task_categories:
- text-generation
language:
- zh
QuestBench
QuestBench is a course-curated benchmark of 256 expert-level questions for evaluating the deep-search capabilities of language models, introduced in the paper Teaching AI Through Benchmark Construction: QuestBench as a Course-Based Practice for Accountable Knowledge Work.
Questions are long-tail queries spanning 14 normalized domains in the humanities and social sciences (Literature, Law, History, Foreign Languages, Social Sciences, Arts, Archaeology, Journalism, …). Each question, reference answer, and grading rubric is in Chinese; the model under evaluation may search and read in any language.
Files
questbench.jsonl— 256 questions, one JSON record per line..claude.json— Claude Code MCP server config.vision_server.py— local MCP server proxying image analysis to OpenRouter.package.json,package-lock.json— npm dependencies.
Each JSONL record has four fields: domain, question, answer, grading_criteria (the rubric an LLM judge uses to assign a 0–100 score).
Run
Setup:
brew install --cask claude # Claude Code CLI
brew install jq node uv
npm install
export ANTHROPIC_API_KEY=... # Claude
export SERPER_API_KEY=... # https://serper.dev
export OPENROUTER_API_KEY=... # https://openrouter.ai (vision)
Run a single question:
QUESTION=$(jq -r '.question' questbench.jsonl | head -n 1) && \
claude -p "$QUESTION" \
--mcp-config ./.claude.json --strict-mcp-config \
--permission-mode bypassPermissions \
--disallowedTools WebSearch WebFetch \
--output-format stream-json --verbose