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

2026/06/20 运行日期

claude-sonnet-4-6 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

官方基准

Aiide

O-Max

LingxiCode

M-

HongMaCC

u-cc

WorldBase.ai

M-

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 23:40:55 ~ 23:41:00 (4.4s) 23:40:53 ~ 23:41:08 (10.3s) 23:40:49 ~ 23:40:59 (5.7s) 23:40:47 ~ 23:40:57 (7.5s) 23:40:51 ~ 23:41:01 (7.0s)

提示词

查看本步 Prompt

ping. Reply with the single word: pong

HTTP 头(TTFB) 2,970 ms 7,213 ms 3,940 ms 6,054 ms 5,248 ms

首文本(SSE delta) 2,972 ms 7,215 ms 3,944 ms 6,056 ms 5,249 ms

总耗时 3,080 ms 8,205 ms 4,313 ms 6,056 ms 5,319 ms

输入 3 3 3 3 1,696

输出 5 5 5 5 2

缓存创建

32,318

5m: 0 / 1h: 32,318

32,457

5m: 0 / 1h: 32,457

32,318

5m: 0 / 1h: 32,318

28,384

5m: 0 / 1h: 28,384

32,206

5m: 32,206 / 1h: 0

缓存读取 0 0 0 0 0

响应

pong

pong

pong

pong

pong

步骤 2: identity text

时间 23:43:38 ~ 23:43:48 (5.2s) 23:44:01 ~ 23:44:11 (7.4s) 23:42:01 ~ 23:42:11 (6.5s) 23:42:10 ~ 23:42:20 (5.4s) 23:44:50 ~ 23:45:00 (9.5s)

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。请只输出以下 3 行内容（label 小写，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) 3,722 ms 4,242 ms 4,838 ms 3,957 ms 7,784 ms

首文本(SSE delta) 3,724 ms 4,904 ms 4,839 ms 3,958 ms 8,023 ms

总耗时 4,184 ms 4,904 ms 5,470 ms 3,958 ms 8,024 ms

输入 3 3 3 3 185

输出 22 22 22 22 21

缓存创建

178

5m: 0 / 1h: 178

178

5m: 0 / 1h: 178

178

5m: 0 / 1h: 178

178

5m: 0 / 1h: 178

35,311

5m: 0 / 1h: 35,311

缓存读取 32,318 32,457 32,318 28,384 0

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 23:47:19 ~ 23:47:29 (5.6s) 23:46:55 ~ 23:47:05 (9.5s) 23:44:46 ~ 23:44:51 (4.9s) 23:46:04 ~ 23:46:14 (5.6s) 23:47:21 ~ 23:47:51 (26.6s)

提示词

查看本步 Prompt

Knowledge cutoff date? Reply with YYYY-MM only.

HTTP 头(TTFB) 4,389 ms 7,847 ms 3,356 ms 4,222 ms 24,641 ms

首文本(SSE delta) 4,515 ms 8,007 ms 3,358 ms 4,224 ms 24,642 ms

总耗时 4,633 ms 8,070 ms 3,604 ms 4,224 ms 24,793 ms

输入 3 3 3 3 34

输出 8 8 8 8 4

缓存创建

5m: 0 / 1h: 38

5m: 0 / 1h: 55

5m: 0 / 1h: 38

5m: 0 / 1h: 0

缓存读取 32,496 32,635 32,496 28,562 35,514

响应

2025-08

2025-08

2025-08

2025-08

2025-08

步骤 4: identity_free text

时间 23:49:26 ~ 23:49:36 (5.9s) 23:49:24 ~ 23:49:34 (5.9s) 23:48:22 ~ 23:48:32 (5.5s) 23:49:13 ~ 23:49:23 (5.0s) 23:49:28 ~ 23:49:38 (6.9s)

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) 4,063 ms 3,513 ms 3,547 ms 3,814 ms 3,455 ms

首文本(SSE delta) 4,065 ms 3,515 ms 3,548 ms 3,815 ms 3,456 ms

总耗时 4,696 ms 4,676 ms 4,598 ms 3,816 ms 4,212 ms

输入 3 3 3 3 35

输出 41 50 55 61 39

缓存创建

5m: 0 / 1h: 39

5m: 0 / 1h: 43

5m: 0 / 1h: 35

5m: 0 / 1h: 43

5m: 0 / 1h: 38

缓存读取 32,534 32,690 32,534 28,600 35,504

输出速度 65.0 tok/s 43.1 tok/s 52.4 tok/s burst 51.6 tok/s

响应

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。我能帮助回答问题、分析代码、写作、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、有益的对话和任务协助。

