the single word: pong

HTTP 头(TTFB) 65,957 ms 2,886 ms 4,761 ms 12,059 ms 6,549 ms

首文本(SSE delta) - 2,888 ms 4,766 ms 12,129 ms 6,622 ms

总耗时 65,958 ms 3,774 ms 5,289 ms 13,151 ms 8,551 ms

输入 10 33,020 10 10 14

输出 53 45 55 51 112

缓存创建

32,406

5m: 32,406 / 1h: 0

5m: 0 / 1h: 0

33,122

5m: 0 / 1h: 33,122

33,122

5m: 0 / 1h: 33,122

32,613

5m: 0 / 1h: 32,613

缓存读取 0 0 0 0 0

输出速度 burst 50.8 tok/s 105.2 tok/s 49.9 tok/s 58.1 tok/s

响应

pong

pong

pong

pong

pong

步骤 2: identity text

时间 21:21:00 ~ 21:21:10 (7.2s) 21:18:50 ~ 21:18:55 (4.7s) 21:17:59 ~ 21:18:15 (10.2s) 21:17:07 ~ 21:17:18 (6.6s) 21:17:39 ~ 21:17:49 (8.1s)

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。严格按以下 3 行格式输出，全部小写英文，不要任何其他文字、解释、标点：
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) 3,925 ms 2,030 ms 2,017 ms 2,772 ms 3,122 ms

首文本(SSE delta) 3,926 ms 2,031 ms 2,019 ms 2,773 ms 3,836 ms

总耗时 5,591 ms 3,841 ms 9,085 ms 4,664 ms 6,216 ms

输入 10 33,573 10 10 240

输出 176 203 266 207 371

缓存创建

191

5m: 191 / 1h: 0

5m: 0 / 1h: 0

248

5m: 0 / 1h: 248

244

5m: 0 / 1h: 244

32,760

5m: 0 / 1h: 32,760

缓存读取 32,406 0 33,122 33,122 0

输出速度 105.7 tok/s 112.2 tok/s 37.6 tok/s 109.5 tok/s 155.9 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 21:23:08 ~ 21:23:18 (5.0s) 21:22:27 ~ 21:22:32 (3.5s) 21:21:48 ~ 21:21:58 (6.2s) 21:18:23 ~ 21:18:28 (4.5s) 21:21:08 ~ 21:21:18 (6.5s)

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) 2,799 ms 1,896 ms 3,890 ms 2,826 ms 4,400 ms

首文本(SSE delta) 2,855 ms 1,897 ms 3,920 ms 2,827 ms 4,563 ms

总耗时 3,453 ms 2,593 ms 5,080 ms 3,388 ms 5,091 ms

输入 10 34,126 10 10 31,448

输出 68 53 82 62 66

缓存创建

5m: 61 / 1h: 0

5m: 0 / 1h: 0

289

5m: 0 / 1h: 289

230

5m: 0 / 1h: 230

1,987

5m: 0 / 1h: 1,987

缓存读取 32,597 0 33,370 33,366 0

输出速度 113.7 tok/s 76.1 tok/s 70.7 tok/s 110.5 tok/s 125.0 tok/s

响应

2025-02

2025-02

2025-02

2025-02

2025-02

步骤 4: identity_free text

时间 21:26:58 ~ 21:27:13 (11.1s) 21:26:21 ~ 21:26:36 (10.1s) 21:25:26 ~ 21:25:36 (8.6s) 21:21:34 ~ 21:21:44 (6.2s) 21:23:03 ~ 21:23:13 (6.6s)

