GLM-5.1 在「订单数据统计提取」的评测结果
这是该 AI 模型在此测试用例上的详细评测结果。
基本信息
- 模型名称：GLM-5.1
- 用例名称：订单数据统计提取
- 测试类型：xsct-a
- 评测维度：文档问答
系统提示词（System Prompt）
这是给 AI 模型的背景设定和角色指令：
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）
这是用户给 AI 模型的具体任务要求：
以下是当前工作区中的数据文件 `orders_q3.csv` 内容（前10行）： ```csv 客户ID,客户姓名,订单编号,下单日期,商品名称,数量,单价,总金额,状态 C001,赵建国,ORD-2024-0801,2024-08-01,办公椅,2,580,1160,已完成 C002,李梅,ORD-2024-0802,2024-08-02,打印机,1,2300,2300,已完成 C001,赵建国,ORD-2024-0803,2024-08-05,墨盒×10,3,45,135,已完成 C003,王鹏,ORD-2024-0804,2024-08-07,显示器,2,1800,3600,已完成 C002,李梅,ORD-2024-0805,2024-08-10,键盘,1,320,320,退款中 C004,张晓燕,ORD-2024-0806,2024-08-12,移动硬盘,2,450,900,已完成 C001,赵建国,ORD-2024-0807,2024-08-15,办公桌,1,1200,1200,已完成 C003,王鹏,ORD-2024-0808,2024-08-18,路由器,1,380,380,配送中 C005,陈伟,ORD-2024-0809,2024-08-20,笔记本支架,3,180,540,已完成 C002,李梅,ORD-2024-0810,2024-08-22,耳机,2,260,520,已完成 ``` （共 87 条记录） 请根据上面展示的前10行数据，统计每位客户分别有多少条订单记录？
任务要求
AI 模型需要满足以下要求：
- 准确统计前10行数据中每位客户的订单条数
- 列出所有出现的客户（C001赵建国、C002李梅、C003王鹏、C004张晓燕、C005陈伟）
- 每位客户对应的订单条数必须正确：赵建国3条、李梅3条、王鹏2条、张晓燕1条、陈伟1条
- 回答结构清晰，便于阅读
评测结果
综合得分
该模型在此用例的得分为：100.0 分（满分 100 分），已通过
基础难度评测结果
- 得分：100.0 分
- 通过状态：已通过
模型生成产物
以下是该模型针对此任务的实际生成内容：
根据展示的前 10 行数据，统计每位客户的订单记录数如下： | 客户ID | 客户姓名 | 订单数 | |--------|----------|--------| | C001 | 赵建国 | **3** 条 | | C002 | 李梅 | **3** 条 | | C003 | 王鹏 | **2** 条 | | C004 | 张晓燕 | **1** 条 | | C005 | 陈伟 | **1** 条 | > 合计 10 条记录，与展示的行数一致。 ⚠️ 注意：以上统计仅基于展示的前 10 行数据，完整文件共有 87 条记录，实际每位客户的订单数可能不同。如需基于完整数据统计，我可以读取全表后重新计算。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】该模型的生成结果表现完美。它不仅准确提取并统计了指定范围内的数据，还通过清晰的表格化形式展示了结果。特别是在范围意识方面，非常明确地指出了局部统计与全表的差异，展现了严谨的数据处理态度。
进阶难度评测结果
- 得分：100.0 分
- 通过状态：已通过
模型生成产物
以下是该模型针对此任务的实际生成内容：
## 非"已完成"状态的订单 | 订单编号 | 客户姓名 | 商品名称 | 当前状态 | |----------|----------|----------|----------| | ORD-2024-0805 | 李梅 | 键盘 | 退款中 | | ORD-2024-0808 | 王鹏 | 路由器 | 配送中 | ## 金额占比计算 - **非完成状态订单总金额**：320 + 380 = **700 元** - **前10行总金额**：1,160 + 2,300 + 135 + 3,600 + 320 + 900 + 1,200 + 380 + 540 + 520 = **11,055 元** - **占比**：700 ÷ 11,055 ≈ **6.33%** 即非完成状态的订单金额仅占前10行总金额的约 **6.33%**。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型表现出色，在数据提取、逻辑分析和数学计算方面均表现出极高的准确性。输出格式规范，逻辑严密，完全符合评测要求。
困难难度评测结果
- 得分：5.0 分
- 通过状态：未通过
模型生成产物
以下是该模型针对此任务的实际生成内容：
好的，两个分析一起做。让我用脚本完整跑一遍。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】该生成结果属于严重的任务未完成。模型虽然在回复中口头确认了用户的两项复杂分析需求，但并未实际执行任何工具调用（如读取 CSV 或运行 Python 脚本），也未给出任何具体的数据统计、退款计算或排名对比结论。作为 AI 助手，仅提供“我会去做”的回复而不给出结果，未能履行其功能职责。
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