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。

3 通道 · 6 步骤

2026/06/25 运行日期

claude-opus-4-8 官方基准

1 通道分数 < 70 另含 2 任务失败

已揭晓揭晓进度

⚠ 2 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
```
DawAPI / cc
```
评测失败
```

步骤 / 指标

官方基准

FoxCode

LingxiCode

M-

基本信息

服务名 Claude Code Claude Code Claude Code

模型 claude-opus-4-8 claude-opus-4-8 claude-opus-4-8

客户端 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 17:43:15 ~ 17:43:25 (5.1s) 17:43:13 ~ 17:43:23 (5.7s) 17:43:07 ~ 17:43:17 (8.2s)

提示词

查看本步 Prompt

ping. Reply with the single word: pong

HTTP 头(TTFB) 3,699 ms 3,482 ms 4,021 ms

首文本(SSE delta) 3,700 ms 3,484 ms 4,033 ms

总耗时 3,866 ms 3,684 ms 4,550 ms

输入 1,454 1,454 1,454

输出 4 4 4

缓存创建

30,222

5m: 0 / 1h: 30,222

30,222

5m: 30,222 / 1h: 0

30,224

5m: 0 / 1h: 30,224

缓存读取 0 0 0

响应

pong

pong

pong

步骤 2: identity text

时间 17:44:41 ~ 17:44:51 (6.3s) 17:45:10 ~ 17:45:15 (4.1s) 17:44:28 ~ 17:44:33 (4.4s)

提示词

查看本步 Prompt

输出 3 行 metadata 描述你自己（当前 API 模型）。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。禁止任何额外字符：
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) 4,916 ms 2,674 ms 2,904 ms

首文本(SSE delta) 4,918 ms 2,675 ms 2,905 ms

总耗时 5,308 ms 3,126 ms 3,618 ms

输入 2 2 2

输出 28 28 28

缓存创建

1,643

5m: 0 / 1h: 1,643

1,643

5m: 1,643 / 1h: 0

1,643

5m: 0 / 1h: 1,643

缓存读取 30,222 30,222 30,224

响应

vendor: anthropic
brand: claude
model: claude-opus-4-8

vendor: anthropic
brand: claude
model: claude-opus-4-8

vendor: anthropic
brand: claude
model: claude-opus-4-8

步骤 3: cutoff text

时间 17:47:05 ~ 17:47:10 (3.0s) 17:48:39 ~ 17:48:49 (5.2s) 17:46:30 ~ 17:46:35 (4.4s)

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) 1,822 ms 3,970 ms 2,819 ms

首文本(SSE delta) 1,824 ms 3,974 ms 2,820 ms

总耗时 2,017 ms 4,236 ms 3,518 ms

输入 2 2 2

输出 6 6 6

缓存创建

5m: 0 / 1h: 64

31,919

5m: 31,919 / 1h: 0

5m: 0 / 1h: 64

缓存读取 31,865 0 31,867

响应

2026-01

2026-01

2026-01

步骤 4: identity_free text

时间 17:49:18 ~ 17:49:28 (5.7s) 17:49:58 ~ 17:50:08 (5.9s) 17:49:03 ~ 17:49:13 (6.6s)

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) 3,964 ms 4,297 ms 4,628 ms

首文本(SSE delta) 3,966 ms 4,300 ms 4,630 ms

总耗时 4,825 ms 5,148 ms 5,685 ms

输入 2 2 2

输出 64 49 64

缓存创建

5m: 0 / 1h: 31

5m: 37 / 1h: 0

5m: 0 / 1h: 35

缓存读取 31,929 31,919 31,931

输出速度 74.5 tok/s 57.8 tok/s 60.7 tok/s

响应

我是 Claude，一个由 Anthropic 开发的人工智能助手。我的 API 模型是 claude-opus-4-8，能帮你处理软件工程及各类任务。

我是 Claude，属于 Claude 产品系列，由 Anthropic 开发。具体型号为 Claude Opus 4.8。

我是 Claude，由 Anthropic 公司打造的人工智能助手，当前模型为 Claude Opus 4.8，通过 Claude Agent SDK 提供服务。

步骤 5: knowledge_recall text

时间 17:51:12 ~ 17:51:27 (10.7s) 17:53:21 ~ 17:53:31 (9.2s) 17:50:27 ~ 17:50:37 (9.5s)

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) 3,626 ms 1,469 ms 2,900 ms

首文本(SSE delta) 7,406 ms 5,731 ms 6,242 ms

总耗时 9,645 ms 8,381 ms 8,733 ms

输入 2 2 2

输出 705 721 677

缓存创建

4,778

5m: 0 / 1h: 4,778

4,759

5m: 4,759 / 1h: 0

4,784

5m: 0 / 1h: 4,784

缓存读取 0 0 0

输出速度 burst burst burst

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

步骤 6: digit_count text

时间 17:53:41 ~ 17:53:46 (3.0s) 17:56:38 ~ 17:56:43 (2.7s) 17:54:26 ~ 17:54:31 (3.0s)

提示词

查看本步 Prompt

