MiniMax-M2.7 在「CSV订单数据摘要」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：MiniMax-M2.7
- 用例名称：CSV订单数据摘要
- 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 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） 请根据以上前10行数据回答： 1. 赵建国一共下了几笔订单？分别买了什么商品？ 2. 哪位客户有一笔订单处于"配送中"状态？对应的商品和金额是多少？ 3. 在这10行数据中，单笔总金额最高的订单是哪一笔？请给出订单编号、客户姓名和总金额。
任务要求
AI 模型需要满足以下要求：
- 准确识别赵建国（C001）在前10行中出现的3笔订单及对应商品（办公椅、墨盒×10、办公桌）
- 正确指出王鹏（C003）有一笔配送中的订单，商品为路由器，金额380元
- 正确识别单笔总金额最高的订单为ORD-2024-0804，客户王鹏，总金额3600元
- 回答结构清晰，按问题编号逐一作答
评测结果
综合得分
该模型在此用例的得分为：99.7 分（满分 100 分），已通过
基础难度评测结果
- 得分：99.7 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：
用户提供了 orders_q3.csv 文件的前10行数据，要求回答三个问题。我可以直接根据数据内容来回答。 1. 赵建国的订单： - ORD-2024-0801：办公椅，2台，1160元 - ORD-2024-0803：墨盒×10，3件，135元 - ORD-2024-0807：办公桌，1台，1200元 共3笔订单 2. 配送中的订单： - ORD-2024-0808：客户C003王鹏，路由器，380元 3. 单笔总金额最高： - ORD-2024-0804：客户C003王鹏，显示器2台，3600元 根据提供的数据： **1. 赵建国的订单（共3笔）：** | 订单编号 | 商品名称 | 数量 | 总金额 | |---------|---------|------|--------| | ORD-2024-0801 | 办公椅 | 2 | ¥1,160 | | ORD-2024-0803 | 墨盒×10 | 3 | ¥135 | | ORD-2024-0807 | 办公桌 | 1 | ¥1,200 | --- **2. 处于"配送中"状态的订单：** - **客户**：王鹏（C003） - **商品**：路由器 - **金额**：¥380（订单编号：ORD-2024-0808） --- **3. 单笔总金额最高的订单：** - **订单编号**：ORD-2024-0804 - **客户姓名**：王鹏 - **总金额**：¥3,600（商品为显示器，2台）
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出在三个评分维度上均表现优异，完全准确地回答了所有问题，信息完整无遗漏，且采用表格和列表相结合的方式使回答结构清晰、易读。回答质量与参考答案高度吻合，并在部分细节上（如补充商品数量、客户ID等）有所增强，整体表现堪称满分。 【GEMINI】模型表现优秀，能够准确理解 CSV 数据内容并进行关键信息提取。回答逻辑严密，格式规范，完全满足了用户提出的所有要求。 【KIMI】该回答表现优秀，完全基于提供的CSV数据准确提取了所有关键信息，三个问题的答案均正确无误。回答结构清晰、格式规范，使用了表格等可视化手段提升可读性。整体符合专业AI评测标准，无明显缺陷。
进阶难度评测结果
- 得分：65.2 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：
The user wants me to: 1. Translate the customer summary table to English with specific headers 2. Remove the "元" from amounts 3. Sort by Completed Order Total from high to low 4. Explain what Li Mei's completed order total would be if the refund order is successfully refunded From the data: - C001 赵建国: 3 orders, completed total = 1160 + 135 + 1200 = 2495 - C002 李梅: 3 orders, completed total = 2300 + 520 = 2820 (excluding the refund order of 320) - C003 王鹏: 2 orders, completed total = 3600 + 380 = 3980 - C004 张晓燕: 1 order, completed total = 900 - C005 陈伟: 1 order, completed total = 540 Sorted by Completed Order Total (high to low): 1. C003 王鹏: 3980 2. C002 李梅: 2820 3. C001 赵建国: 2495 4. C004 张晓燕: 900 5. C005 陈伟: 540 If the refund order (ORD-2024-0805, 320) is successfully refunded, Li Mei's completed total would remain 2820 because the refund order was not completed in the first place — it was in "退款中" (refund in progress) status. The refund, if successful, would reduce her paid amount, not increase her completed order total. Wait, I need to think about this more carefully. The user is asking about the impact on "已完成订单总金额" (Completed Order Total). The refund order ORD-2024-0805 has status "退款中", meaning it was never completed in the first place. So if the refund is successfully processed, it wouldn't change her completed order total since that order never contributed to it in the first place — she'd still have 2820 in completed orders. The refund only affects her financial records, not the completed order count. **Customer Summary Table (Sorted by Completed Order Total, Descending)** | Customer ID | Customer Name | Order Count | Completed Order Total | |-------------|---------------|-------------|----------------------| | C003 | Wang Peng | 2 | 3600 | | C002 | Li Mei | 3 | 2820 | | C001 | Zhao Jianguo | 3 | 2495 | | C004 | Zhang Xiaoyan | 1 | 900 | | C005 | Chen Wei | 1 | 540 | **关于退款对李梅已完成金额的影响：** 实际上，**李梅的已完成订单总金额不会发生变化，仍为 2,820**。...
