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What is GLM-5.3?

GLM-5.3 is Z.ai’s frontier coding model built to improve complex software engineering, long-horizon agent work, and advanced technical reasoning through scaled post-training. The model uses the same base model as GLM-5.2, with performance gains coming from additional post-training environments, more diverse tasks, and expanded compute on the existing training stack. Z.ai’s stack includes IndexShare for efficient long-context processing, SAO for reinforcement learning on long-horizon tasks, and slime for large-scale asynchronous post-training. GLM-5.3 is designed to perform better on work that resembles real engineering tasks rather than short coding exercises. Its training environments include production-style workflows where the model must diagnose bottlenecks, inspect documentation, use codebases, run experiments, implement changes, and produce measurable improvements. The model improves coding performance across public and private benchmarks, including Terminal Bench 3.0, DeepSWE, Agents’ Last Exam, and Z.ai Code Bench. GLM-5.3 also improves token efficiency, producing stronger agentic coding results than GLM-5.2 while using fewer output tokens in Z.ai’s internal evaluations. The model supports three reasoning effort levels, low, high, and max, and no longer supports disabling thinking. Z.ai recommends max reasoning effort for coding tasks, while applications using disabled thinking must migrate to enabled thinking before switching to GLM-5.3. The release also reports emergent cyber capabilities, including stronger vulnerability discovery and exploitation-chain reasoning, with open-weight release planned after safety evaluation and hardening. By combining scaled post-training, long-context infrastructure, long-horizon reinforcement learning, coding-agent workflows, benchmark improvements, reasoning controls, and ZCode integration, GLM-5.3 helps developers and researchers work on demanding coding and agentic tasks.

What is Altar-1?

The Aikido Altar is a state-of-the-art open-weight security framework designed to provide organizations with sophisticated defensive security intelligence specifically adapted to their infrastructure needs. This system is particularly effective in environments that demand sovereign security, safeguarding sensitive materials such as source code, architectural blueprints, vulnerability reports, and various confidential data within the organization's perimeter, preventing any sharing with outside inference services. Utilizing the GLM-5.3 architecture, Altar incorporates methods like quantization and expert pruning that reduce the model size from a substantial 1.51 TB in full precision to a more manageable 328 GB, while still preserving most of the original model's reasoning and security capabilities. The architecture maintains 168 of the originally 256 routed experts in each backbone expert layer and employs a W4A16 representation, improving its utility for security tasks that necessitate managing large and evolving context windows. Expert selection was calibrated using internal pentesting data alongside multilingual resources, ensuring that no client data was involved, thus maintaining the integrity of cybersecurity, programming, and language processing capabilities. This groundbreaking strategy not only simplifies the deployment process but also enhances the organization's defense against new and evolving threats, ensuring robust protection in an increasingly complex digital landscape. Furthermore, the ongoing development of additional features aims to further bolster the system’s resilience and adaptability to future security challenges.

Media

Media

Integrations Supported

Amp
Augment Code
C++
CSS
Cheaper Inference
Claw Code
Go
Java
JavaScript
Kotlin
LM Studio Bionic
Ollama
OpenRouter
Python
SQL
Sup AI
Swift
Vercel AI Gateway
ZCode
pandas

Integrations Supported

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Open source
Free Version

Pricing Information

$350 per month
Free Version

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

24 Hour Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Online Training

Company Facts

Organization Name

Z.ai

Date Founded

2023

Company Location

China

Company Website

z.ai/

Company Facts

Organization Name

Aikido Security

Date Founded

2022

Company Location

Belgium

Company Website

www.aikido.dev/blog/aikido-altar-open-weight-ai-sovereign-security

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

AI Reasoning Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

Categories and Features

AI Cybersecurity

Not specified

AI Models

Not specified

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