Qwen3.7-Max: Long-Horizon Agents, Frontend Prototypes, and Office Prompts

One-sentence takeaway

The official release page provides copyable prompts for frontend interactions and office documents, and recommends retaining prior thinking in long-horizon Agents so that tool feedback drives iteration instead of requesting only one final answer.

Use cases

• Suitable tasks: Multi-turn code/tool Agents, frontend prototypes, document-format repair, GPU/kernel optimization, and workflows that require continuous verification.

• Unsuitable tasks: Open-ended creation without an acceptance script, production operations that cannot be rolled back, and treating a release demo as the success rate of a real business.

• Applicable model versions: Qwen3.7-Max; the official page says it is available through the Alibaba Cloud Bailian API.

• Applicable clients, Agents, or APIs: Alibaba Cloud Bailian-compatible OpenAI Chat Completions/Responses and Anthropic APIs; it can connect to Claude Code, OpenClaw, Qwen Code, and others.

• Recommended reasoning level and parameters: Enable thinking for Agent tasks and retain prior thinking in multi-turn messages; for complex reasoning, use the xhigh explanation template provided on the official page and calibrate it against test results.

Ready-to-use content

Frontend interactive prototype prompt

Create a real-time interactive 3D particle-system webpage with Three.js. Requirements:
1. Use the camera to detect whether the palm is open or closed and control the contraction and expansion of the particle cloud: when the palm is open, the particles disperse; when the palm is clenched, the particles contract into a sphere;
2. When the gesture is 1, have the particles form the text (hello, world); when the gesture is 2, have them form the text (I’m Qwen);
3. The particles must respond to gesture changes in real time;
4. The text should have a 3D rotation effect;
5. Implement it with HTML.

Office-document format-repair prompt

Please complete a thesis-format repair task.

Input files:
- Formatting-specification file: 研究生学位论文格式规范.docx
- Thesis with inconsistent formatting (to be repaired): 论文_格式混乱版.docx

Output file:
- Repaired thesis: 论文_格式修复版.docx

Please read the specification and the thesis to be repaired, and fix the page layout, heading styles, font and font size, margins, table of contents, and reference formatting according to the specification. After each modification, verify that the output file can be opened, and report any formatting differences that could not be verified.

Minimal Bailian-compatible API configuration

from openai import OpenAI

client = OpenAI(
    api_key=os.environ["DASHSCOPE_API_KEY"],
    base_url=os.environ.get(
        "DASHSCOPE_BASE_URL",
        "https://dashscope.aliyuncs.com/compatible-mode/v1",
    ),
)

completion = client.chat.completions.create(
    model="qwen3.7-max",
    messages=[{"role": "user", "content": "Write a Python function that merges two sorted linked lists."}],
    extra_body={"enable_thinking": True, "preserve_thinking": True},
    stream=True,
)

Test/workflow steps

1. Put the input files, specification files, target output, and completion criteria in the prompt; do not use “make it better” as an acceptance condition.

2. For code or document tasks, first have the model read the materials and make a plan, then call tools step by step; retain the thinking content from the preceding response in every round.

3. After every tool call, run compilation, rendering, format checks, or unit tests, and return the failure log to the model verbatim.

4. For frontend tasks, run the result in a real browser and check permissions, camera input, frame rate, text legibility, and mobile layout.

5. For office tasks, save the original, repaired, and specification files, and use an independent document checker to verify headers, the table of contents, fonts, and references.

6. Set a budget, a maximum number of tool calls, a stop condition for no improvement, and a human handoff point for long-horizon tasks; after completion, summarize changes, verification evidence, and unresolved items.

Original evidence and data

• The official page says Qwen3.7-Max supports preserve_thinking and recommends retaining the preceding turns' thinking content for Agent tasks.

• The page examples show a Three.js camera-gesture particle webpage and the use of office-cli to repair thesis formatting.

• In the release page's long-horizon example, the model completed 432 kernel evaluations and 1,158 tool calls in approximately 35 hours, ultimately achieving a geometric-mean speedup of 10.0x over the Triton reference implementation.

• The page also shows the model ID and base URL for integrations with Claude Code, OpenClaw, and Qwen Code; actual availability by region and version should follow the Bailian documentation.

Applicability boundaries

• The frontend and office examples are official demos, not independent blind tests; the complete materials, random seeds, tool versions, and failure samples were not disclosed.

• preserve_thinking may increase context and cost; record input/output tokens for each round, and do not retain all history unconditionally.

• Camera, document read/write, and shell tools involve privacy, permission, and file-destruction risks; use a sandbox, least privilege, and rollback.

• The 35-hour kernel experiment used specific hardware, validators, and a tool environment, so it cannot be generalized to every code repository or GPU.

Source excerpt or observation (for a compliant short quotation only)

The official page positions the model as “a new-generation flagship model for the agentic era,” but its reusable value lies in the concrete inputs, tools, and acceptance loop.