Google: Gemma 4 26B A4B 在「职业列表排序整理」的评测结果

基本信息

• 模型名称：Google: Gemma 4 26B A4B
• 用例名称：职业列表排序整理
• 测试类型：xsct-a
• 评测维度：文档格式排版

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：

我有一份文档 workers.txt，里面列出了几种技术工种：电焊工、木工、数控机床操作员。请按照从高级到初级的职级顺序（高级技师 > 技师 > 高级工 > 中级工 > 初级工）将这三个工种重新排列成表格形式，每行包含序号、工种名称和对应职级。

任务要求

AI 模型需要满足以下要求：

1. 读取 workers.txt 文件内容
2. 按照高级技师 > 技师 > 高级工 > 中级工 > 初级工的职级顺序排列三个工种
3. 输出为表格形式，每行包含序号、工种名称、对应职级三列
4. 将排列结果写回文件或以文档形式呈现

评测结果

综合得分

该模型在此用例的得分为：61.3 分（满分 100 分），已通过

基础难度评测结果

• 得分：61.3 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：

call:read{path: "workers.txt"}

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型展现了专业的 AI 助手素质，能够准确识别任务依赖，并采取了正确的首步动作（读取文件）。在多轮对话的 Agent 场景下，这是一个完美的开始。然而，由于本次评估针对的是单次生成结果，模型尚未执行核心的排序逻辑和表格化输出工作，导致在格式维度上缺失分数。综合来看，其行为逻辑正确但任务尚未闭环。 【KIMI】候选人正确理解了任务要求，并正确调用了read工具读取文件，但在职级分配和表格输出方面尚未完成，需要进一步的操作来满足格式规范性和工具调用准确性的要求。

进阶难度评测结果

• 得分：3.0 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：

call:read:read{path: 'jobs.md'}

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型仅对用户指令中的“读取”动作做出了初步响应，未能执行排序、格式化及写回文件等关键指令。虽然工具调用的语法规范，但由于未提供实质性的整理结果，导致任务完成度极低。 【KIMI】整体评价：候选人仅完成了读取文件的操作，未进行任何排序和写回文件的操作，因此各项得分均为0。需要按照题目要求完成排序和写回文件的操作，才能得到较高的评分。

困难难度评测结果

• 得分：96.5 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{edit_commands: [{file: 'staff_list.docx', text: '# 技术工种人员名册\n\n## 高级工\n1. 高级电工 张三\n2. 高级钳工 吴九\n3. 高级焊工 赵六\n\n## 中级工\n1. 中级焊工 李四\n2. 中级钳工 郑十\n3. 中级电工 孙七\n\n## 初级工\n1. 初级电工 冯十一\n2. 初级钳工 王五\n3. 初级焊工 周八\n\n---\n共计9人'}], filename: 'staff_list.docx'}

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，能够完美处理复杂的指令变更链。它不仅准确执行了分组和多重排序逻辑（包括特定组的倒序排列），还精确完成了 Markdown 格式的排版要求。输出结果与参考答案完全一致，体现了极强的指令遵循能力和逻辑一致性。 【KIMI】整体而言，模型表现出了良好的理解和执行能力，能够准确追踪用户的意图变更，并按照要求完成文档的格式排版和内容排序。

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：「职业列表排序整理」
• 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：「文档格式排版」
• 返回排行榜首页