# 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//. 模型产出原文件(HTML / SVG),文件名 = 案例 id models/.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/#` | 定位到某个案例 / Anchor to a case (e.g. `#mythos-craft`) | | `https://bench.nagi.fun/#:` | 定位案例并选中模型 / Case + preselected model tab | | `https://bench.nagi.fun/outputs//.` | 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 ```bash 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: 1. 产出文件:`outputs//.`(如 `outputs/gpt-5-5-pro/pelican-cycling.svg`)。 2. 模型登记:`models/.json`(新模型新建文件;已有模型在 `runs` 里加一条): ```json { "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//.` and register it in `models/.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.