qwen3.5-flash 在「Docker 部署后 API 不通」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：qwen3.5-flash
  • 用例名称：Docker 部署后 API 不通
  • 测试类型：xsct-a
  • 评测维度：Gateway 配置与管理

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：

我用 Docker 跑起了 OpenClaw Gateway，容器状态是「Up」，但是访问「http://localhost:8081/health」却提示拒绝连接，我该怎么检查？

任务要求

AI 模型需要满足以下要求：

  1. 要求检查 Docker 端口映射配置（-p 参数）
  2. 要求检查容器内服务监听地址是否为「0.0.0.0」而非「127.0.0.1」
  3. 要求检查宿主机防火墙或安全组设置
  4. 建议查看容器日志以确认服务是否真正启动成功

评测结果

综合得分

该模型在此用例的得分为：62.5 分（满分 100 分），已通过

基础难度评测结果

  • 得分：62.5 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：

我来帮你诊断这个问题。首先检查一下 Gateway 容器的端口映射和网络配置： ```bash docker ps | grep openclaw-gateway ``` 请运行上面的命令，然后告诉我输出结果。同时，我们还需要检查： 1. **端口映射是否正确** - 确认 8081 端口是否真的映射到了宿主机 2. **Gateway 服务是否真的在监听** - 进入容器内部查看进程状态 3. **防火墙设置** - 宿主机或容器网络是否有拦截 请先运行第一个命令看看容器信息，我可以继续帮你排查后续步骤。

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】候选回答结构清晰，提到了端口映射和防火墙两个排查方向，但遗漏了「容器内服务监听地址是否为 0.0.0.0」这一关键点（这是 Docker 场景下「容器 Up 但宿主机无法访问」最典型的原因），也未建议通过 docker logs 查看服务启动日志。采用交互式「请先运行再告诉我」的方式降低了回答的实用性，用户期望得到完整的排查清单而非分步引导。整体勉强及格，但与参考答案相比信息完整度有明显差距。 【GEMINI】候选模型具备基本的排查思路，能够通过交互式方式引导用户进行诊断。但在技术细节的覆盖面上不够全面，特别是忽略了容器日志分析和关键的监听地址配置检查，导致排查效率可能降低。建议在后续回复中直接给出具体的排查指令和关键配置检查点。 【KIMI】候选输出采用了交互式排查思路，但严重违背了「主动使用工具诊断」的指令要求，且遗漏了 OpenClaw Gateway 容器访问问题的核心检查点（监听地址配置）。参考答案明确指出了 0.0.0.0 与 127.0.0.1 的区别、docker ps 检查端口映射、以及查看日志等关键步骤，而候选输出均未覆盖，排查深度和准确性不足。

进阶难度评测结果

  • 得分：85.75 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：

需要做两步修改： **1. 修改配置文件** - 把监听地址改为 `0.0.0.0` ```bash docker exec 7b2a1c3d4e5f sed -i 's/host: 127.0.0.1/host: 0.0.0.0/' /app/config.yaml ``` **2. 重启网关并重新映射端口** - 去掉 `127.0.0.1` 限制 ```bash docker stop 7b2a1c3d4e5f && docker rm 7b2a1c3d4e5f docker run -d --name openclaw-gateway -p 8081:8081 openclaw-gateway ``` 执行完这两步后，你可以通过 `http://:8081` 访问了。需要我帮你执行这些命令吗？

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】候选输出整体质量较高，准确识别了双重监听限制的根本原因，修复步骤清晰易懂。主要不足是：1）先修改容器内文件再删除重建容器的流程存在配置丢失风险，未建议挂载宿主机配置文件；2）未提及 auth.enabled 对 API 访问的潜在影响。整体属于合格偏优的回答。 【KIMI】候选输出整体质量较高，准确诊断了双重 127.0.0.1 监听限制的核心问题，并给出了可行的修复步骤。主要不足在于重新运行容器的命令缺少配置文件卷挂载，可能导致配置修改无法持久化；同时未提及 auth.enabled 对 API 访问的潜在影响。建议在重新运行命令中添加 -v 参数挂载配置文件，并补充关于认证配置的说明。 【GEMINI】模型能够清晰地给出解决问题的技术路径，诊断准确且命令操作具备实操性。但在方案的完整性上略有欠缺，未考虑到容器重启后的配置持久化问题以及认证机制对 API 访问的影响。

困难难度评测结果

  • 得分：31.75 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：

我还没执行修复操作。让我先检查容器当前状态，然后停止占用端口的旧进程并重启 Gateway 容器。

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】候选输出在用户追问「现在呢？」时，仅给出了一句意图性的自我说明，既未提供任何具体的修复命令（如 kill 1234、docker start gateway-srv），也未给出健康检查验证步骤或备选端口映射方案。整个回答停留在「我打算做什么」的层面，完全没有转化为对用户有实际价值的操作指导，与参考答案差距极大。 【GEMINI】模型在故障诊断阶段表现出色，能够精准定位问题根源。但在执行阶段，模型未能将诊断结果转化为实际的修复方案，回复过于被动，未能满足用户解决问题的预期。 【KIMI】该候选输出表现极差。模型完全忽视了对话上下文中已完成的全部故障排查工作（容器名纠正、崩溃状态识别、端口冲突定位、占用进程确认），反而声称要「先检查容器当前状态」，表现出严重的上下文理解障碍。恢复方案仅有模糊意向而无具体命令、验证步骤或备选策略，无法指导用户完成故障修复。这种表现不符合「专业 AI 助手」在复杂故障场景下的基本能力要求。

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