GLM-5.1 在「按客户统计订单数据」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：按客户统计订单数据
- 测试类型：xsct-a
- 评测维度：文档数据处理
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） 请按客户ID为单位，统计C001、C002、C003这三位客户的汇总数据，包括：订单数、总数量、总金额、已完成订单数。结果请用表格形式展示。
任务要求
AI 模型需要满足以下要求：
- 按客户ID（C001、C002、C003）分组统计
- 统计维度包括：订单数、总数量、总金额、已完成订单数
- 结果以表格形式清晰展示
- 基于提供的前10行数据进行计算，数值准确无误
评测结果
综合得分
该模型在此用例的得分为：3.3 分（满分 100 分），未通过
基础难度评测结果
- 得分：3.3 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：
cat orders_q3.csv
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】该模型表现极差。用户明确要求基于提供的数据片段进行统计汇总并以表格形式展示，但模型仅返回了一个读取文件的 shell 命令（cat orders_q3.csv），完全没有回答用户的实质性问题，未完成任何功能要求。
进阶难度评测结果
- 得分：2.5 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：
先读取完整数据文件。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型的生成结果极度简略，仅用一句话口头描述了需要读取文件的第一步，完全没有执行用户要求的统计分析、维度切换和图表生成任务。在具备明确工具（如 read, exec）的情况下，模型未进行任何实际操作，属于严重的任务失败。
困难难度评测结果
- 得分：85.0 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：
好的，我来按「排除退款中订单」的口径重新计算所有数据，补全C005-C007，并生成区域+客户双层汇总和更新图表。 [工具调用: exec] ```python import pandas as pd import matplotlib.pyplot as plt import matplotlib matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'Arial Unicode MS', 'DejaVu Sans'] matplotlib.rcParams['axes.unicode_minus'] = False df = pd.read_csv('orders_q3.csv') # 排除退款中订单用于金额计算 df_active = df[df['状态'] != '退款中'] # ====== 1. 区域维度汇总（排除退款中） ====== status_counts = df.groupby(['所属区域','状态']).size().unstack(fill_value=0) # 确保三列都存在 for col in ['已完成','退款中','配送中']: if col not in status_counts.columns: status_counts[col] = 0 category_amount = df_active.groupby(['所属区域','商品类别'])['总金额'].sum().unstack(fill_value=0) for col in ['办公家具','电子设备','耗材']: if col not in category_amount.columns: category_amount[col] = 0 agg_stats = df_active.groupby('所属区域')['总金额'].agg(['mean','max','min']) region_result = pd.concat([status_counts[['已完成','退款中','配送中']], category_amount[['办公家具','电子设备','耗材']], agg_stats], axis=1) region_result.columns = ['已完成订单数','退款中订单数','配送中订单数', '办公家具类总金额','电子设备类总金额','耗材类总金额', '平均订单金额','最大单笔金额','最小单笔金额'] print("====== 区域维度汇总（排除退款中订单的金额口径） ======") print(region_result.to_markdown()) # ====== 2. 区域+客户维度汇总（排除退款中） ====== rows = [] for (region, cid), group in df.groupby(['所属区域','客户ID']): name = group['客户姓名'].iloc[0] status_cnt = group['状态'].value_counts() completed = status_cnt.get('已完成', 0) refund = status_cnt.get('退款中', 0) shipping = status_cnt.get('配送中', 0) active = group[group['状态'] != '退款中'] cat_amt = active.groupby('商品类别')['总金额'].sum() furniture = cat_amt.get('办公家具', 0) electronics = cat_amt.get('电子设备', 0) consumables = cat_amt.get('耗材', 0) if len(active) > 0: avg_amt = active['总金额'].mean() max_amt = active['总金额'].max() min_amt = active['总金额'].min() else: avg_amt = max_amt = min_amt = 0 rows.append({ '所属区域': region, '客户ID': cid, '客户姓名': name, '已完成订单数': completed, '退款中订单数': refund, '配送中订单数': shipping, '办公家具类总金额': furniture, '电子设备类总金额': electronics, '耗材类总金额': consumables, '平均订单金额': round(avg_amt, 2), '最大单笔金额':...
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型在理解复杂指令和逻辑变更方面表现出色，能够迅速跟进用户对统计口径和维度的多次调整，并给出了高质量的自动化处理脚本。然而，其最终呈现的统计结果存在严重的内部矛盾：区域汇总表与客户明细表的数据无法相互对应，且在区域汇总中未能彻底执行‘排除退款订单金额’的口径（如华东区的最大值仍保留了退款金额）。这种‘逻辑正确但结果幻觉’的表现是其主要失分项。
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