提示词

查看本步 Prompt

用中文说说你的身份：什么产品？由谁打造？50 字以内。

HTTP 头(TTFB) 3,024 ms 2,559 ms 1,975 ms 3,084 ms 3,551 ms

首文本(SSE delta) 3,025 ms 2,560 ms 2,001 ms 3,085 ms 3,552 ms

总耗时 9,060 ms 9,080 ms 7,550 ms 5,128 ms 5,368 ms

输入 10 34,532 10 10 31,448

输出 831 1,604 419 359 137

缓存创建

32,697

5m: 32,697 / 1h: 0

5m: 0 / 1h: 0

122

5m: 0 / 1h: 122

5m: 0 / 1h: 96

2,104

5m: 0 / 1h: 2,104

缓存读取 0 0 33,659 33,596 0

输出速度 137.7 tok/s 246.0 tok/s 75.5 tok/s 175.7 tok/s 75.4 tok/s

响应

我是Claude Haiku 4.5，由Anthropic公司打造的AI助手。专长是代码编写、分析调试等软件工程任务。

我是Claude，Anthropic开发的AI助手。专长编程、写作、分析。追求有帮助、诚实、无害。

我叫Claude Haiku 4.5，是由Anthropic公司开发的AI助手。我属于Claude产品家族，这是Anthropic创建的大型语言模型系列。

我是Claude，一个由Anthropic公司开发的AI助手。我可以帮助用户进行编程、分析、创意写作等多种任务。

我是Claude Code，由Anthropic公司开发的AI助手。属于Claude系列产品，基于Claude Haiku 4.5模型，提供代码编辑、软件工程任务协助等功能。

步骤 5: knowledge_recall text

时间 21:30:00 ~ 21:30:46 (43.6s) 21:28:27 ~ 21:28:47 (17.3s) 21:27:01 ~ 21:27:31 (29.7s) 21:25:43 ~ 21:26:08 (23.0s) 21:26:13 ~ 21:26:28 (12.4s)

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) 7,231 ms 1,812 ms 1,401 ms 4,578 ms 2,410 ms

首文本(SSE delta) 7,233 ms 1,813 ms 1,404 ms 4,604 ms 2,412 ms

总耗时 40,894 ms 16,142 ms 28,398 ms 21,917 ms 11,245 ms

输入 10 11,489 10 10 753

输出 5,125 2,571 2,100 2,579 1,382

缓存创建

8,072

5m: 8,072 / 1h: 0

5m: 0 / 1h: 0

8,818

5m: 0 / 1h: 8,818

8,669

5m: 0 / 1h: 8,669

265

5m: 0 / 1h: 265

缓存读取 0 0 0 0 8,420

输出速度 152.3 tok/s 179.4 tok/s 77.8 tok/s 149.0 tok/s 156.5 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 21:33:31 ~ 21:34:21 (46.8s) 21:31:02 ~ 21:31:12 (7.1s) 21:29:58 ~ 21:30:13 (13.9s) 21:29:14 ~ 21:29:49 (33.8s) 21:30:09 ~ 21:30:29 (15.7s)

提示词

查看本步 Prompt

Count digits exactly in s. Return {"n":len(s),"c":[count0,count7,count9]}. s=374038204085234097079562340572388014584916498256177702548244032600361738

HTTP 头(TTFB) 6,698 ms 1,029 ms 1,647 ms 2,062 ms 1,989 ms

首文本(SSE delta) 6,845 ms 1,031 ms 1,649 ms 2,064 ms 2,213 ms

总耗时 44,773 ms 5,997 ms 12,541 ms 32,609 ms 13,357 ms

输入 238 210 193 193 206

输出 2,833 1,082 1,922 3,588 1,420

缓存创建

5m: 0 / 1h: 0

缓存读取 0 0 0 0 0

输出速度 74.7 tok/s 217.9 tok/s 176.5 tok/s 117.5 tok/s 127.4 tok/s

响应

```json
{
  "n": 72,
  "c": [11, 8, 4]
}
```

```json
{"n":72,"c":[11,8,4]}
```

```json
{"n":72,"c":[11,8,4]}
```

```json
{"n": 72, "c": [11, 8, 4]}
```

```json
{"n":72,"c":[11,8,4]}
```

