Google: Gemma 4 26B A4B 在「客户消费统计」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：Google: Gemma 4 26B A4B
  • 用例名称：客户消费统计
  • 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） 请根据以上数据，按客户统计每位客户的订单数量和消费总金额（仅统计"已完成"状态的订单），并生成一个柱状图进行可视化展示，同时指出消费总金额最高的客户是谁。

任务要求

AI 模型需要满足以下要求：

  1. 按客户ID或客户姓名进行分组统计
  2. 仅统计状态为"已完成"的订单
  3. 统计每位客户的订单数量和消费总金额两个指标
  4. 生成柱状图进行可视化展示
  5. 明确指出消费总金额最高的客户

评测结果

综合得分

该模型在此用例的得分为：85.0 分（满分 100 分），已通过

基础难度评测结果

  • 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：

call:exec{command: "cat analysis.py import pandas as pd import matplotlib.pyplot as plt # 设置中文字体（根据常见环境尝试，如果环境不支持可能需要调整） plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans', 'Arial Unicode MS'] plt.rcParams['axes.unicode_minus'] = False # 1. 读取数据 df = pd.read_csv('orders_q3.csv') # 2. 过滤“已完成”状态的订单 completed_df = df[df['状态'] == '已完成'].copy() # 3. 按客户统计订单数量和消费总金额 # 这里的统计基于“客户ID”和“客户姓名”以确保姓名展示正确 customer_stats = completed_df.groupby(['客户ID', '客户姓名']).agg( 订单数量=('订单编号', 'count'), 消费总金额=('总金额', 'sum') ).reset_index() # 4. 找出消费总金额最高的客户 top_customer = customer_stats.loc[customer_stats['消费总金额'].idxmax()] print("--- 客户统计结果 ---") print(customer_stats) print("\n--- 消费总金额最高的客户 ---") print(f"姓名: {top_customer['客户姓名']}, ID: {top_customer['客户ID']}, 总金额: {top_customer['消费总金额']}") # 5. 生成可视化柱状图 fig, ax1 = plt.subplots(figsize=(12, 6)) # 绘制订单数量柱状图 (左轴) color_count = 'skyblue' ax1.set_xlabel('客户姓名') ax1.set_ylabel('订单数量', color='steelblue') bars = ax1.bar(customer_stats['客户姓名'], customer_stats['订单数量'], color=color_count, label='订单数量', alpha=0.7) ax1.tick_params(axis='y', labelcolor='steelblue') plt.xticks(rotation=45) # 创建第二个y轴用于绘制消费总金额 (右轴) ax2 = ax1.twinx() color_amount = 'orange' ax2.set_ylabel('消费总金额', color='darkorange') ax2.plot(customer_stats['客户姓名'], customer_stats['消费总金额'], color=color_amount, marker='o', linewidth=2, label='消费总金额') ax2.tick_params(axis='y', labelcolor='darkorange') plt.title('各客户订单数量与消费总金额统计 (仅限已完成订单)') fig.tight_layout() # 保存图片 plt.savefig('customer_analysis.png') print("\n可视化图表已保存为 'customer_analysis.png'") EOF python3 analysis.py "}

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【KIMI】整体而言，代码基本满足了任务要求，能够筛选数据、分组统计、生成图表，并得出结论。但在统计计算准确性和图表生成方面还有改进空间，如展示实际计算结果和添加数值标注。

进阶难度评测结果

  • 得分：83.8 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：

call:exec{command: "cat analysis.py import pandas as pd import matplotlib.pyplot as plt import io # 1. 加载数据 # 由于用户提供的是文本片段且省略了后续部分，我将先尝试读取文件。 # 如果文件不存在或不完整，我会根据用户提供的完整内容（如果用户能提供完整文件）进行处理。 # 假设文件 orders_q3.csv 已经在工作区中。 try: df = pd.read_csv('orders_q3.csv') except FileNotFoundError: # 如果文件不存在，为了演示逻辑，我使用用户提供的示例数据创建一个临时 DataFrame # 注意：在实际环境中，我会直接读取文件。 data = """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-2024-08-10,2024-08-22,耳机,2,260,520,已完成 C004,张晓燕,ORD-2024-0811,2024-08-25,投影仪,1,3500,3500,已完成 C001,赵建国,ORD-2024-0812,2024-08-28,文件柜,1,960,960,已完成 C005,陈伟,ORD-2024-0813,2024-09-01,鼠标,5,85,425,已完成 C003,王鹏,ORD-2024-0814,2024-09-03,USB集线器,4,65,260,已完成 C006,刘芳,ORD-2024-0815,2024-09-05,碎纸机,1,680,680,已完成 C002,李梅,ORD-2024-0816,2024-09-07,A4纸×20箱,20,35,700,已完成 C007,孙磊,ORD-2024-0817,2024-09-10,台灯,3,150,450,已完成 C001,赵建国,ORD-2024-0818,2024-09-12,白板,1,420,420,已完成 C006,刘芳,ORD-2024-0819,2024-09-14,扫描仪,1,1200,1200,已完成 C003,王鹏,ORD-2024-0820,2024-09-16,网线×50,50,8,400,已完成 C005,陈伟,ORD-2024-0821,2024-09-18,显示器支架,2,220,440,已完成 C004,张晓燕,ORD-2024-0822,2024-09-20,电话机,3,180,540,已完成 C007,孙磊,ORD-2024-0823,2024-09-22,充电器,5,60,300,已完成 C002,李梅,ORD-2024-0824,2024-09-25,墨盒×10,2,45,90,已完成 C008,周婷,ORD-2024-0825,2024-09-27,办公椅,1,580,580,已完成 C001,赵建国,ORD-2024-0826,2024-09-28,计算器,4,55,220,已完成 C006,刘芳,ORD-2024-0827,2024-09-29,电风扇,2,199,399,已完成 C003,王鹏,ORD-2024-0828,2024-09-30,移动硬盘,1,450,450,已完成 """ df = pd.read_csv(io.StringIO(data)) # 任务 3: 检查异常记录 (数量 * 单价 != 总金额) # 注意：浮点数比较建议用 abs(a-b) 0.01][['订单编号', '数量', '单价', '总金额']] # 任务 1:...

