nagi-bench/README.en.md

NAGI BENCH

中文 | English

One-shot LLM eval cases by NAGI STUDIO: same prompt, different model x harness x effort combinations, one attempt and no retries — a side-by-side record of the runnable artifacts they ship.

Live site: https://bench.nagi.fun/

Models vs harnesses

Model + Harness = Agent — a model is just weights; wrap it in a runtime and it becomes an agent that can actually do the work. That agent is the unit this bench evaluates.

• Model: the weights and inference engine itself — GPT-5.5, Gemini 3.1 Pro, Claude Fable 5.
• Harness: the product/scaffolding wrapping the model — Codex CLI, Cursor, AntiGravity, the Claude web app. It controls tool calls, system prompts, context management and continuation strategy, and often shapes the outcome as much as the model does.
• The unit of evaluation here is therefore an Agent: the same model under a different harness is a different agent and a separate entry (GPT-5.5 Pro and GPT-5.5 running in Codex CLI at xhigh are two agents from the same model family). Effort is a parameter of that agent, defaulting to max.

Registry

Model	Vendor	Harness x Effort	Runs
Claude Fable 5	Anthropic	Claude Web App · Max Claude Code · Max Cursor · High	06
Claude Opus 4.8	Anthropic	Claude Code · Max	02
GPT-5.5 Pro	OpenAI	ChatGPT Web · Extended Pro	02
GPT-5.5	OpenAI	Codex CLI · xhigh	02
Gemini 3.1 Pro	Google	AntiGravity · High Google AI Studio · High	03
Gemini 3.5 Flash	Google	AntiGravity · High Google AI Studio · High	03
Grok Build	xAI	Grok Build TUI · Max	02
Composer 2.5	Cursor	Cursor · Max Cursor · Default Grok Build TUI · Default	05
Mistral Medium 3.5	Mistral AI	Vibe · Thinking	02
DeepSeek-V4-Pro	DeepSeek	Claude Code · Max	02
Doubao Seed 2.0 Pro	ByteDance	Doubao Web · Pro Mode	01
Doubao Seed 2.0 Mini	ByteDance	Doubao Web · Fast Mode	01
MiMo v2.5 Pro	Xiaomi	Claude Code · Max	02
Kimi K2.6	Moonshot AI	Kimi Code · Thinking	02
MiniMax M3	MiniMax	MiniMax Code Web · Thinking	01
Qwen3.7-Max	Alibaba	Qoder · Default	02

Pending: GLM-5.1 (PRs welcome)

This table is a hand-maintained snapshot; the site's registry section is generated live from models/*.json and is authoritative.

Arena blind battles & community board

• Each case defaults to Arena mode: two anonymous artifacts battle (A / B / Neither) and identities reveal only after you vote; switch to Demo Show to browse everything (tabs are grouped by model — a model with several harnesses can be viewed singly or side by side).
• Casting 03 votes total unlocks the community leaderboard, ranked by Bradley-Terry scores (the LMSYS Chatbot Arena approach); "neither" counts separately as both-bad.
• Votes stay in the browser by default; deploying worker/ (Cloudflare Worker + D1 + Turnstile, free tier) upgrades the board to sitewide crowdsourced data — see worker/README.md.

Structure

outputs/<model-id>/<case-id>.<ext>   raw model artifacts (HTML / SVG), filename = case id
models/<model-id>.json               combination registry: label / vendor / harness / effort / order / run notes
cases.json                           case definitions (bilingual prompts, maintainer-owned)
scripts/validate-data.ts             data validation (CI runs it on every PR)

The site and vote-backend source lives in the private nagi-bench-site repo, deployed on Cloudflare Pages; this repo is the site's data source.

Validation

bun scripts/validate-data.ts   # the same data validation CI runs

Contributing a run

If you are an AI agent (Claude Code / Codex / Cursor, etc.), this part is for you:

• The unit you contribute is one Agent = a model × the harness you run in. The <agent-id> (lowercase letters / digits / dashes) encodes that pairing — e.g. gpt-5-5-pro, claude-fable-5-cc (cc = Claude Code). The same model in a different harness is a different Agent with a different id.
• There are two cases right now, defined in cases.json: mythos-craft (HTML, a playable voxel world) and pelican-cycling (SVG, a pelican cycling by the sea). The prompt lives in cases.json — use it verbatim, do not paraphrase.
• To find what's missing: read cases.json (all cases) and models/*.json (what each Agent has run) — a <case-id> absent from an Agent's json is an open slot; any row in the Registry table above whose "Runs" count is below the number of cases is an Agent missing a case; a brand-new Agent can fill both.
• Then follow the two-file flow below, write a bilingual note (how the artifact was produced — one-shot or fixed), and run bun scripts/validate-data.ts until it passes before opening a PR.

Contributions are data-only — no code changes needed:

1. Artifact: outputs/<model-id>/<case-id>.<ext> (e.g. outputs/gpt-5-5-pro/pelican-cycling.svg).
2. Registration: models/<model-id>.json (new file for a new combination; add to runs for an existing one) — see the schema example in the Chinese README.

CI-enforced rules: dash-only lowercase ids matching the outputs/ folder name; a required bilingual provenance note per run (which harness, what effort, one-shot or fixed); every declared run must have its artifact; multiple versions of the same combination x case go in an array with distinct file names (e.g. pelican-cycling-2.svg). Set contributor to your GitHub username — the site shows your avatar linked to your profile next to the run. Fill in harness and effort truthfully for new combinations: the case page renders Harness / Effort metadata chips from these fields and auto-matches brand icons for the model, vendor and harness (via lobe-icons) — contributors never touch icons.

PRs get data validation from CI; merges to main rebuild the site automatically (usually instant, at most 6 hours).