Google: Gemma 4 26B A4B 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：Google: Gemma 4 26B A4B
- 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：97.3 分（满分 100 分），已通过
基础难度评测结果
- 得分：97.3 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：
根据您提供的 `orders_q3.csv` 前10行数据，每位客户的订单记录统计如下： | 客户姓名 | 订单记录数 | | :--- | :--- | | 赵建国 | 3 条 | | 李梅 | 3 条 | | 王鹏 | 2 条 | | 张晓燕 | 1 条 | | 陈伟 | 1 条 | **统计过程说明：** 1. **赵建国 (C001)**: 出现了 3 次 (ORD-2024-0801, ORD-2024-0803, ORD-2024-0807) 2. **李梅 (C002)**: 出现了 3 次 (ORD-2024-0802, ORD-2024-0805, ORD-2024-0810) 3. **王鹏 (C003)**: 出现了 2 次 (ORD-2024-0804, ORD-2024-0808) 4. **张晓燕 (C004)**: 出现了 1 次 (ORD-2024-0806) 5. **陈伟 (C005)**: 出现了 1 次 (ORD-2024-0809)
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】该回答表现完美。它不仅准确提取了CSV数据中的关键信息并完成了统计任务，还通过结构化的表格和详细的逻辑说明使结果一目了然。同时，模型严格遵守了仅处理前10行数据的限制条件，是一个高质量的回复。 【KIMI】整体来看，候选人输出在信息提取准确性和表达清晰度上都做得很好，范围意识也较好，只是在表达方式上有微小的改进空间。
进阶难度评测结果
- 得分：98.7 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：
在这10行数据中，非“已完成”状态的订单如下： | 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 | ### 比例计算说明： 1. **前10行总金额计算：** 1160 + 2300 + 135 + 3600 + 320 + 900 + 1200 + 380 + 540 + 520 = **11,055 元** 2. **非完成状态订单总金额：** 320 (键盘) + 380 (路由器) = **700 元** 3. **占比计算：** 700 ÷ 11,055 ≈ **6.33%** **结论：** 这些非完成状态的订单总金额占前10行总金额的比例约为 **6.33%**。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】生成结果非常专业且准确。模型不仅完美执行了信息提取和数学计算任务，还通过结构化的呈现方式使结果易于阅读。计算逻辑透明，数据引用准确，是一个高质量的回答。 【KIMI】整体而言，候选人输出在信息提取准确性和数值计算正确性方面表现完美，呈现格式与完整性方面略有小瑕疵，但不影响结果的清晰性和完整性。
困难难度评测结果
- 得分：95.2 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，我明白您的新需求了。我将分两步进行详细分析： 1. **月度消费对比分析**：统计每个客户在 8 月和 9 月期间，状态为「已完成」的订单金额，并进行对比。 2. **退款影响评估**： * 计算所有「退款中」订单的总金额。 * 将这些退款金额从原有的客户总消费中扣除，重新计算排名，看排名是否发生变化。 我现在开始处理。 thought call:exec{command: "python3 -c \" import csv from collections import defaultdict orders = [] with open('orders_q3.csv', mode='r', encoding='utf-8') as f: reader = csv.DictReader(f) for row in reader: row['数量'] = int(row['数量']) row['单价'] = float(row['单价']) row['总金额'] = float(row['总金额']) orders.append(row) # 1. 月度消费对比 (仅限 '已完成') monthly_stats = defaultdict(lambda: {'8月': 0.0, '9月': 0.0}) for o in orders: if o['状态'] == '已完成': month = o['下单日期'].split('-')[1] customer = f\"{o['客户ID']} {o['客户姓名']}\" if month == '08': monthly_stats[customer]['8月'] += o['总金额'] elif month == '09': monthly_stats[customer]['9月'] += o['总金额'] print('=== 8月 vs 9月已完成订单消费对比 ===') for cust, months in sorted(monthly_stats.items(), key=lambda x: (x[1]['8月'] + x[1]['9月']), reverse=True): print(f\"{cust}: 8月 {months['8月']:.2f}元 | 9月 {months['9月']:.2f}元\") # 2. 退款影响分析 refund_total = 0.0 refund_by_cust = defaultdict(float) for o in orders: if o['状态'] == '退款中': refund_total += o['总金额'] refund_by_cust[o['客户ID']] += o['总金额'] print(f'\\n=== 退款分析 ===') print(f'退款中订单总金额: {refund_total:.2f}元') # 原排名 (基于全部订单总额) original_totals = defaultdict(float) for o in orders: original_totals[o['客户ID']] += o['总金额'] original_ranking = sorted(original_totals.items(), key=lambda x: x[1], reverse=True) # 扣除退款后的新排名 adjusted_totals = {} for cid, total in original_totals.items(): adjusted_totals[cid] = total - refund_by_cust[cid] adjusted_ranking = sorted(adjusted_totals.items(), key=lambda x: x[1], reverse=True) # 获取姓名映射以便输出 id_to_name = {o['客户ID']: o['客户姓名'] for o in orders} print('\\n原排名 (按总金额):') for i, (cid, amt) in enumerate(original_ranking, 1): print(f\"{i}. {cid} {id_to_name[cid]}: {amt:.2f}元\") print('\\n扣除退款后排名:') for i, (cid, amt) in enumerate(adjusted_ranking, 1): print(f\"{i}. {cid} {id_to_name[cid]}: {amt:.2f}元\") # 检查排名变化 changed = False for i...
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】这是一次教科书级别的任务处理。模型展现了极强的数据处理意识和逻辑严密性，能够准确理解用户复杂的变更指令，并通过编写高质量的脚本工具来确保 87 条数据的统计准确无误。不仅完成了所有分析任务，还预想到了结果的对比呈现，表现非常专业。 【KIMI】整体而言，模型表现出了良好的意图追踪能力，准确地处理了数据，并以结构化的方式呈现了结果。在数据提取准确性和工具使用合理性方面表现突出，但在输出的清晰度上还有提升空间。
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