Google: Gemma 4 26B A4B 在「文档数据求和计算」的评测结果

基本信息

• 模型名称：Google: Gemma 4 26B A4B
• 用例名称：文档数据求和计算
• 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文档 `cost_analysis.docx` 的内容： **文档标题**：2024年第三季度项目进展报告 **作者**：产品运营部 **章节**：资源投入与成本分析 --- 本季度各项目组待回收账款明细如下： | 序号 | 项目组 | 待回收账款（元） | |------|--------|------------------| | 1 | 项目组A | 15000 | | 2 | 项目组B | 1624.28 | | 3 | 项目组C | 2773.15 | | 4 | 项目组D | 4150.82 | | 5 | 项目组E | 2120 | | 6 | 项目组F | 1836.54 | | 7 | 项目组G | 4240 | | 8 | 项目组H | 3865 | | 9 | 项目组I | 10372 | | 10 | 项目组J | 4295.5 | | 11 | 项目组K | 3184.6 | | 12 | 项目组L | 1950 | | 13 | 项目组M | 2180 | | 14 | 项目组N | 2335 | --- 请问这份文档中所有项目组的待回收账款合计是多少？

任务要求

AI 模型需要满足以下要求：

1. 正确识别文档中全部14笔待回收账款数据
2. 准确计算所有数值的总和
3. 最终给出的合计金额为 59926.89 元
4. 回答中应明确展示合计结果

评测结果

综合得分

该模型在此用例的得分为：98.7 分（满分 100 分），已通过

基础难度评测结果

• 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：

根据文档中提供的明细数据，各项目组待回收账款的计算如下： 15000 + 1624.28 + 2773.15 + 4150.82 + 2120 + 1836.54 + 4240 + 3865 + 10372 + 4295.5 + 3184.6 + 1950 + 2180 + 2335 = **59926.89** 这份文档中所有项目组的待回收账款合计是 **59,926.89 元**。

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型表现优秀，完美完成了从文档提取数值并进行准确计算的任务，过程透明，结果完全正确。 【KIMI】模型在数值提取完整性和计算准确性方面表现完美，成功识别并计算了文档中的全部待回收账款数据。在回答清晰度方面，虽然模型明确给出了合计结果，但缺少了对计算过程的详细说明，导致清晰度略有不足。整体而言，模型在完成本任务方面表现优秀，能够准确提取并计算文档中的数值数据，并给出清晰的合计结果。

进阶难度评测结果

• 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：

根据您提供的文档数据，计算结果如下： 1. **8个子项目外包服务费合计**： $12.85 + 8.36 + 15.72 + 6.90 + 9.48 + 11.25 + 7.60 + 13.40 = \mathbf{85.56}$ **万元** 2. **Q3总成本**（外包服务费合计 + 人力成本 + 云资源费用）： $85.56 + 186.5 + 42.3 = \mathbf{314.36}$ **万元** 3. **外包服务费占Q3总成本的百分比**： $(85.56 \div 314.36) \times 100\% \approx \mathbf{27.2\%}$

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型表现出色，在数值提取、算术运算和格式化输出方面都达到了最高水平。计算结果准确无误，过程清晰，完全符合评分标准。 【KIMI】整体评价：模型的回答质量很高，计算结果准确无误，计算过程清晰，并且以结构化的方式逐一回答了用户的问题。

困难难度评测结果

• 得分：66.0 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:edit{file: 'project_report.docx', text: '### 4.4 待回收账款\n以下为Q3期间各客户待回收账款明细（单位：万元）：\n客户A: 15.00\n客户B: 16.24\n客户C: 27.73\n客户D: 41.51\n客户E: 21.20\n客户F: 18.37\n客户G: 42.40\n客户H: 38.65\n客户I: 98.50\n客户J: 42.96\n客户K: 31.85\n客户L: 19.50\n客户M: 21.80\n客户N: 23.35\n客户O: 8.60\n待回收账款合计：467.66万元'}

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型仅完成了更新4.4节数据的部分任务，虽然数值正确且工具选择恰当，但对于4.3节的文本追加以及最后关于可用资金的数值计算请求完全没有响应。在处理多指令复合任务时表现出明显的遗漏，未能提供完整的最终结果。 【KIMI】整体而言，候选人的表现非常出色，除了在指令执行完整性方面缺少了计算可用资金的明确步骤外，其他方面均符合要求，正确执行了所有指令，数值计算准确，工具调用合理，并且正确追踪了多轮对话中的意图变更。

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：「文档数据求和计算」
• 查看该模型的所有评测结果：Google: Gemma 4 26B A4B
• 浏览同维度的其他用例：「文档问答」
• 返回排行榜首页