qwen3.6-plus-preview 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：qwen3.6-plus-preview
- 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：14.29 分（满分 100 分），未通过
基础难度评测结果
- 得分：14.29 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：
仅看前10行的话可以手动统计，但文件共有 87 条记录，我先读取完整文件再做准确统计。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【KIMI】候选人输出中并未提供具体的统计结果，而是表示需要读取完整文件后再进行统计，因此没有正确完成统计前10行数据中每位客户的订单条数的任务。 【CLAUDE】候选输出未完成评测任务。用户明确要求「根据前10行数据统计每位客户的订单记录条数」，这是一个可以直接从给定数据中手动统计得出的简单任务，参考答案已清晰列出C001至C005五位客户及其对应的3/3/2/1/1条订单。但候选输出选择读取完整文件而非回答问题，导致完全没有提供任何统计结果。这属于任务理解偏差：将「基于前10行样本统计」误解为「需要完整数据才能统计」。核心评测点（准确提取客户及订单条数）完全未达成。
进阶难度评测结果
- 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：
前10行中非"已完成"状态的订单共有 **2 笔**： | 订单编号 | 客户姓名 | 商品名称 | 当前状态 | 金额（元） | |----------|----------|----------|----------|------------| | ORD-2024-0805 | 李梅 | 键盘 | 退款中 | 320 | | ORD-2024-0808 | 王鹏 | 路由器 | 配送中 | 380 | **占比计算：** - 非完成订单总金额：320 + 380 = **700 元** - 前10行订单总金额：1160 + 2300 + 135 + 3600 + 320 + 900 + 1200 + 380 + 540 + 520 = **11,055 元** - 占比：700 ÷ 11,055 ≈ **6.33%**
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出与参考答案高度吻合，信息提取、数值计算和格式呈现三个维度均达到满分水平。回答结构清晰，逻辑严谨，计算过程透明，是一份高质量的标准答案。 【GEMINI】该模型的表现非常出色。它完美执行了信息提取任务，在处理CSV数据时展现了极高的准确性。计算逻辑清晰且完全正确，格式规范，完全符合评测标准的要求。 【KIMI】模型对前10行CSV数据的分析完全正确，准确识别出2笔非完成状态订单，所有数值计算精确无误，结果以清晰的表格和结构化方式完整呈现。整体表现优秀，符合任务要求。
困难难度评测结果
- 得分：9.1 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：
好的，我来读取完整文件，同时做这两个分析。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出极度不完整，仅是一句承诺性的开场白，没有执行任何实际分析。既未调用工具读取完整数据，也未完成按月份对比已完成订单的统计，更未计算退款总金额和扣除后的排名对比。整体表现接近于无效回复，与参考答案要求的完整双任务分析相差甚远。 【KIMI】模型虽然正确理解了用户的需求变化，但并没有实际执行数据分析任务，也没有给出任何具体的输出结果。在数据提取准确性、工具使用合理性和输出结构与清晰度这三个关键维度上都未能满足要求，整体表现不合格。
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