AI Models

GLM-5 Release Review: 745B MoE Architecture and Claude Code Compatibility

02/12/2026

5.735min

GLM-5Zhipu AIMoAI-ADKClaude CodeMoE ArchitectureLLM

Zhipu AI's first annual flagship model GLM-5 has been released. We analyze the 745B MoE architecture, 202K token context window, DeepSeek sparse attention mechanism, and explain how to integrate with moai-adk 2.2.7.

Overview

February 2026 marks the release of GLM-5, Zhipu AI's first annual flagship model. This release represents more than a simple model update—it demonstrates significant progress in LLM architecture.

This document analyzes the technical features and performance of GLM-5 and explains how to leverage GLM-5 in Claude Code through MoAI-ADK.

1. GLM-5 Technical Architecture

1.1 Core Technical Specifications

The most notable feature of GLM-5 is its 745B parameter MoE (Mixture of Experts) architecture. Unlike traditional dense models, MoE dynamically selects which expert groups to activate, maximizing inference efficiency.

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MoE Architecture - Mixture of Experts architecture operation

The key advantages of this architecture are:

Efficient Inference: Only a subset of parameters are activated per token
Specialized Learning: Expert groups specialize in specific domains
Scalability: Linear performance improvements with model size

1.2 Context Window Expansion

GLM-5 supports a 202K token context window. This represents approximately 150,000 words, enabling the following tasks in a single session:

Analysis of entire large codebases
Consistent processing of hundreds of pages of technical documentation
Complex multi-file refactoring

1.3 Sparse Attention Mechanism

The sparse attention mechanism, inspired by DeepSeek, optimizes token relationship computation for improved efficiency in long context processing.

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Sparse Attention Flow - Sparse attention mechanism processing flow

2. Performance Benchmark Analysis

2.1 Coding Performance

GLM-5 demonstrates open-source SOTA (State-of-the-Art) coding performance. It particularly achieves performance close to Claude Opus 4.5 in real-world programming tasks.

GLM-5 Coding Benchmark - Coding performance benchmark

Benchmark results show the following characteristics:

Agent Task Optimization: High accuracy in complex multi-step code generation
Real-world Scenario Enhancement: Performance optimized for actual development projects
Language Balance: Consistent performance across various programming languages

2.2 Agent Performance

GLM-5 shows particular strength in agent-based tasks. Tool usage, planning capabilities, and context retention are comprehensively evaluated.

GLM-5 Agent Benchmark - Agent performance benchmark

3. Claude Code Compatibility

3.1 One-Click Integration

GLM-5 features full Claude Code compatibility. Through Z.AI's DevPack, you can use GLM-5 in Claude Code without code modifications.

The core of this compatibility is the OpenAI API compatibility layer. GLM-5 supports the standard OpenAI API endpoint format, simplifying integration with Claude Code.

3.2 SDK and API Support

GLM-5 supports various developer tools.

Official Python SDK (zai-sdk)

Python
from zai import ZAI

client = ZAI(api_key="your-api-key")

response = client.chat.completions.create(
    model="glm-5",
    messages=[
        {"role": "user", "content": "Explain MoE architecture"}
    ],
    thinking=True  # Enable reasoning mode
)

OpenAI Python SDK Compatible

Python
from openai import OpenAI

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

response = client.chat.completions.create(
    model="glm-5",
    messages=[...]
)

Java SDK

Java
ZAIClient client = new ZAIClient("your-api-key");
ChatRequest request = ChatRequest.builder()
    .model("glm-5")
    .messages(messages)
    .thinking(true)
    .build();

ChatResponse response = client.chat(request);

3.3 Thinking Parameter

GLM-5 supports the thinking parameter. When enabled, this parameter exposes the model's reasoning process, useful for debugging and verification tasks.

Parameter	Type	Description
`thinking`	boolean	Whether to expose reasoning process
`max_tokens`	integer	Maximum output tokens
`temperature`	float	Output randomness control

4. MoAI-ADK Integration

4.1 moai glm Command

Starting from MoAI-ADK 2.2.7, GLM-5 is supported. Use the moai glm command to switch Claude Code's LLM backend to GLM-5.