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出在英文表格翻译和格式规范方面表现良好，表头、金额格式、客户姓名翻译均符合要求，排序也正确。信息提取基本准确。但在退款假设问题的回答上存在明显不足：未能给出参考答案期望的「退款成功后总金额变为2500元（2820-320=2500）」这一核心计算结论，而是简单判断「金额不变」，导致追问回答完整性得分较低，影响整体质量。 【GEMINI】模型在表格格式化和翻译方面表现出色，但在处理 CSV 数据时对「状态」字段的过滤逻辑存在疏忽，导致王鹏的订单总金额计算错误。关于退款的追问，模型给出了合理的业务解释，但未能充分展示假设性计算过程。 【KIMI】模型在计算C003王鹏的已完成订单总金额时出现严重错误。根据数据，王鹏有两笔订单：ORD-2024-0804（显示器，3600元，已完成）和ORD-2024-0808（路由器，380元，配送中）。因此已完成订单总金额应为3600元，而非模型计算的3980元。模型错误地将配送中状态的订单计入了已完成金额。此外，排序也因此错误，C003应排在C002之后。其他客户数据计算正确。
困难难度评测结果
- 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：
The user wants me to: 1. Create a comparative table of key vascular parameters between the two studies 2. Use bilingual (Chinese/English) headers for the table 3. Provide bilingual analysis conclusions 4. Include a note about "退款中" and "配送中" orders from orders_q3.csv (first 10 rows) as a "data completeness check" appendix Let me extract the data: From the clinical report: - 受区动脉：胫后动脉（PTA），外径3.8-5.2 mm - 供区血管蒂直径：0.8-1.5 mm - 管径比值范围：2.8-5.0倍 - 动脉切开口径倍数：3.0倍 - 受区远端流量：>70 ml/min - 皮瓣血流量范围：12-22 ml/min Now looking at the comparative study data with the 股深动脉（DFA）showing larger vessel diameters ranging from 4.8-6.5 mm, thicker donor pedicles at 1.2-2.0 mm, slightly lower diameter ratios of 2.5-4.5倍, and different flow characteristics. Looking at the order status breakdown, I see there's 1 order with a refund in progress and 1 currently being delivered, while the vast majority have already been completed. Now I'm setting up a bilingual comparison table to present these findings clearly. **关键血管参数对比分析：** | 参数 / Parameter | 本研究（SCIP皮瓣）Current Study (SCIP Flap) | 对比研究（DFA受区）Comparative Study (DFA Recipient) | |---|---|---| | 受区动脉 / Recipient Artery | 胫后动脉（PTA）Posterior Tibial Artery (PTA) | 股深动脉（DFA）Deep Femoral Artery (DFA) | | 受区动脉外径范围 / Recipient Artery Diameter Range | 3.8–5.2 mm | 4.8–6.5 mm | | 供区血管蒂直径范围 / Flap Pedicle Diameter Range | 0.8–1.5 mm | 1.2–2.0 mm | | 管径比值范围 / Vessel Diameter Ratio Range | 2.8–5.0 倍 | 2.5–4.5 倍 | | 动脉切开口径倍数 / Arteriotomy Length Ratio | 3.0 倍 | 3.5 倍 | | 受区远端流量 / Distal Recipient Flow | >70 ml/min | >85 ml/min | | 皮瓣血流量范围 / Flap Perforator Flow Range | 12–22 ml/min | 18–28 ml/min | --- **分析结论 / Analysis Conclusion：** **中文：** 两项研究在受区动脉选择上存在显著差异。本研究选用PTA作为受区动脉，其外径（3.8–5.2 mm）明显小于对比研究中DFA的外径（4.8–6.5 mm），两者相差约1.0–1.3 mm。这一差异直接导致了两组供区血管蒂直径的不同——SCIP皮瓣的血管蒂（0.8–1.5 mm）显著细于对比研究的供区血管（1.2–2.0 mm），管径比值也因此呈现不同分布区间。尽管如此，本研究中动脉切开口径倍数（3.0倍）与对比研究（3.5倍）均落在了各自管径比值范围的合理区间内，术后血流动力学随访结果均满意（PTA远端>70 ml/min，DFA远端>85...
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】该模型在本次复杂多轮任务中表现优异，完整且准确地完成了血管参数对比表格的生成、中英双语分析结论的撰写以及CSV数据的跨文档整合。6个血管参数全部正确提取，双语医学术语专业规范，CSV附注数据准确无误（退款中1笔、配送中1笔），整体输出结构清晰（表格→结论→附注）。对比分析具有一定临床深度，能指出两组研究的关键差异及其临床意义。多轮意图追踪能力强，历次用户意图变更均被正确响应。唯一小瑕疵是对比分析中未深入讨论管径比值范围的重叠区间，但这属于锦上添花的内容，不影响整体高质量输出。 【GEMINI】模型表现卓越，不仅在多轮复杂的意图变更中保持了极高的准确性，而且在处理跨文档（CSV数据与临床报告）的综合分析任务时展现了极强的逻辑整合能力。输出格式规范，双语表达专业，完全满足所有评测标准。 【KIMI】该候选输出表现优异，在所有评分维度均达到或接近满分。信息提取零误差，对比分析专业深入，双语输出规范准确，跨文档整合完美执行，多轮意图追踪清晰无误。整体结构层次分明（表格→分析结论→附注），医学专业性强，完全满足用户的复杂复合需求。 minor建议：分析结论可适当精简以提升可读性。
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