GLM-5.1 Is Now on MegaNova: Setup Guide for Agentic Workloads

Z.ai's GLM-5.1 is now available on MegaNova's serverless API. It is an open-source Mixture-of-Experts model built specifically for agentic engineering — long-horizon tasks, autonomous coding, and multi-step reasoning that gets sharper the longer it runs.

This guide covers what GLM-5.1 is, when to use it, and how to call it via the MegaNova API.

What Is GLM-5.1?

GLM-5.1 (zai-org/GLM-5.1) is Z.ai's flagship MoE model, designed from the ground up for agentic workflows. The defining characteristic is its long-horizon performance: unlike standard models that degrade over extended tasks, GLM-5.1's outputs improve the longer it runs — making it particularly suited for complex, multi-step tasks that require sustained coherent reasoning across many turns or tool calls.

Key specs:

• Context window: 202,752 tokens
• Architecture: Mixture-of-Experts (MoE), FP8 quantization
• Thinking mode: Available — toggleable for deeper reasoning on complex tasks
• Price: $1.40 / 1M input tokens · $4.40 / 1M output tokens
• Rate limit: 10,000 requests/day (current tier) · 50,000 requests/day (next tier)
• Model ID: zai-org/GLM-5.1

GLM-5.1 vs GLM-5: Which to Use

GLM-5 (zai-org/GLM-5) is Z.ai's 744B-parameter (40B active) flagship at $0.80/$2.56 per million tokens. GLM-5.1 is priced higher at $1.40/$4.40, and the premium is justified by a specific focus: agentic task performance and long-horizon stability.

Choose GLM-5 for cost-sensitive agentic workloads and general-purpose tasks. Choose GLM-5.1 when task complexity is high, the pipeline spans many steps, or output quality over long sequences is a priority.

Setting Up GLM-5.1 on MegaNova

MegaNova's API is OpenAI-compatible. Any SDK or library that works with the OpenAI API works with MegaNova — just change the base URL and model ID.

Step 1 — Get your API key

Go to console.meganova.ai/apiKeys and create a key. Copy it.

Step 2 — Install the OpenAI SDK (if not already installed)

pip install openai

Step 3 — Make your first call

Python:

from openai import OpenAI
import os

client = OpenAI(
    base_url="https://api.meganova.ai/v1",
    api_key=os.environ.get("MEGANOVA_API_KEY")
)

response = client.chat.completions.create(
    model="zai-org/GLM-5.1",
    messages=[
        {"role": "user", "content": "Write a Python script that monitors a folder for new files and processes each one through an analysis pipeline."}
    ],
    max_tokens=None,
    temperature=1,
    top_p=0.9,
    stream=False
)

print(response.choices[0].message.content)

cURL:

curl -X POST "https://api.meganova.ai/v1/chat/completions" \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer $MEGANOVA_API_KEY" \
    --data-raw '{
        "messages": [
            {"role": "user", "content": "Write a Python script that monitors a folder for new files and processes each one through an analysis pipeline."}
        ],
        "model": "zai-org/GLM-5.1",
        "max_tokens": null,
        "temperature": 1,
        "top_p": 0.9,
        "stream": false
    }'

JavaScript / Node.js:

import OpenAI from 'openai';

const client = new OpenAI({
    baseURL: 'https://api.meganova.ai/v1',
    apiKey: process.env.MEGANOVA_API_KEY
});

const response = await client.chat.completions.create({
    model: "zai-org/GLM-5.1",
    messages: [
        {"role": "user", "content": "Write a Python script that monitors a folder for new files and processes each one through an analysis pipeline."}
    ],
    max_tokens: null,
    temperature: 1,
    top_p: 0.9,
    stream: false
});

console.log(response.choices[0].message.content);

What GLM-5.1 Excels At

Agentic coding pipelines. GLM-5.1's design centers on code generation for real-world engineering tasks — not isolated function completion, but multi-file, multi-dependency codebases where the model needs to hold context and intent across many steps.

Long-horizon task execution. The model is specifically built to maintain coherence across extended reasoning chains. For orchestration agents, research pipelines, or complex problem decomposition tasks, the quality gap between GLM-5.1 and standard instruction models widens as task length increases.

Tool use and function calling. The model handles tool-calling natively and performs well on multi-turn tool interactions — where the agent calls a tool, gets a result, decides what to call next, and continues iterating until the task is complete.

Multilingual tasks. The GLM family from Z.ai has strong Chinese-English bilingual capabilities, making GLM-5.1 a practical choice for applications that need high-quality output in both languages.

Recommended Parameters

The 202,752-token context window means you can pass large codebases, long document chains, or extensive conversation history without truncation. Use this — GLM-5.1 is built to use the full context productively.

Rate Limits and Tiers

Current account tier: 10,000 requests per day (RPD). The next tier unlocks at 50,000 RPD.

To increase your rate limit, go to console.meganova.ai/limits and review tier requirements.

For high-volume agentic pipelines, plan your request budget against the 10K daily limit at the base tier. A pipeline making 100 tool calls per run has a ceiling of 100 full pipeline executions per day at this tier.

Try It in the Console

Before integrating via API, test GLM-5.1 in the MegaNova console playground. The chat interface supports both standard and Thinking mode — run your target prompts there first to calibrate the model's behavior before writing integration code.

Start using GLM-5.1 on MegaNova →

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