Skill / MCP:让你的智能体使用榜单数据
这里的一切都是公开的:无需账号或 API Key。把下面的技能文件交给你的 AI 智能体,或直接调用 REST API。
智能体技能(SKILL.md)
把这个文件保存为技能目录下的 SKILL.md,智能体就知道如何通过公开 API 读取榜单和模型能力对比。
---
name: pandanpc-leaderboard
description: Look up current AI model leaderboards and the top-10 model capability comparison from PandaNpc's public API. Use when the user asks which AI model is best, how models rank on chat, coding, image, video, speech or agent tasks, or how top models compare on benchmarks.
---
# PandaNpc leaderboard
Public, read-only JSON API. No account and no API key. Base URL: https://api.pandanpc.com/api/public/leaderboard
## Boards
GET https://api.pandanpc.com/api/public/leaderboard
GET https://api.pandanpc.com/api/public/leaderboard?category=<board key>
Response: { boards: [{ key, source, metric, meta, rows }], vendorIcons }
- With ?category=<key> the response still contains every board, but only the requested one has rows; boards with an empty rows array are shells. Always pick a board by key (for example jq '.boards[] | select(.key=="llm_chat")'), never by index.
- key: llm_chat, image_gen, code, image_edit, video, agent, vision, search, document, image_to_video, video_edit, text_to_speech, speech_to_text, speech_to_speech, or_usage, aa_intelligence
- source: arena | artificial_analysis | openrouter
- meta: { source_url, last_updated, synced_at }
- rows: [{ rank, model, vendor, score, ci, votes, ...price fields }]
Scores are comparable only inside one board. Arena boards are Elo from human blind votes; read the rank together with ci. Lower is better for speech-to-text (word error rate).
## Capability comparison
GET https://api.pandanpc.com/api/public/leaderboard/capabilities
Response: { dimensions, models, sources, fetched_at }
- models: top 10 by Epoch Capabilities Index: { rank, model, model_key, vendor, eci }
- sources: [{ source, label, license, url, updated, fetched_at, boards }] including epoch, vals, aa, google_card, browserbase, browser_use, coarena and ale
- boards: [{ key, name, capability, comparison_key, updated, fetched_at, scores }] where scores is keyed by model_key: { score, score_raw, scale, effort, harness, cost_per_task, model_version, evaluation_metadata }
Preserve every published configuration. Compare only within one source and comparison_key. Read scale: percent is 0-100, rating (Coarena) is not a percentage. Coarena is mixed-task blind human preference; show its 95% interval. ALE allows GUI/CLI tools; keep its full/max configuration, harness and run-task / split-task counts, and separate pass rate from partial score. updated is the upstream date when published; fetched_at is PandaNpc's fetch time, never a substitute source date.
## Attribution (required)
Name the source next to any number you show:
- Epoch AI, CC BY 4.0 (epoch.ai)
- Vals.ai (vals.ai)
- Artificial Analysis (artificialanalysis.ai)
- Coarena (coarena.ai) and Agents’ Last Exam, CC BY 4.0 (agents-last-exam.org), for their matching sources
- arena.ai and OpenRouter for the matching boards
Data refreshes about once a day. Cache for at least an hour.
MCP 服务器
用下面的配置把 PandaNpc MCP 服务器添加到你的客户端,或运行下方命令。
{
"mcpServers": {
"pandanpc": {
"command": "npx",
"args": ["-y", "@pandanpc/mcp-server"]
}
}
}
claude mcp add pandanpc -- npx -y @pandanpc/mcp-serverMCP 服务器暂未提供榜单工具。获取榜单数据请使用上面的技能或下面的 REST API。
curl 示例
# All boards (Arena, Artificial Analysis, OpenRouter) in one response
curl -s https://api.pandanpc.com/api/public/leaderboard
# One board: add ?category=<board key>, e.g. llm_chat, code, image_gen, aa_intelligence
# The response still lists every board, but only the requested one has rows (the others are empty shells)
curl -s "https://api.pandanpc.com/api/public/leaderboard?category=llm_chat"
# Top 5 models of the chat board: select the board by its key, never by position
curl -s "https://api.pandanpc.com/api/public/leaderboard?category=llm_chat" | jq '.boards[] | select(.key=="llm_chat") | .rows[:5] | map({rank, model, vendor, score})'
# Capability comparison: top 10 models by Epoch Capabilities Index x benchmarks
curl -s https://api.pandanpc.com/api/public/leaderboard/capabilities
# GPQA Diamond score of every model (Epoch AI)
curl -s https://api.pandanpc.com/api/public/leaderboard/capabilities | jq '.sources[] | select(.source=="epoch") | .boards[] | select(.key=="gpqa_diamond") | .scores'响应字段
| 字段 | 含义 |
|---|---|
boards | 榜单数组。每个榜单包含 key、source、metric、meta 和 rows。 |
key | 榜单 key,例如 llm_chat、code 或 aa_intelligence;在能力接口中也是基准的 key。 |
source | 数据来源:arena、artificial_analysis 或 openrouter;能力接口中为 epoch、vals 或 aa。 |
metric | 该榜单上分数的含义,例如 Elo 或指数。 |
meta | source_url、last_updated(上游日期)和 synced_at(PandaNpc 最近刷新时间)。 |
rows | 排名行:rank、model、vendor、score、ci、votes 以及可选的价格字段。 |
vendorIcons | 从小写厂商名到图标 URL 的映射。 |
models | 能力接口:按 Epoch 能力指数取前 10 的模型,包含 rank、model、model_key、vendor 和 eci。 |
sources | 能力榜响应:每个发布方分别提供榜单及抓取时间,包含 Coarena 和 ALE。不同来源与比较组不混排。 |
scores | 每个基准与模型包含 score、score_raw、scale、effort、harness、model_version 和 evaluation_metadata。百分数使用 0–100;Coarena 偏好分不是百分比,需保留置信区间和任务数。 |
updated | 上游公开的基准日期;null 表示未公开。fetched_at 单独记录 PandaNpc 抓取该来源的时间。 |
数据署名
数据来自 Epoch AI(CC BY 4.0)、Vals.ai、Artificial Analysis(artificialanalysis.ai)、arena.ai 和 OpenRouter。展示数字时请同时标注来源名称。