qwen3.5-35b-a3b 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：qwen3.5-35b-a3b
  • 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：29.0 分（满分 100 分），未通过

基础难度评测结果

  • 得分：29.0 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：

我来帮你检查一下这个问题。首先查看一下 Gateway 的容器状态和端口映射情况。 ```bash docker ps | grep openclaw ```

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】候选输出几乎没有提供有价值的排查内容。仅输出了一条初步的 docker ps 命令，缺少对核心问题（端口映射缺失、监听地址配置错误、服务启动失败等）的任何分析与指导，与参考答案的完整性差距悬殊。回答未完成，用户无法从中获得实际帮助。 【GEMINI】候选模型仅输出了一个简单的命令，虽然符合「工具调用」的交互风格，但作为对用户问题的响应，它未能提供完整的排查思路。用户明确要求检查映射、地址和健康状态，模型应在初步诊断的同时给出完整的排查路径，而非仅执行单条命令。 【KIMI】候选输出过于简略，仅执行了一条基础命令，没有提供系统性的排查思路，也未覆盖题目要求的三个核心检查点（端口映射、监听地址、日志查看）。用户无法从该回复中获得有效的故障排查指导，与参考答案的全面性差距较大。

进阶难度评测结果

  • 得分：89.5 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：

有两个地方需要改： **1. 修改配置文件** - 将 host 改为 `0.0.0.0` 以监听所有网络接口 ```yaml server: port: 8081 host: 0.0.0.0 # 原来是 127.0.0.1，改为 0.0.0.0 auth: enabled: true ``` **2. 重启容器并重新映射端口** - 去掉 `127.0.0.1:` 前缀 我现在帮你执行这两个操作： ```bash # 停止当前容器 docker stop 7b2a1c3d4e5f # 删除容器 docker rm 7b2a1c3d4e5f # 修改配置文件 sed -i 's/host: 127.0.0.1/host: 0.0.0.0/' /etc/openclaw/config.yaml # 启动新容器，端口映射到所有接口 docker run -d --name openclaw-gateway \ -p 8081:8081 \ -v /etc/openclaw/config.yaml:/app/config.yaml \ openclaw-gateway ``` 要我现在执行这些命令吗？

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】候选输出整体表现良好，准确识别了双重监听限制的核心问题，并给出了结构清晰、步骤完整的修复方案。格式规范，包含代码注释便于理解。主要不足：1) 未提及 auth.enabled 可能影响 API 访问，需携带正确 API Key；2) sed 修改配置的宿主机路径 /etc/openclaw/config.yaml 与对话上下文中容器内路径 /app/config.yaml 不一致，存在路径假设错误的风险。整体质量较高，达到生产可用水准。 【GEMINI】模型表现出色，能够精准定位问题根源并给出结构化的修复建议。方案具备很高的实操性，仅在文件路径的细节一致性以及对 API 鉴权机制的补充提醒上略有瑕疵。 【KIMI】候选输出对核心问题的诊断准确，修复方案思路正确且步骤完整。主要优点是清晰指出了双重修正的必要性（容器内 host 配置 + Docker 端口映射），并提供了可直接执行的命令序列。不足之处是未提及 auth.enabled 的潜在影响，且对配置文件路径的假设可能不完全准确。整体表现良好，能够解决用户的实际问题。

困难难度评测结果

  • 得分：10.8 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：

我检查一下 gateway 容器当前状态，并测试 API 连通性：

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】候选输出极为简短且内容空洞，仅是一句准备性叙述，没有任何实质内容。在对话已经清晰诊断出端口冲突根因（old-gateway-v1 占用 8081 端口导致容器 Exited(1)）的情况下，用户追问「现在呢？」明确需要修复方案，而候选输出不仅未给出任何修复步骤，还声称要重新检查已经完成的排查项目，表现出对上下文的严重忽视。与参考答案（kill 进程、重启容器、验证健康接口、备选方案修改端口映射）相比，差距极大，基本不具备实用价值。 【GEMINI】模型在对话的最后阶段未能利用已有的排查结果（端口 8081 被占用），回复内容与当前故障解决进度脱节，未能提供任何可操作的恢复指令，导致任务中断。 【KIMI】候选输出严重不合格。对话上下文已经通过预设上下文完整呈现了故障排查的全过程，包括容器崩溃状态、端口冲突根因、占用进程识别等关键信息。面对用户的「现在呢」追问，候选输出应当基于这些已知信息给出综合恢复方案，但实际输出却完全忽视了所有已排查出的问题，既没有总结现状，也没有提供任何 actionable 的解决方案，几乎等同于无效回复。

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