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

2026/06/21 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

SSSAiCode

O-Max-L2

PackyCode

cc-Zeus

官方基准

ClaudeZZ

R-kiro

TopRouterCN

O-Max

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 02:59:26 ~ 02:59:31 (3.4s) 02:59:30 ~ 02:59:36 (4.1s) 02:59:36 ~ 02:59:57 (3.6s) 02:59:24 ~ 02:59:35 (5.5s) 02:59:28 ~ 02:59:39 (5.0s)

提示词

查看本步 Prompt

ping. Reply with the single word: pong

HTTP 头(TTFB) 1,947 ms 2,560 ms 2,240 ms 3,372 ms 3,989 ms

首文本(SSE delta) 2,287 ms 2,561 ms 2,241 ms 3,374 ms 3,990 ms

总耗时 2,322 ms 2,953 ms 2,568 ms 4,132 ms 4,063 ms

输入 10 10 10 10 1,645

输出 50 43 43 36 63

缓存创建

32,790

5m: 0 / 1h: 32,790

33,122

5m: 0 / 1h: 33,122

33,122

5m: 0 / 1h: 33,122

22,404

5m: 0 / 1h: 22,404

31,487

5m: 0 / 1h: 31,487

缓存读取 0 0 0 0 0

输出速度 burst 109.7 tok/s 131.5 tok/s 47.5 tok/s -

响应

pong

pong

pong

pong

pong

步骤 2: identity text

时间 03:01:28 ~ 03:01:38 (6.7s) 03:01:26 ~ 03:01:31 (4.8s) 03:02:13 ~ 03:02:23 (6.0s) 03:02:35 ~ 03:02:45 (8.4s) 03:01:52 ~ 03:02:02 (5.4s)

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) 1,578 ms 1,577 ms 2,179 ms 3,862 ms 4,586 ms

首文本(SSE delta) 5,645 ms 1,579 ms 2,181 ms 3,863 ms 4,587 ms

总耗时 5,829 ms 3,798 ms 4,923 ms 7,505 ms 4,667 ms

输入 10 10 10 10 10

输出 447 204 302 239 204

缓存创建

244

5m: 0 / 1h: 244

237

5m: 0 / 1h: 237

237

5m: 0 / 1h: 237

237

5m: 0 / 1h: 237

1,892

5m: 0 / 1h: 1,892

缓存读取 32,790 33,122 33,122 22,404 31,487

输出速度 burst 91.9 tok/s 110.1 tok/s 65.6 tok/s burst

响应

vendor: anthropic
brand: claude
model: claude-3-5-sonnet

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

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

步骤 3: cutoff text

时间 03:03:28 ~ 03:03:33 (3.2s) 03:04:54 ~ 03:04:59 (3.1s) 03:05:19 ~ 03:05:24 (3.8s) 03:06:44 ~ 03:06:54 (5.6s) 03:05:07 ~ 03:05:12 (3.6s)

提示词

查看本步 Prompt

你掌握的世界知识截止于何时？YYYY-MM 格式作答，不解释。

HTTP 头(TTFB) 1,666 ms 1,547 ms 2,338 ms 3,823 ms 2,758 ms

首文本(SSE delta) 2,313 ms 1,548 ms 2,340 ms 3,825 ms 2,759 ms

总耗时 2,352 ms 2,013 ms 2,798 ms 4,815 ms 2,850 ms

输入 10 10 10 10 10

输出 85 61 65 77 77

缓存创建

487

5m: 0 / 1h: 487

227

5m: 0 / 1h: 227

325

5m: 0 / 1h: 325

303

5m: 0 / 1h: 303

246

5m: 0 / 1h: 246

缓存读取 33,034 33,359 33,359 22,641 33,379

输出速度 burst 131.2 tok/s 141.9 tok/s 77.8 tok/s -

响应

2024-04

2025-02

2025-02

2025-02

2025-02

步骤 4: identity_free text

时间 03:06:15 ~ 03:06:20 (3.9s) 03:08:54 ~ 03:09:04 (6.1s) 03:06:55 ~ 03:07:05 (5.8s) 03:08:34 ~ 03:09:14 (39.6s) 03:09:05 ~ 03:09:15 (8.9s)

