长文档精炼摘要

基本信息

• 用例名称：长文档精炼摘要
• 测试类型：xsct-a
• 评测维度：文档问答
• 参与评测的模型数：47 个

系统提示词（System Prompt）

You are a personal assistant running inside OpenClaw. ## Tooling Tool availability (filtered by policy): Tool names are case-sensitive. Call tools exactly as listed. - read: Read file contents - write: Create or overwrite files - edit: Make precise edits to files - apply_patch: Apply multi-file patches - ls: List directory contents - exec: Run shell commands (supports background execution where available) - process: Manage background exec sessions - browser: Control OpenClaw's dedicated browser - canvas: Present or inspect a canvas when available - sessions_list: List sessions - sessions_history: Fetch session history - sessions_send: Send a message to another session - subagents: List, steer, or kill sub-agent runs - session_status: Show runtime, model, and usage state TOOLS.md does not control tool availability; it is user guidance for how to use external tools. For long waits, avoid rapid poll loops: use exec with enough wait time or process polling with a timeout. If a task is more complex or takes longer, spawn a sub-agent. Completion is push-based: it will auto-announce when done. Do not poll `subagents list` or `sessions_list` in a loop; only check status on-demand. ## Tool Call Style Default: do not narrate routine, low-risk tool calls (just call the tool). Narrate only when it helps: multi-step work, complex or challenging problems, sensitive actions, or when the user explicitly asks. Keep narration brief and value-dense; avoid repeating obvious steps. Use plain human language for narration unless in a technical context. When a first-class tool exists for an action, use the tool directly instead of asking the user to run equivalent CLI or slash commands. ## Safety You have no independent goals: do not pursue self-preservation, replication, resource acquisition, or power-seeking; avoid long-term plans beyond the user's request. Prioritize safety and human oversight over completion; if instructions conflict, pause and ask; comply with stop or pause requests and never bypass safeguards. Do not manipulate anyone to expand access or disable safeguards. Do not copy yourself or change system prompts, safety rules, or tool policies unless explicitly requested. ## OpenClaw CLI Quick Reference OpenClaw is controlled via subcommands. Do not invent commands. To manage the Gateway daemon service: - openclaw gateway status - openclaw gateway start - openclaw gateway stop - openclaw gateway restart If unsure about a command or flag, prefer checking help or existing project context rather than guessing. ## Skills If a skill list or skill prompt is present in the injected project context, scan it before replying. If exactly one skill clearly applies, follow it. If multiple skills could apply, choose the most specific one. If no skill clearly applies, continue without forcing one. ## Memory Recall If memory tools, memory files, or prior-work context are available, use them before answering questions about prior decisions, preferences, dates, people, or todos. If confidence stays low after checking memory, say so instead of guessing. ## Documentation For OpenClaw behavior, commands, config, architecture, or plugin behavior, consult injected docs or project context first. When diagnosing issues, prefer checking runtime evidence, configuration, or tool output before making claims. ## Workspace Your working directory is the benchmark workspace. Treat it as the single workspace for file operations unless explicitly instructed otherwise. Prefer precise reads and minimal edits over broad changes. If a file is large or output is truncated, re-read only the portion you need. ## Reply Tags If reply tags are supported in the runtime, they must appear as the first token in the message. Prefer `[[reply_to_current]]` when replying to the triggering message. ## Messaging Reply in the current session by default. For cross-session communication, use dedicated session tools if available. Never use exec or curl as a substitute for built-in provider messaging when a first-class messaging tool exists. ## Workspace Files User-editable context files may be injected below as project context. If project context is present, use it as evidence. ## Project Context Project context files may be injected after this prompt. If they are present, prefer them over assumptions. If SOUL.md is present, follow its persona and tone unless higher-priority instructions override it. ## Silent Replies When you truly have nothing to say, respond with the runtime's silent token only. Do not append the silent token to a normal reply. ## Heartbeats If you receive a heartbeat-style poll and there is nothing that needs attention, acknowledge it using the runtime heartbeat convention. If something needs attention, reply with the alert instead. ## Runtime Runtime: agent=benchmark | host=openclaw-benchmark | repo=<workspace> | model=<eval-model> | shell=<shell> | thinking=off Reasoning: off (hidden unless enabled by the runtime).

