Google: Gemma 4 26B A4B 在「地址信息整理入档」的评测结果

基本信息

• 模型名称：Google: Gemma 4 26B A4B
• 用例名称：地址信息整理入档
• 测试类型：xsct-a
• 评测维度：文档多轮对话

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：

请帮我创建一个文件 addresses.md，把以下5个地址按统一格式整理进去，每条地址单独一行，格式为「序号. 区域 - 街道/路名 - 小区/楼栋名称」，如果地址中没有明确的街道/路名则该字段填"无"。地址如下： 1. 滨海区锦绣新城夏园 2. 青山区云栖里巷 3. 青山区云栖里花园 4. 青山区观澜路博雅学府B座 5. 海岳区望江路瀚海公馆小区

任务要求

AI 模型需要满足以下要求：

1. 创建名为 addresses.md 的文件
2. 文件中包含5条地址，每条单独一行
3. 每条地址格式为「序号. 区域 - 街道/路名 - 小区/楼栋名称」
4. 对于没有明确街道/路名的地址，街道/路名字段填写"无"
5. 地址顺序与用户提供的顺序一致

评测结果

综合得分

该模型在此用例的得分为：94.7 分（满分 100 分），已通过

基础难度评测结果

• 得分：94.7 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：

call:write{content: '1. 滨海区 - 无 - 锦绣新城夏园 2. 青山区 - 云栖里巷 - 无 3. 青山区 - 无 - 云栖里花园 4. 青山区 - 观澜路 - 博雅学府B座 5. 海岳区 - 望江路 - 瀚海公馆小区',path: 'addresses.md'}

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，完美执行了所有指令要求。它不仅在格式上做到了精确对齐，而且在处理模糊地址（如区分巷名与花园名）时表现出了很好的逻辑判断能力，完全符合预期的整理结果。 【KIMI】整体来看，生成结果较好地完成了创建addresses.md文件并整理地址的任务。格式正确性高，字段拆分大部分准确，文件创建与内容完整性良好。但在第2条地址的字段拆分上存在一点小瑕疵，总体表现优秀。

进阶难度评测结果

• 得分：81.5 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:write{contents: "东湖区翠柳路阳光花园A栋 清城区碧波巷锦华花园 北城区观澜路博雅学府B座",path: "address_list.txt"}

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型在逻辑理解和上下文处理上非常出色，能够准确处理复杂的修改和删除逻辑。然而，在执行细节上存在明显疏漏：一是忽略了用户关于“序号重排”的格式要求，导致输出内容缺失序号；二是未能履行“告知完整内容”的对话义务，仅完成了后台文件写入操作，缺乏对用户请求的直接回应。 【KIMI】模型整体表现优秀，能够准确理解和执行用户的指令，正确处理上下文信息和矛盾指令，最终结果呈现也基本符合要求。但在重排序号和结果呈现的表述上还有改进空间。

困难难度评测结果

• 得分：64.0 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:write{contents: '编号 | 所属城区 | 详细路名 | 楼盘全称 | 备注 ---|---|---|---|--- 1 | 龙华区 | 长安街 | 龙腾雅苑 | 已确认 2 | 朝阳区 | 建设大道 | 鼎盛华庭 | 需实地勘察 3 | 朝阳区 | 观澜路 | 博雅学府B座 | 待确认 4 | 海岳区 | 望江路 | 瀚海公馆 | 需实地勘察',path: 'addresses.md'}

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型在理解复杂的备注修改逻辑和维持表格格式方面表现良好，但在处理多轮对话的数据完整性上出现了严重疏漏，丢失了一条地址记录。此外，模型完全忽略了用户关于统计数据的指令，未能完成任务的统计与展示要求。 【KIMI】模型整体表现良好，能够正确追踪上下文和处理矛盾指令，但在操作准确性和统计展示方面还有改进空间。

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：「地址信息整理入档」
• 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：「文档多轮对话」
• 返回排行榜首页