GLM-5.1 Quickstart - Open-Source Agentic Coding Model

1. Model Overview

GLM-5.1 is Z.ai's latest open-source flagship model, released in March 2026. It represents a major upgrade to the GLM series, reaching new heights in long-horizon agentic capabilities, coding performance, and persistent execution of complex engineering tasks. It is currently one of the strongest Agentic coding models in the open-source domain.

1.1 Core Specifications

Architecture: Trillion-scale Mixture-of-Experts (MoE)
Total parameters: 754B (approx. 744B–754B)
Active parameters per inference: ~40B (40 billion)
Pre-training: 28.5 trillion tokens + extensive Agentic and long-horizon data
Native context length: 200K tokens
Supported inputs: Text (native MCP tool integration)

1.2 Key Capabilities

Key capabilities that put it ahead of most open-source models:

Ultra-strong long-horizon agent capabilities: Capable of continuous autonomous operation for hours to 8+ hours, maintaining high efficiency through hundreds of iterative rounds and thousands of tool calls
Industry-leading coding: Achieves SOTA on SWE-Bench Pro (58.4), surpassing most proprietary models
Persistent optimization: Reflects, experiments, and adjusts strategies repeatedly when facing ambiguous problems, improving over time
Transparent reasoning and tool use: Supports complex multi-step planning, terminal operations, repository generation, and other real-world engineering tasks
Open-source friendly: MIT License, fully open weights

2. How to Use

Fully open-source weights on Hugging Face; the official API is compatible with the OpenAI format.

• Python Example:

from openai import OpenAI

BASE_URL = "https://inference.canopywave.io/v1"
API_KEY = os.environ.get("CANOPYWAVE_API_KEY")

client = OpenAI(api_key=API_KEY, base_url=BASE_URL)

response = client.chat.completions.create(
    model="zai/glm-5.1",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Tell me a story"}
    ]
)

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

3. Use Cases

Scenario	Typical Input Size	Why GLM-5.1 Wins
Large-scale Code Repository Generation & Refactoring	Complete requirements + multi-file codebase	Strong NL2Repo capabilities; one-stop generation of runnable repositories
Long-horizon Agent Engineering Tasks	Hundreds of tool calls + iterative optimization	Sustained autonomous execution for 8+ hours; the longer it runs, the better it performs
Real Terminal Operations & DevOps	Terminal command sequences	Leading performance on Terminal-Bench 2.0
Complex Problem Persistent Solving	Ambiguous requirements + multi-round experiments	Repeated reflection + strategy adjustment; ideal for R&D and optimization tasks
Tool-intensive Agents	MCP tool integration + multi-source data	Powerful tool calling and long-term context retention

4. Prompting Best Practices

4.1 Always force thinking trace for serious tasks

You are a world-class software engineer. Use thinking tags to show every step of your reasoning. Think step by step, analyze requirements, formulate a plan, check feasibility, and then provide a complete executable solution.

4.2 For maximum reliability, use two-stage Plan → Execute

First ask the model to output a detailed execution plan (Plan), then have it execute specific steps after user confirmation.

4.3 Recommended parameters

Complex Agent / coding tasks: temperature 0.6-0.8
Precise reasoning tasks: temperature 0.5-0.7
Thinking Mode: Always recommended to enable for complete reasoning traces

4.4 Self-check prompt (add before the final answer)

Before providing the final answer, please double-check logical consistency, technical feasibility, potential edge cases, and confirm whether sufficient iterative optimization has been performed.

5. Pricing & Limitations

Item	Detail
Pricing	Input: $1.40 / million tokens Output: $4.40 / million tokens Cache: $0.26 / million tokens
Latency	Long thinking and multi-round agent tasks may take considerable time
Hard context ceiling	200K tokens
Knowledge cutoff	Early 2026
Best for	High-complexity engineering, agentic tasks, long-horizon R&D — not suited for casual chat

6. Quick Links

GLM-5.1 model card: GLM-5.1 API
Get Started Now: Canopy Wave
Model weights (open-source): zai-org/GLM-5.1 · Hugging Face

GLM-5.1 truly fuses powerful coding capabilities with persistent Agentic engineering, becoming a critical milestone for the open-source community in software engineering.

Whether you're building large-scale software systems, automating complex engineering workflows, or developing long-horizon autonomous agents, GLM-5.1 can significantly boost efficiency.

GLM-5.1