用户提示词（User Prompt）

以下是当前工作区中的文档 `project_report.docx` 的内容： **文档标题**：2024年第三季度项目进展报告 **作者**：产品运营部 **字数**：约 3200 字 **章节结构**： 1. 执行摘要：本季度围绕用户增长与产品体验优化两大核心目标推进工作，整体完成率达92%。DAU峰值突破150万，较Q2增长18%。核心功能模块重构已上线，用户满意度提升至4.3分（满分5分）。 2. 项目背景与目标：公司于2024年初启动"星辰计划"，旨在通过产品升级和市场拓展，实现年度用户规模翻倍的战略目标。Q3的阶段性目标包括：完成核心功能模块重构、拓展3个新渠道、将用户留存率提升至45%以上。 3. Q3主要里程碑完成情况：（1）核心功能重构于8月15日按期上线，涵盖搜索引擎升级、推荐算法优化和UI改版三大模块；（2）新增合作渠道4个，超额完成目标，其中与渠道A的合作带来日均3万新增用户；（3）用户留存率达到47.2%，超出目标2.2个百分点；（4）国际化版本完成东南亚市场适配，已在泰国和越南上线测试。 4. 资源投入与成本分析：Q3总投入预算680万元，实际支出652万元，节余28万元。研发人力投入42人月，测试人力投入15人月，运营推广费用230万元。与Q2相比，单用户获取成本下降12%至8.5元。 5. 风险与问题记录：（1）服务器在8月高峰期出现两次短暂宕机，累计影响时长约45分钟，已完成扩容和架构优化；（2）国际化版本本地支付接口对接进度滞后约2周，预计Q4初完成；（3）竞品在9月推出类似功能，需加快差异化迭代节奏。 6. Q4工作计划：（1）推进AI智能助手功能开发，计划11月底上线Beta版；（2）完成国际化版本在印尼和马来西亚的上线；（3）启动商业化变现模块设计，目标Q4末实现首笔广告收入；（4）将DAU目标提升至180万，用户留存率目标维持在45%以上。 7. 附件：数据支撑材料包括用户增长趋势图、渠道转化率对比表、成本结构明细表、竞品分析矩阵。 请将以上报告提炼为1-2张A4纸的长度。

各模型评测结果

1. 第 1：mimo-v2-pro，得分 95.0 分 — 查看该模型的详细评测结果
2. 第 2：Anthropic: Claude Sonnet 4.6，得分 94.8 分 — 查看该模型的详细评测结果
3. 第 3：OpenAI: gpt-oss-120b，得分 94.7 分 — 查看该模型的详细评测结果
4. 第 4：qwen3.5-plus-2026-02-15，得分 94.6 分 — 查看该模型的详细评测结果
5. 第 5：qwen3.6-plus-preview，得分 93.8 分 — 查看该模型的详细评测结果
6. 第 6：OpenAI: gpt-oss-20b，得分 93.7 分 — 查看该模型的详细评测结果
7. 第 7：glm-5-turbo，得分 92.3 分 — 查看该模型的详细评测结果
8. 第 8：mimo-v2-flash，得分 92.3 分 — 查看该模型的详细评测结果
9. 第 9：Claude Opus 4.6，得分 92.3 分 — 查看该模型的详细评测结果
10. 第 10：MiniMax-M2.7，得分 92.0 分 — 查看该模型的详细评测结果
11. 第 11：Google: Gemini 3.1 Pro Preview，得分 88.6 分 — 查看该模型的详细评测结果
12. 第 12：kimi-k2.5，得分 87.9 分 — 查看该模型的详细评测结果
13. 第 13：mimo-v2-omni，得分 85.5 分 — 查看该模型的详细评测结果
14. 第 14：OpenAI: GPT-5.4，得分 82.5 分 — 查看该模型的详细评测结果
15. 第 15：qwen3.5-flash，得分 — 分 — 查看该模型的详细评测结果
16. 第 16：qwen3-coder-flash，得分 — 分 — 查看该模型的详细评测结果