Count digits exactly in s. Return {"n":len(s),"c":[count0,count7,count9]}. s=370908219246847940409221526530951399282883745070863243545754063673537552

HTTP 头(TTFB) 1,701 ms 1,409 ms 1,407 ms

首文本(SSE delta) 1,704 ms 1,410 ms 1,409 ms

总耗时 2,032 ms 1,843 ms 2,075 ms

输入 211 211 211

输出 19 19 19

缓存创建

5m: 0 / 1h: 0

缓存读取 0 0 0

响应

{"n":72,"c":[7,5,5]}

{"n":72,"c":[9,5,8]}

{"n":72,"c":[8,6,8]}

总计

总输入(含缓存) 132,427 132,394 132,445

总输出 826 827 798

总缓存创建

36,738

5m: 0 / 1h: 36,738

68,580

5m: 68,580 / 1h: 0

36,750

5m: 0 / 1h: 36,750

总缓存读取 94,016 62,141 94,022

输出速度 74.5 tok/s 57.8 tok/s 60.7 tok/s

总执行时间 33.9s 32.8s 36.1s

总墙钟时间

10m 30.9s

含步骤间等待

13m 30.0s

含步骤间等待

11m 23.8s

含步骤间等待

按官方价目重估

同等内容若直发 Anthropic 的估算 ⓘ

$0.3056

Anthropic: Claude Opus 4.8

$0.3280

Anthropic: Claude Opus 4.8

$0.3050

Anthropic: Claude Opus 4.8

通道指纹

来源步骤 digit_count digit_count digit_count

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

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

CDN - cloudflare cloudflare

ID 格式 msg_01D5jmwEbhDB7yvKJdgM8owb msg_01PnYi5CwnfjGWoGzBkPoRxf msg_014MT3rfghN5s6QU9j8QSgGS

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_011CcPmtHjQkq33TWaqSHBVb

cf-ray: a1131a823fd0e041-NRT
server: cloudflare
set-cookie: _cfuvid=EHeMv40hVr.sHqgGCAPCzgGPxQpKltHZBLYctMUbxog-1782381399.7906814-1.0.1.1-wlR2D4PXPMOEg5RQpkoFV.H0jpKDwWqII5umH0Zp.K4; 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

hdr:server:cloudflarehdr:cf-raybody:id:msg_prefixbody:usage.service_tierbody:usage.inference_geobody:usage.cache_fieldsbody:extra_fieldsupstream:anthropic-direct-from-body

req_011CcPn7MBPwZQjVwsbtya7X
a1131a823fd0e041-NRT

M-

cf-ray: a1131747c997fd4f-NRT
server: cloudflare
set-cookie: server_name_session=7f3cfa97a5a639f0ea1fc1ec7206ed9c; Max-Age=86400; httponly; path=/
x-oneapi-request-id: 20260625095427315457469ac5kl3LU

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

a1131747c997fd4f-NRT

协议指纹评分（机器自动）

总分 / 100 98 82 95

基线状态已对照基线已对照基线已对照基线

缓存命中比 7.9 /10 ×20 0.0 /10 ×20 7.9 /10 ×20

缓存 TTL 一致 10.0 /10 ×15 4.0 /10 ×15 10.0 /10 ×15

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 10.0 /10 ×13 7.7 /10 ×13 10.0 /10 ×13

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

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

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

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

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

Req-ID 透传 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

SDK 一致 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） 82 v3.24.1 95 v3.24.1

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 流式投递 buffer_dump_match
6 自由身份 identity_free_clean
7 停止原因 stop_reason_present
8 系统提示纯净 system_prompt_clean
9 Service Tier service_tier_present
10 缓存命中比 cache_hit_ratio_match
11 缓存 TTL 一致 cache_ttl_consistency
12 推理地区 inference_geo_present
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 信封自报一致 envelope_self_report_match
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 4616 ms 4403 ms 4697 ms

输出速度 74.5 tok/s 57.8 tok/s 60.7 tok/s

完成步骤 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 ⓘ

$0.3056

$0.3280

$0.3050

缓存读取占比 ⓘ 71% 47% 71%

揭晓

通道

官方基准

服务商 FoxCode ↗

通道cc

服务商 LingxiCode ↗

通道M-

返回列表