提示词

查看本步 Prompt

中文回答：你叫什么、属于哪个产品、谁开发？50 字内。

HTTP 头(TTFB) 1,767 ms 2,252 ms 2,406 ms 4,275 ms 7,828 ms

首文本(SSE delta) 2,450 ms 2,641 ms 2,407 ms 4,277 ms 7,829 ms

总耗时 3,007 ms 4,796 ms 4,762 ms 38,671 ms 7,910 ms

输入 10 10 10 10 10

输出 167 383 445 3,290 667

缓存创建

125

5m: 0 / 1h: 125

103

5m: 0 / 1h: 103

5m: 0 / 1h: 99

125

5m: 0 / 1h: 125

115

5m: 0 / 1h: 115

缓存读取 33,521 33,586 33,684 22,944 33,625

输出速度 burst 177.7 tok/s 189.0 tok/s 95.7 tok/s burst

响应

我叫 Claude，是由 Anthropic 公司开发的 AI 助手。属于 Claude 产品系列，基于大语言模型技术。我可以帮助用户进行对话、编程、分析等多种任务。

我是Anthropic开发的Claude Haiku 4.5 AI助手，可协助软件工程、代码编写、问题解答等工作。

我是Claude，由Anthropic公司开发的AI助手。我可以帮助用户编写代码、解答问题、分析文本等各种任务。

我是Claude Code，Anthropic的编程助手。基于其模型，帮助用户进行代码编写、调试、测试等。

我是Claude Haiku 4.5，由Anthropic公司打造的AI语言模型。是Claude系列中轻量级版本，用于快速高效地完成各类任务。

步骤 5: knowledge_recall text

时间 03:08:36 ~ 03:08:46 (7.9s) 03:12:20 ~ 03:12:35 (14.0s) 03:09:13 ~ 03:09:38 (24.4s) 03:10:47 ~ 03:11:42 (54.2s) 03:11:51 ~ 03:12:06 (12.9s)

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) 1,390 ms 1,656 ms 1,494 ms 3,704 ms 6,998 ms

首文本(SSE delta) 6,742 ms 1,657 ms 1,496 ms 3,706 ms 6,999 ms

总耗时 6,978 ms 12,776 ms 23,319 ms 53,310 ms 12,132 ms

输入 10 10 10 10 10

输出 1,025 1,440 3,339 4,183 1,626

缓存创建

8,431

5m: 0 / 1h: 8,431

8,690

5m: 0 / 1h: 8,690

8,846

5m: 0 / 1h: 8,846

11,841

5m: 0 / 1h: 11,841

9,025

5m: 0 / 1h: 9,025

缓存读取 0 0 0 0 0

输出速度 burst 129.5 tok/s 153.0 tok/s 84.3 tok/s burst

响应

{"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

```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

```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 03:12:16 ~ 03:12:41 (21.6s) 03:15:45 ~ 03:16:15 (29.6s) 03:13:18 ~ 03:13:44 (23.4s) 03:15:43 ~ 03:16:38 (52.2s) 03:13:23 ~ 03:13:53 (25.2s)

提示词

查看本步 Prompt

Count digits exactly in s. Return {"n":len(s),"c":[count0,count7,count9]}. s=808689797691997446870170002214302735314336164157433585794192095028134485

HTTP 头(TTFB) 964 ms 939 ms 2,234 ms 3,103 ms 5,935 ms

首文本(SSE delta) 966 ms 940 ms 2,236 ms 3,105 ms 5,936 ms

总耗时 20,838 ms 28,515 ms 22,412 ms 51,286 ms 24,373 ms

输入 139 193 193 10 193

输出 4,136 5,437 3,751 4,743 4,514

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 208.1 tok/s 197.2 tok/s 185.9 tok/s 98.4 tok/s 244.8 tok/s

响应

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

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

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

I'll count the digits 0, 7, and 9 in the string.