我是 Claude，由 Anthropic 开发的 AI 助手。当前运行的是 Claude Code，Anthropic 官方命令行工具，帮助开发者完成软件工程任务。

我叫 Claude，是由 Anthropic 开发的 AI 助手。当前版本为 Claude Sonnet 4.6，属于 Claude 4 系列产品。

步骤 5: knowledge_recall text

时间 23:52:38 ~ 23:52:43 (4.7s) 23:51:39 ~ 23:51:49 (8.6s) 23:50:43 ~ 23:50:54 (6.6s) 23:51:37 ~ 23:51:47 (6.3s) 23:52:46 ~ 23:53:01 (14.2s)

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) 1,878 ms 2,789 ms 2,604 ms 5,083 ms 7,730 ms

首文本(SSE delta) 1,880 ms 2,790 ms 2,605 ms 5,084 ms 7,731 ms

总耗时 3,680 ms 4,784 ms 5,301 ms 5,085 ms 11,640 ms

输入 3 3 3 3 775

输出 204 205 205 205 183

缓存创建

8,003

5m: 0 / 1h: 8,003

8,033

5m: 0 / 1h: 8,033

8,013

5m: 0 / 1h: 8,013

4,188

5m: 0 / 1h: 4,188

5m: 0 / 1h: 0

缓存读取 0 0 0 11,447 8,751

输出速度 113.3 tok/s 102.8 tok/s 76.0 tok/s burst 46.8 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 23:54:23 ~ 23:54:43 (18.3s) 23:52:58 ~ 23:53:18 (16.5s) 23:54:38 ~ 23:55:03 (21.3s) 23:54:10 ~ 23:54:35 (23.6s) 23:54:07 ~ 23:54:32 (22.0s)

提示词

查看本步 Prompt

Count digits exactly in s. Return {"n":len(s),"c":[count0,count7,count9]}. s=298611264104282071362790466743564827843390607844349274670754507553948260

HTTP 头(TTFB) 2,203 ms 1,919 ms 1,956 ms 22,359 ms 5,606 ms

首文本(SSE delta) 2,205 ms 2,477 ms 2,368 ms 22,360 ms 5,608 ms

总耗时 16,970 ms 15,192 ms 20,132 ms 22,360 ms 19,942 ms

输入 164 164 164 3 199

输出 1,329 1,310 1,579 1,976 522

缓存创建

5m: 0 / 1h: 0

2,016

5m: 0 / 1h: 2,016

5m: 0 / 1h: 0

缓存读取 0 0 0 11,447 0

输出速度 90.0 tok/s 103.0 tok/s 88.9 tok/s burst 36.4 tok/s

响应

## Counting digits in s

**s** = `298611264104282071362790466743564827843390607844349274670754507553948260`

### Length
Splitting into groups of 10:
`2986112641` `0428207136` `2790466743` `5648278433`

I'll parse s digit-by-digit, indexed 1–72:

**s:** 2 9 8 6 1 1 2 6 4 1 **0** 4 2 8 2 **0** **7** 1 3 6 2 **7** 9 **0** 4 6 6 **7** 4 3 5 6 4 8 2 **7** 8 4 3 3 9 **0** 6 **0** **7** 8 4 4 3 4 9 2 **7**

## Counting digits in s

**s** = `298611264104282071362790466743564827843390607844349274670754507553948260`

### Length Count (groups of 10)
```
2986112641  → 10
0428207136  → 20
2790466743  → 30
5648

I'll work through the string methodically, indexing each character:

**String:** `298611264104282071362790466743564827843390607844349274670754507553948260`

**Position-by-position:**

| # | d | # | d


