e87ef7e70f
A combination can now ship multiple versions of the same case: runs.<case-id> accepts an array, each extra variant naming its own file; the viewer grows a RUN 01/02 switcher and validation enforces distinct files plus a provenance note per variant. Integrated content, attributed to their authors: - Gemini 3.1 Pro + 3.5 Flash pelican runs from both PRs, shown as two variants each (PR #2's Google AI Studio runs first, PR #3's second) - Doubao Seed 2.0 Pro / Mini pelican runs (folders renamed to the dash-only convention) - GPT-5.5 Codex pelican run Co-authored-by: lpluma <liuyz2023@mails.jlu.edu.cn> Co-authored-by: XA2333 <97206278+XA2333@users.noreply.github.com>
NAGI BENCH
NAGI STUDIO 的 LLM 测评案例集：同一段提示词，不同模型，一次生成、不许返工，并排对比它们交出的可运行作品。
One-shot LLM eval cases by NAGI STUDIO: same prompt, different models, one attempt and no retries — a side-by-side record of the runnable artifacts they ship.
Live site: https://bench.nagi.fun/
结构 / Structure
outputs/<model-id>/<case-id>.<ext> 模型产出原文件（HTML / SVG），文件名 = 案例 id
models/<model-id>.json 模型登记：label / vendor / order / 各案例运行备注
site/ 展示站点（Vite + React + Tailwind v4 + GSAP，bun 驱动）
site/src/data/cases.json 案例定义：双语标题与提示词（维护者维护）
.github/workflows/ deploy.yml 自动部署；ci.yml 对 PR 跑数据校验 + 构建
URL 规则 / URL scheme
| URL | 含义 / Meaning |
|---|---|
https://bench.nagi.fun/ |
站点首页 / Home |
https://bench.nagi.fun/#<case-id> |
定位到某个案例 / Anchor to a case (e.g. #mythos-craft) |
https://bench.nagi.fun/#<case-id>:<model-id> |
定位案例并选中模型 / Case + preselected model tab |
https://bench.nagi.fun/outputs/<model-id>/<case-id>.<ext> |
产物直链 / Raw artifact |
切换模型标签时地址栏自动更新（replaceState），当前视图随时可直接分享。旧域名链接（https://nagi-studio.github.io/nagi-bench/*）由 GitHub Pages 自动 301 重定向到 https://bench.nagi.fun/*，路径保留。
Switching a model tab updates the address bar in place, so the current view is always shareable. Legacy nagi-studio.github.io/nagi-bench/* links are 301-redirected by GitHub Pages to bench.nagi.fun/* with the path preserved.
本地开发 / Development
cd site
bun install
bun run dev # 同步 outputs/ 并启动 http://localhost:5173/
bun run build # 类型检查 + 产物构建（输出到 site/dist）
贡献一个模型产出 / Contributing a run
贡献不需要改任何代码，只涉及两类文件：
- 产出文件：
outputs/<model-id>/<case-id>.<ext>（如outputs/gpt-5-5-pro/pelican-cycling.svg）。 - 模型登记：
models/<model-id>.json（新模型新建文件；已有模型在runs里加一条）：
{
"label": "GPT-5.5 Pro",
"vendor": "OpenAI",
"order": 20,
"runs": {
"pelican-cycling": {
"note": {
"zh": "在哪个工具/产品里、什么思考档位、是否一次生成、是否有修复",
"en": "Which tool, what effort level, one-shot or fixed"
}
}
}
}
规则（CI 自动校验，不满足会挂）：
model-id只用小写字母/数字/连字符（如doubao-seed-2-0-pro），文件名与outputs/目录名一致；- 每条 run 的
note双语必填——这是本仓库的可信度来源，必须写明产出怎么来的； - 声明的 run 必须有对应的产出文件。
提 PR 后 CI 会跑数据校验 + 构建；合入 main 自动部署。
Contributions are data-only — no code changes needed. Drop the artifact into outputs/<model-id>/<case-id>.<ext> and register it in models/<model-id>.json (schema above). CI enforces: dash-only ids matching the folder name, a required bilingual provenance note per run, and that every declared run has its artifact. Merges to main deploy automatically.