String: `808689797691997446870170002214302735314336164157433585794192095028134485`

**Counts:**
- Length (n): 72
- Count of 0s: 8 (positions 2, 21, 24

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

总计

总输入(含缓存) 141,611 142,689 143,037 108,939 143,134

总输出 5,910 7,568 7,945 12,568 7,151

总缓存创建

42,077

5m: 0 / 1h: 42,077

42,379

5m: 0 / 1h: 42,379

42,629

5m: 0 / 1h: 42,629

40,890

5m: 0 / 1h: 40,890

42,765

5m: 0 / 1h: 42,765

总缓存读取 99,345 100,067 100,165 67,989 98,491

输出速度 208.1 tok/s 172.3 tok/s 165.9 tok/s 91.4 tok/s 244.8 tok/s

总执行时间 46.6s 1m 1.8s 1m 7.0s 2m 45.5s 1m 1.0s

总墙钟时间

13m 14.9s

含步骤间等待

16m 45.0s

含步骤间等待

14m 7.1s

含步骤间等待

17m 13.3s

含步骤间等待

14m 25.0s

含步骤间等待

按官方价目重估

同等内容若直发 Anthropic 的估算 ⓘ

$0.0923

Anthropic: Claude Haiku 4.5

$0.1011

Anthropic: Claude Haiku 4.5

$0.1033

Anthropic: Claude Haiku 4.5

$0.1208

Anthropic: Claude Haiku 4.5

$0.1009

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

ID 格式 msg_01PmEZuSJrq8fHU8kzPkmLZU msg_01SjPV9sgG3mB87dgDYbBzQf msg_01KsycGZLK5b8rv1dpvwecwp msg_01FGZMLzkzvNB4J6QyofbwCD msg_01MFebtq2Av9FfvWcVWc2KtU

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-L2	cf-ray: a0ed158f4f4ee41a-LAX server: Photon-Edge set-cookie: _cfuvid=NOQCQh86vx9YR1wm_8TDOELCanYqNH3s_sEg.6mX1fw-1781982737.8087394-1.0.1.1-WqrxlhwSNDbnTQcepjQh7mjrUPCOs4wD5N.kUvuQMbI; 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	1781982737761-tn37e5jsccs9b req_011CcF3SG2KtmXiCYuKiiBNG a0ed158f4f4ee41a-LAX
cc-Zeus	via: 1.1 Caddy cf-ray: a0ed1aab4d6934db-LAX server: cloudflare x-oneapi-request-id: 202606210315471103304219ijce7WB	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	a0ed1aab4d6934db-LAX
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_011CcF3WrMBYGPm9GFjQEuXy
R-kiro	via: 1.1 google cf-ray: 851409596fd359b3d1b46da36c8b75e3-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_851409596fd359b3d1b46da36c8b75e3 851409596fd359b3d1b46da36c8b75e3-IAD
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: 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	tr-ew7y4grd-bnfxa1qgw8-111038

协议指纹评分（机器自动）

总分 / 100 83 98 100 92 97

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 5.0 /10 ×8 10.0 /10 ×8 10.0 /10 ×8 0.0 /10 ×8 10.0 /10 ×8

身份(结构) 6.7 /10 ×7 10.0 /10 ×7 10.0 /10 ×7 10.0 /10 ×7 10.0 /10 ×7

知识截止 0.0 /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 10.0 /10 ×5 10.0 /10 ×5 9.9 /10 ×5 8.0 /10 ×5

Req-ID 透传 10.0 /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 83 v3.24.0 98 v3.24.0 基准（参考） 92 v3.24.0 97 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 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 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 6888 ms 9142 ms 10130 ms 26620 ms 9333 ms

输出速度 208.1 tok/s 172.3 tok/s 165.9 tok/s 91.4 tok/s 244.8 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 ⓘ

$0.0923

$0.1011

$0.1033

$0.1208

$0.1009

缓存读取占比 ⓘ 70% 70% 70% 62% 69%

揭晓

通道

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官方基准

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