GLM-5.1: Long-horizon Agent and Claude Code Configuration

One-sentence takeaway

GLM-5.1 should be configured as a “long-horizon engineering Agent”: provide ample context and output budget, clarify the role, tech stack, and acceptance criteria first, then let it loop through execution, compilation, testing, and iteration; in Claude Code, you can switch the model name directly to GLM-5.1.

Use cases

• Suitable tasks: Long-running coding, frontend and backend project generation, repository-level refactoring, terminal tasks, performance optimization, and OpenClaw/Claude Code Agents.

• Unsuitable tasks: High-concurrency workloads that only need low-latency short answers, or unattended production changes without testing/review mechanisms.

• Applicable model version: glm-5.1.

• Applicable clients, Agents, or APIs: Z.AI API, BigModel.cn, Claude Code, OpenCode, Kilo Code, Roo Code, Cline, Droid, OpenClaw.

• Recommended reasoning level and parameters: Official benchmarks commonly use temperature=1.0 and top_p=0.95; use the provider documentation for the actual API parameters. Long tasks must be given a sufficient max_tokens value and roughly 200K of context.

Ready-to-use content

Official OpenAI-compatible Python configuration

The client and system role below come from Z.AI documentation; the user task is a replaceable template based on the official example:

from openai import OpenAI

client = OpenAI(
    api_key="your-Z.AI-api-key",
    base_url="https://api.z.ai/api/paas/v4/",
)

completion = client.chat.completions.create(
    model="glm-5.1",
    messages=[
        {
            "role": "system",
            "content": (
                "You are a senior full-stack software engineer, proficient in "
                "frontend development, backend architecture design, and modern "
                "web technology stacks."
            ),
        },
        {
            "role": "user",
            "content": (
                "Design and build <project>. Include <pages/features>. "
                "Use <stack>. First inspect the environment, then implement, "
                "run tests, and report the files changed and remaining risks."
            ),
        },
    ],
)

Reusable long-horizon task instruction

You are responsible for delivering the following engineering objective to a verifiable state: <目标>.

Environment and inputs: <仓库/操作系统/语言/启动命令>
Hard constraints: <不能改动的接口、依赖、权限和资源>
Acceptance criteria: <测试、性能、视觉、兼容性或交付物标准>

Execute in a loop: inspect the current state → define the next step → modify → compile/test/run → read the results →
adjust the strategy based on evidence. Keep TODOs and blockers up to date; do not announce completion when only the skeleton is finished.
At the end of each round, record the actual changes, verification results, reasons for failure, and the plan for the next round. If there are no objective metrics,
use a clear checklist to review completeness, interactions, and edge cases, and state the assumptions that remain unverified at the end.

In Claude Code, update the model name in the configuration to "GLM-5.1" as instructed officially; the Coding Plan consumes 3× the quota during peak hours and 2× during off-peak hours, while a limited-time promotion at the time set off-peak usage to 1×. Review the current billing rules before launch.

Test/workflow steps

1. Send a short request through the official SDK/API to confirm the model name, base URL, and API key.

2. Give the Agent a small, reversible repository task, requiring it to inspect the environment, write a TODO, then make changes and run tests.

3. Record max_tokens, the context limit, tool timeouts, and workspace resources in the experiment log; save patches/commits by phase for long tasks.

4. For performance optimization tasks, retain baseline metrics and require compilation/test/benchmark results in each round to avoid relying only on the model’s self-assessment.

5. In Claude Code/OpenCode, compare the target model using the same prompt, and record quota usage, latency, tool errors, and human intervention.

Original evidence and data

• Z.AI documentation positions GLM-5.1 as a long-horizon model that can work autonomously on a single task for up to 8 hours, covering the full loop of planning, execution, iterative optimization, and delivery.

• The official API example uses the model glm-5.1 and https://api.z.ai/api/paas/v4/, and provides a senior full-stack engineer system role and a complete task direction for a personal blog website.

• The official release page says it can be used in coding Agents such as Claude Code by updating the model name to "GLM-5.1"; it also says that local inference frameworks such as vLLM and SGLang are supported.

• Official long-horizon task demonstrations: 600+ rounds of outer-loop optimization and 6,000+ tool calls for VectorDBBench, with approximately 3,547→21,500 QPS; a Linux desktop web app ran for 8 hours in a single run, with the model continuously self-checking and filling in features.

Applicability boundaries

• The “8 hours” and long-horizon results in the official blog examples depend on specific harnesses, tools, and tasks; ordinary chat requests will not all run continuously by default.

• The API example does not include a complete tool schema, permission policy, or test scripts; the client/Agent must implement its own safety boundaries.

• A larger output budget does not equal higher accuracy; set stop conditions, test gates, and a maximum cost.

• The quota multipliers and promotions for the Z.AI Coding Plan are time-sensitive information and must not be treated as permanent pricing.

Source excerpt or observation (for compliant short quote only)

The official description says the model can complete “the full loop from planning and execution to iterative optimization”; the configuration template preserves this closed loop while leaving final acceptance responsibility to the user’s tests and review.