```
s = "298611264104282071362790466743564827843390607844349274670754507553948260"
```

Indexing each character:

| # | digit | | # | digit | | # | digit |
|---|-------|-|---|-------|-|---|-------|

总计

总输入(含缓存) 138,103 138,727 138,109 143,305 150,248

总输出 1,609 1,600 1,874 2,277 771

总缓存创建

40,576

5m: 0 / 1h: 40,576

40,766

5m: 0 / 1h: 40,766

40,582

5m: 0 / 1h: 40,582

34,847

5m: 0 / 1h: 34,847

67,555

5m: 32,206 / 1h: 35,349

总缓存读取 97,348 97,782 97,348 108,440 79,769

输出速度 91.5 tok/s 98.6 tok/s 85.5 tok/s burst 39.2 tok/s

总执行时间 44.1s 58.2s 50.5s 53.3s 1m 26.3s

总墙钟时间

13m 48.0s

含步骤间等待

12m 25.0s

含步骤间等待

14m 14.0s

含步骤间等待

13m 48.1s

含步骤间等待

13m 41.1s

含步骤间等待

按官方价目重估

同等内容若直发 Anthropic 的估算 ⓘ

$0.2060

Anthropic: Claude Sonnet 4.6

$0.2067

Anthropic: Claude Sonnet 4.6

$0.2100

Anthropic: Claude Sonnet 4.6

$0.1974

Anthropic: Claude Sonnet 4.6

$0.2523

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%) one-api (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 - cloudflare

ID 格式 msg_01TshUN7ksWqThGykv2Q5vrx msg_01XLATscDG9D5fd7aeXobNTr msg_01QCmKdiQqAwypbeX3NuLkJd msg_01J5WMhVaApkd83yALsVWrLc msg_012ck3hUwh6CpzqMGRZaBsKh

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
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_011CcEnM3kFmPPWzsPueg4BB
O-Max	cf-ray: a0ebf19cce1b66e5-AMS server: cloudflare x-new-api-version: 0.12.11 x-oneapi-request-id: 202606201552599042879938268d9d62H1MBmoS	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	a0ebf19cce1b66e5-AMS
M-	cf-ray: a0ebf40bec56fd4f-NRT server: cloudflare set-cookie: server_name_session=34dafb6a1ed616f236acca93e01021ee; Max-Age=86400; httponly; path=/ x-oneapi-request-id: 20260620155439485127664YJT26uRh	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	a0ebf40bec56fd4f-NRT
u-cc	server: nginx x-new-api-version: 1.0.14 x-oneapi-request-id: 202606201554115639269638268d9d6Wk9v444Q	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-idbody:id:msg_prefixbody:usage.service_tierbody:usage.inference_geobody:usage.cache_fieldsbody:extra_fieldsupstream:anthropic-direct-from-body	—
M-	cf-ray: a0ebf3511fbb4a25-AMS server: cloudflare	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:server:cloudflarehdr:cf-raybody:id:msg_prefixbody:usage.service_tierbody:usage.inference_geobody:usage.cache_fieldsbody:extra_fieldsupstream:anthropic-direct-from-body	b86fe8b7-ca4b-4aa8-b438-a72f1f60311a a0ebf3511fbb4a25-AMS

协议指纹评分（机器自动）

总分 / 100 99 98 98 90 83

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 8.4 /10 ×20

缓存 TTL 一致 10.0 /10 ×15 10.0 /10 ×15 10.0 /10 ×15 10.0 /10 ×15 7.0 /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 7.7 /10 ×13

原生 msg-ID 10.0 /10 ×8 10.0 /10 ×8 10.0 /10 ×8 10.0 /10 ×8 7.0 /10 ×8

系统提示纯净 10.0 /10 ×8 10.0 /10 ×8 10.0 /10 ×8 0.0 /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 10.0 /10 ×5 10.0 /10 ×5 10.0 /10 ×5 5.0 /10 ×5

流式投递 10.0 /10 ×5 10.0 /10 ×5 10.0 /10 ×5 0.0 /10 ×5 10.0 /10 ×5

Req-ID 透传 10.0 /10 ×4 0.0 /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

与基线相似度基准（参考） 98 v3.24.0 98 v3.24.0 90 v3.24.0 83 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 6207 ms 7639 ms 7236 ms 7583 ms 12322 ms

输出速度 91.5 tok/s 98.6 tok/s 85.5 tok/s burst 39.2 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 ⓘ

$0.2060

$0.2067

$0.2100

$0.1974

$0.2523

缓存读取占比 ⓘ 70% 70% 70% 76% 53%

揭晓

通道

官方基准

服务商 Aiide ↗

通道O-Max

服务商 LingxiCode ↗

通道M-

服务商 HongMaCC ↗

通道u-cc

服务商 WorldBase.ai ↗

通道M-

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