Google: Gemma 4 26B A4B 在「图片文字提取」的评测结果

基本信息

• 模型名称：Google: Gemma 4 26B A4B
• 用例名称：图片文字提取
• 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：

请帮我提取 workspace/notice.png 图片中的文字内容，并保存到 workspace/ocr_result.txt 文件中。

任务要求

AI 模型需要满足以下要求：

1. 正确识别用户意图为对指定图片进行OCR文字提取
2. 读取或处理 workspace/notice.png 图片文件
3. 将提取到的文字内容写入 workspace/ocr_result.txt 文件
4. 输出的文本应保留原图中文字的基本段落结构

评测结果

综合得分

该模型在此用例的得分为：92.0 分（满分 100 分），已通过

基础难度评测结果

• 得分：92.0 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:exec{command: "tesseract workspace/notice.png workspace/ocr_result"}

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型表现非常出色，能够精准识别用户意图，并利用系统环境中最直接有效的 OCR 工具（Tesseract）完成任务。工具调用语法完全正确，且遵循了系统提示词中关于‘不叙述常规低风险工具调用’的指令，简洁高效。 【KIMI】模型整体表现良好，能够理解用户意图并正确调用工具执行OCR命令。但在工具调用的准确性和输出格式规范性方面还有改进空间。

进阶难度评测结果

• 得分：93.8 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: "# 收据信息 - **日期**: 2024-03-15 - **商户名称**: 城市便利超市 - **支付方式**: 微信支付 - **交易单号**: TX20240315078923 | 商品名 | 数量 | 金额 | | :--- | :--- | :--- | | 矿泉水 | 2 | 4.00 | | 面包 | 1 | 8.50 | | 纸巾 | 3 | 15.00 | | **合计** | | **27.50** | ",path:"receipt_info.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，能够精准执行工具调用并处理复杂的格式化要求。唯一的瑕疵在于对“合计”字段的归类未完全遵循用户关于“表格上方列表”和“表格明细”的划分逻辑，将其作为表格的一行处理了。整体排版规范，信息提取准确。 【KIMI】整体来看，模型表现良好，正确理解了用户的需求并执行了相应的操作。Markdown格式的使用基本正确，但在表格的呈现上有一点小瑕疵，合计金额的展示方式需要调整以符合常规的表格使用习惯。

困难难度评测结果

• 得分：88.0 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: " body { font-size: 14px; line-height: 1.6; color: #333; } h1, h2 { color: #1a3c6e; } table { border-collapse: collapse; width: 100%; margin: 10px 0; } table, th, td { border: 1px solid black; } th, td { padding: 8px; text-align: left; } 第一章 项目概述 1.1 背景介绍 本项目旨在构建一套智能文档管理系统，支持多种格式的文件解析与检索。系统需要满足以下核心需求： 支持 PDF、Word、图片等格式的导入 提供全文检索与语义搜索能力 具备权限管理与审计日志功能 1.2 目标用户 企业内部知识管理团队、法务合规部门、研发文档管理人员。 第二章 技术方案 2.1 架构设计 采用微服务架构，主要包含以下模块： 序号 模块名称 （1） 文件解析服务 （2） 索引构建服务 （3） 查询网关服务 2.2 关键技术 OCR引擎：Tesseract + PaddleOCR 双引擎融合 向量检索：基于 FAISS 的近似最近邻搜索 ",filename:"extracted.html"}

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型在内容转换、HTML 结构设计和 CSS 样式应用方面表现非常出色，生成的内容完全符合用户的业务需求。但在工具链执行方面存在瑕疵，忽略了用户要求通过 read 工具进行回读确认的指令，导致任务流程不完整。 【KIMI】模型整体表现良好，能够理解用户意图变更并生成符合要求的HTML文件。在HTML结构、语义和CSS样式方面都基本满足题目要求，只是在CSS样式定义中有少量遗漏。同时需要注意使用read工具回读文件内容，以确保生成结果的正确性。

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：「图片文字提取」
• 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