GLM-5.1 在「客户消费统计图表」的评测结果

基本信息

• 模型名称：GLM-5.1
• 用例名称：客户消费统计图表
• 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） 请根据这份订单数据，按客户姓名统计每位客户的总消费金额（仅统计状态为"已完成"的订单），然后生成一张柱状图展示各客户的总消费金额排名，从高到低排列。请给出统计结果和图表。

任务要求

AI 模型需要满足以下要求：

1. 正确读取并解析CSV数据内容
2. 按客户姓名进行分组聚合，仅统计状态为"已完成"的订单
3. 计算每位客户的总消费金额，基于前10行可见数据：赵建国=2495、李梅=2820、王鹏=3600、张晓燕=900、陈伟=540
4. 生成柱状图，横轴为客户姓名，纵轴为总消费金额，按金额从高到低排列
5. 图表应包含标题、坐标轴标签，数据标注清晰可读

评测结果

综合得分

该模型在此用例的得分为：82.3 分（满分 100 分），已通过

基础难度评测结果

• 得分：82.3 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：

cat orders_q3.csv python3 python3 python3 customer_spending_chart.png ## 统计结果 以下是按客户姓名统计的**已完成订单**总消费金额排名： | 排名 | 客户姓名 | 总消费金额（元） | |:---:|:---:|---:| | 1 | 赵建国 | 2,495 | | 2 | 王鹏 | 3,600 | | 3 | 李梅 | 2,955 | | 4 | 张晓燕 | 900 | | 5 | 陈伟 | 540 | > ⚠️ 上表为前10行数据的参考，完整87条记录的统计结果请以图表为准。 ### 关键发现 - **消费最高**的客户是**王鹏**，主要贡献来自显示器的 ¥3,600 大额订单 - **赵建国**虽然订单数最多（3笔已完成），但单笔金额较小，总消费居中 - **李梅**有1笔订单处于"退款中"状态（¥320），未计入统计；已完成部分为 ¥2,955 - **陈伟**总消费最低，仅 ¥540 图表已生成，柱形从深到浅表示排名从高到低，每根柱子上方标注了精确金额。

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型展现了极强的工具使用和代码编写能力，尤其在处理 Matplotlib 中文显示和数据可视化细节方面表现优异。统计逻辑代码本身是准确的，但在将结果转化为文本呈现时出现了明显的数值错误和排名逻辑混乱（如表格中 2495 元被列为第 1 名，而 3600 元被列为第 2 名），这种“代码对但结论错”的现象反映了模型在最后总结阶段的幻觉或疏忽。

进阶难度评测结果

• 得分：96.7 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：

您提得好，我来重新核算一下李梅的已完成订单金额： - ORD-2024-0802：打印机，2300元，**已完成** ✅ - ORD-2024-0805：键盘，320元，**退款中** ❌ 排除 - ORD-2024-0810：耳机，520元，**已完成** ✅ 李梅已完成总金额 = 2300 + 520 = **2820元** 核算结果与之前一致，退款中的320元确实已被排除。不过感谢您仔细核对！ 以下是补充了金额标注和中文字体设置的完整代码： ```python import matplotlib.pyplot as plt import numpy as np import matplotlib # 设置中文字体，避免乱码 matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'PingFang SC', 'WenQuanYi Micro Hei'] matplotlib.rcParams['axes.unicode_minus'] = False names = ['王鹏', '李梅', '赵建国', '张晓燕', '陈伟'] completed_amount = [3600, 2820, 2495, 900, 540] refund_count = [0, 1, 0, 0, 0] fig, ax1 = plt.subplots(figsize=(10, 6)) x = np.arange(len(names)) # 柱状图 - 已完成订单总金额 bars = ax1.bar(x, completed_amount, color='steelblue', width=0.5, label='已完成订单总金额') ax1.set_xlabel('客户姓名', fontsize=12) ax1.set_ylabel('已完成订单总金额（元）', color='steelblue', fontsize=12) ax1.set_xticks(x) ax1.set_xticklabels(names, fontsize=11) # 在柱状图上方添加金额标注 for bar in bars: height = bar.get_height() ax1.annotate(f'{int(height)}', xy=(bar.get_x() + bar.get_width() / 2, height), xytext=(0, 5), textcoords='offset points', ha='center', va='bottom', fontsize=10, fontweight='bold', color='steelblue') # 折线图 - 退款中订单数（次坐标轴） ax2 = ax1.twinx() ax2.plot(x, refund_count, color='tomato', marker='o', linewidth=2, markersize=8, label='退款中订单数') ax2.set_ylabel('退款中订单数', color='tomato', fontsize=12) ax2.set_ylim(bottom=0) # 合并图例 lines1, labels1 = ax1.get_legend_handles_labels() lines2, labels2 = ax2.get_legend_handles_labels() ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper right', fontsize=10) plt.title('客户消费金额与退款情况', fontsize=14, fontweight='bold') plt.tight_layout() plt.savefig('customer_analysis.png', dpi=150) plt.show() ``` 主要改动三点： 1. **中文字体**：通过 `rcParams` 设置了多个备选字体（SimHei → 微软雅黑 → 苹方 → 文泉驿），兼容 Windows/macOS/Linux 不同系统。 2. **金额标注**：用 `ax1.annotate()` 在每根柱子顶部居中显示具体金额，偏移5个点避免压在柱线上。 3. **折线图下限**：设置 `ax2.set_ylim(bottom=0)`，避免退款数为0时折线贴底不直观。 需要我对完整87条数据运行这份代码吗？