总计

总输入(含缓存) 138,718 146,950 142,993 142,688 142,258

总输出 9,086 5,558 4,844 6,846 3,488

总缓存创建

73,427

5m: 73,427 / 1h: 0

5m: 0 / 1h: 0

42,599

5m: 0 / 1h: 42,599

42,361

5m: 0 / 1h: 42,361

69,729

5m: 0 / 1h: 69,729

总缓存读取 65,003 0 100,151 100,084 8,420

输出速度 113.1 tok/s 190.3 tok/s 92.8 tok/s 128.3 tok/s 131.0 tok/s

总执行时间 3m 35.0s 47.7s 1m 15.1s 1m 28.4s 59.8s

总墙钟时间

19m 5.6s

含步骤间等待

16m 1.2s

含步骤间等待

14m 56.1s

含步骤间等待

14m 40.1s

含步骤间等待

15m 15.3s

含步骤间等待

按官方价目重估

同等内容若直发 Anthropic 的估算 ⓘ

$0.1096

Anthropic: Claude Haiku 4.5

$0.1747

Anthropic: Claude Haiku 4.5

$0.0877

Anthropic: Claude Haiku 4.5

$0.0974

Anthropic: Claude Haiku 4.5

$0.1696

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

ID 格式 msg_01UUnjnMoRwjugDGJAfHPTZj msg_01562hWMXgnzR9m7usLO5tPM msg_011rS8YrZF4GNrENnKSdMq33 msg_01RSRe8utbLDax5WU8XvKUrt msg_hqZdTpF2vwNckD42GGjduAeC

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-Bdrk	x-new-api-version: v1.0.0-rc.11 x-oneapi-request-id: 202606231333333884038598268d9d6ra3qDlqe	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	—
R-ultra	cf-ray: a103d9d64ccb7343-NRT server: cloudflare x-new-api-version: v0.0.0 x-oneapi-request-id: 202606231331041505549048268d9d6Hl87SZwB	id_format: msg_prefix 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-idhdr:server:cloudflarehdr:cf-raybody:id:msg_prefixbody:usage.service_tierbody:usage.inference_geobody:model:date_suffixbody:usage.cache_fieldsupstream:anthropic-direct-from-body	a103d9d64ccb7343-NRT
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_011CcLGm8YgQjkEozZeFzTSU
M-	cf-ray: a103d732ec07fd4f-NRT server: cloudflare set-cookie: server_name_session=7f3cfa97a5a639f0ea1fc1ec7206ed9c; Max-Age=86400; httponly; path=/ x-oneapi-request-id: 20260623132916891118147I85VqoT5	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-oneapi-request-idhdr: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	a103d732ec07fd4f-NRT
M-	cf-ray: a103d88d0c51931d-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	d9e74b82-c8a9-4e43-ae9f-dab1a6d57cdc a103d88d0c51931d-AMS

协议指纹评分（机器自动）

总分 / 100 81 66 100 98 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 5.6 /10 ×20 0.0 /10 ×20 10.0 /10 ×20 10.0 /10 ×20 0.0 /10 ×20

缓存 TTL 一致 0.0 /10 ×15 0.0 /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 5.0 /10 ×14 10.0 /10 ×14 10.0 /10 ×14 10.0 /10 ×14

sliding 5m 7.7 /10 ×13 5.0 /10 ×13 10.0 /10 ×13 10.0 /10 ×13 0.0 /10 ×13

原生 msg-ID 10.0 /10 ×8 10.0 /10 ×8 10.0 /10 ×8 10.0 /10 ×8 0.0 /10 ×8

系统提示纯净 5.0 /10 ×8 10.0 /10 ×8 10.0 /10 ×8 10.0 /10 ×8 5.0 /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 8.1 /10 ×5 10.0 /10 ×5 8.9 /10 ×5 10.0 /10 ×5 7.4 /10 ×5

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

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 81 v3.24.0 66 v3.24.0 基准（参考） 98 v3.24.0 69 v3.24.0

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 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 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 28288 ms 6905 ms 11324 ms 13476 ms 8305 ms

输出速度 113.1 tok/s 190.3 tok/s 92.8 tok/s 128.3 tok/s 131.0 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 ⓘ

$0.1096

$0.1747

$0.0877

$0.0974

$0.1696

缓存读取占比 ⓘ 47% 0% 70% 70% 6%

揭晓

通道

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服务商 FoxCode ↗

通道R-ultra

官方基准

服务商 LingxiCode ↗

通道M-

服务商 WorldBase.ai ↗

通道M-

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