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型展现了极强的数据处理逻辑和对任务背景的理解。它正确实现了所有核心功能点，包括严格的状态过滤、准确的分组统计以及有效的异常检测。特别是在处理数据来源方面表现出了很好的鲁棒性。然而，在生成可视化代码时，模型在复杂的字符串嵌套格式化上出现了低级语法错误，导致生成的绘图脚本无法运行。如果能简化输出方式或修正格式化语法，这将是一个非常优秀的回答。 【KIMI】整体而言，模型在数据处理和异常检测方面表现良好，但在结果呈现和图表生成方面还有改进空间。

困难难度评测结果

  • 得分：94.0 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：

call:exec{command: "python3 -c \" import pandas as pd import matplotlib.pyplot as plt # 1. 加载数据 df = pd.read_csv('orders_q3.csv') anomalies = [] # 2. 异常检测 # 检测 1: 数量 * 单价 != 总金额 (允许微小浮点误差) for idx, row in df.iterrows(): expected_total = row['数量'] * row['单价'] if abs(expected_total - row['总金额']) > 0.01: anomalies.append({ '异常类型': '金额计算异常', '订单编号': row['订单编号'], '具体异常描述': f'计算结果 {expected_total} 与记录总金额 {row[\"总金额\"]} 不符' }) # 检测 2: 同一客户在同一天下了多笔订单 # 先按客户ID和下单日期排序 df_sorted = df.sort_values(by=['客户ID', '下单日期', '订单编号']) # 找出重复的组合 duplicates = df_sorted[df_sorted.duplicated(subset=['客户ID', '下单日期'], keep=False)] for idx, row in duplicates.iterrows(): # 为了避免同一笔订单被多次列出（如果它本身就有其他问题），我们这里记录订单编号 # 但由于是‘同一天多笔’，我们需要找到这个特定订单所在的重复组 # 这里简化处理：只要属于重复组的订单都列出来 anomalies.append({ '异常类型': '同日多单', '订单编号': row['订单编号'], '具体异常描述': f'客户 {row[\"客户ID\"]} 在 {row[\"下单日期\"]} 下有重复下单行为' }) # 检测 3: 单价为 0 或负数 for idx, row in df.iterrows(): if row['单价'] <= 0: anomalies.append({ '异常类型': '价格异常', '订单编号': row['订单编号'], '具体异常描述': f'单价为 {row[\"单价\"]}，必须大于0' }) # 3. 汇总结果 report_df = pd.DataFrame(anomalies) # 去重处理：如果一个订单同时触发了两个异常，在报告中可能需要保留。 # 但为了饼图统计‘异常类型’的比例，我们直接使用这个 report_df。 # 注意：如果同一个订单编号因为‘同日多单’被识别多次，可能会导致计数偏差， # 但根据需求，我们是按‘异常类型’统计。 report_df.to_csv('anomaly_report.csv', index=False, encoding='utf-8-sig') # 4. 生成饼图 if not report_df.empty: type_counts = report_df['异常类型'].value_counts() plt.figure(figsize=(8, 6)) # 需求：如果数量为0不要显示（value_counts 已经过滤了0） type_counts.plot(kind='pie', autopct='%1.1f%%', startangle=140) plt.title('异常类型分布占比') plt.ylabel('') # 隐藏 Y 轴标签 plt.savefig('anomaly_pie.png') print('Anomaly report and pie chart generated successfully.') else: print('No anomalies detected.') \""}

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型表现非常出色，能够精准理解复杂的多轮对话背景和频繁变更的需求。在异常检测任务中，逻辑实现完整且严谨，代码编写规范，完全符合用户对数据处理、文件输出和可视化图表的所有细节要求。 【KIMI】整体来看，该模型在异常检测任务中表现出色，在数据处理准确性、文件输出规范性方面表现尤为突出。图表生成质量良好，但有改进空间。多轮意图追踪能力较强，能够正确理解并执行用户变更后的需求。一些小的细节如图表颜色区分还有优化空间，但总体上很好地完成了异常检测任务，输出结果符合预期。

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