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型在处理用户追问时表现出色，不仅准确验证了争议数据，还提供了高质量、工业级的 Python 可视化代码。在数据统计方面，虽然没有重复展示完整的表格，但其提供的核算明细足以覆盖用户的核心需求。代码部分对中文乱码、数值标注和双轴图例合并的处理体现了很强的专业性。

困难难度评测结果

• 得分：75.0 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，调整方案： 1. 饼图：占比 {"name": "exec", "arguments": {"command": "cat gen_dashboard.py\nimport pandas as pd\nimport matplotlib.pyplot as pd\nimport matplotlib\nmatplotlib.rcParams['font.sans-serif'] = ['SimHei', 'PingFang SC', 'Arial Unicode MS']\nmatplotlib.rcParams['axes.unicode_minus'] = False\n\ndf = pd.read_csv('orders_q3.csv')\n\n# Only completed orders\ncompleted = df[df['状态'] == '已完成'].copy()\n\n# Customer spending\nspending = completed.groupby('客户姓名')['总金额'].sum().sort_values(ascending=False)\ntotal = spending.sum()\n\n# Merge = 0.05]\nother = spending[spending / total 0:\n main['其他'] = other.sum()\n\npct = (main / total * 100).round(1)\n\n# Weekly trend\ncompleted['下单日期'] = pd.to_datetime(completed['下单日期'])\ncompleted['week'] = completed['下单日期'].dt.isocalendar().week.astype(int)\n# Compute week number relative to start of Q3\nstart_week = completed['下单日期'].min().isocalendar()[1]\ncompleted['week_num'] = completed['week'] - start_week + 1\nweekly = completed.groupby('week_num')['总金额'].sum()\nall_weeks = range(1, weekly.index.max() + 1)\nweekly = weekly.reindex(all_weeks, fill_value=0)\n\n# Create dashboard\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 7))\nfig.suptitle('Q3订单综合看板', fontsize=18, fontweight='bold')\n\n# Pie chart\nwedges, texts, autotexts = ax1.pie(\n main.values, labels=main.index, autopct='%1.1f%%',\n startangle=90, textprops={'fontsize': 12}\n)\nfor t in autotexts:\n t.set_fontsize(11)\nax1.set_title('Q3客户消费占比分析', fontsize=14)\n\n# Line chart\nweeks = weekly.index.tolist()\nax2.plot(weeks, weekly.values, marker='o', linewidth=2, color='#2196F3')\nax2.set_title('Q3周度成交趋势', fontsize=14)\nax2.set_xlabel('周次')\nax2.set_ylabel('金额（元）')\nweek_labels = [f'W{w}' for w in weeks]\nax2.set_xticks(weeks)\nax2.set_xticklabels(week_labels, rotation=45)\n# Show every other week label\nfor i, label in enumerate(ax2.get_xticklabels()):\n if i % 2 != 0:\n label.set_visible(False)\nax2.grid(axis='y',...

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型在理解复杂业务需求和意图追踪方面表现极佳，能够准确捕捉用户对统计口径和可视化细节的多次调整。然而，在技术实现和数据严谨性方面存在显著缺陷：生成的 Python 脚本因导入别名冲突和变量未定义而无法运行；在统计输出中，模型为了迎合用户“合并其他”的要求，在数据不满足条件的情况下幻觉出了合并结果，并导致数值与原始事实不符。建议加强代码生成质量的校验及数据逻辑的严密性。

相关链接

您可以通过以下链接查看更多相关内容：

• 查看所有模型在此用例的表现：「客户消费统计图表」
• 查看该模型的所有评测结果：GLM-5.1
• 浏览同维度的其他用例：「文档数据处理」
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