Bash
# Initial setup (save API key)
moai glm sk-xxx-your-api-key

# Switch with saved key
moai glm

# Switch back to Claude
moai cc

This command automatically performs the following tasks:

Saves API key securely to ~/.moai/.env.glm (0o600 permissions)
Injects environment variables into .claude/settings.local.json
Automatically adds API key file to .gitignore

4.2 MoAI-ADK Installation

MoAI-ADK is distributed as a single Go binary. Installation is straightforward.

Bash
# macOS / Linux / WSL
curl -fsSL https://raw.githubusercontent.com/modu-ai/moai-adk/main/install.sh | bash

# Windows PowerShell
irm https://github.com/modu-ai/moai-adk/raw/main/install.ps1 | iex

After installation, initialize your project.

Bash

moai init my-project
cd my-project

4.3 Activating GLM-5

The procedure to activate GLM-5 in your project is as follows.

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GLM-5 Setup Process - GLM-5 configuration process

Command execution example:

Bash
# Switch with API key
moai glm sk-xxx-your-zai-api-key

# Output
# GLM API key saved to ~/.moai/.env.glm
# Switched to GLM backend.
# Environment variables injected into .claude/settings.local.json
# Run 'moai cc' to switch back to Claude.

The generated settings file structure is as follows.

JSON
{
  "env": {
    "ANTHROPIC_AUTH_TOKEN": "${GLM_API_KEY}",
    "ANTHROPIC_BASE_URL": "https://api.z.ai/api/anthropic",
    "API_TIMEOUT_MS": "3000000"
  }
}

5. Pricing Policy

5.1 GLM Coding Plan

Z.AI offers the GLM Coding Plan in three tiers. This represents significant cost savings compared to Claude Code Max at $80/month.

Service	Monthly	Annual	Key Features
GLM Coding Lite	$10	$120	Entry-level, ~80 prompts per 5-hour cycle
GLM Coding Pro	$30	$360	Active developers, ~400 prompts per 5-hour cycle
GLM Coding Max	$80	$960	Heavy users, ~1,600 prompts per 5-hour cycle, GLM-5 support
Claude Code Max	$80	$960	Anthropic official, 1M token context

5.2 Cost Efficiency Analysis

The cost savings from using GLM Coding Plan are as follows:

87.5% Cost Reduction: Lite plan saves ~$840 annually vs. Claude Code Max
62.5% Cost Reduction: Pro plan saves ~$600 annually vs. Claude Code Max
Same Price, More Usage: Max plan offers ~1,600 prompts per 5-hour cycle at the same $80
Equal Performance: Near Opus 4.5 performance in real programming tasks

Conclusion

GLM-5, as Zhipu AI's first annual flagship model, demonstrates significant technical progress. The 745B MoE architecture and 202K token context window provide powerful tools for large-scale development work.

Key Takeaways:

Architecture Innovation: 745B MoE achieves balance between inference efficiency and performance
Claude Code Compatibility: One-click integration via MoAI-ADK eliminates complex configuration
Cost Efficiency: Starting from Lite $10, up to 87.5% savings vs. Claude Code Max
Real-world Performance: SOTA-level performance in coding and agent tasks

Recommended Next Steps:

Get API key from Z.AI subscription page
Install MoAI-ADK: curl -fsSL https://raw.githubusercontent.com/modu-ai/moai-adk/main/install.sh | bash
Switch to GLM-5: moai glm sk-xxx-your-api-key
Restart Claude Code and start developing

The combination of GLM-5 and MoAI-ADK provides a cost-effective, high-performance AI development environment.

Overview

1. GLM-5 Technical Architecture

1.1 Core Technical Specifications

1.2 Context Window Expansion

1.3 Sparse Attention Mechanism

2. Performance Benchmark Analysis

2.1 Coding Performance

2.2 Agent Performance

3. Claude Code Compatibility

3.1 One-Click Integration

3.2 SDK and API Support

3.3 Thinking Parameter

4. MoAI-ADK Integration

4.1 moai glm Command

4.2 MoAI-ADK Installation

4.3 Activating GLM-5

5. Pricing Policy

5.1 GLM Coding Plan

5.2 Cost Efficiency Analysis

Conclusion

References