GLM-5.3

GLM-5.3

Z.ai
Hydrogen-1.0

Hydrogen-1.0

Deep Principle
+
+

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About

GLM-5.3 is Z.ai’s frontier coding model designed for complex software engineering, long-horizon agent tasks, and advanced post-training research. The model uses the same base model as GLM-5.2, with improvements coming from scaled post-training across more environments, more diverse tasks, and larger compute investment. GLM-5.3 delivers stronger coding performance, better task ownership, improved benchmark results, and greater efficiency across realistic development workflows. It is built to handle complex coding tasks, production-style engineering work, research environments, automation tasks, and agentic workflows that require multi-step execution. The model also shows emergent cyber capabilities in vulnerability discovery and exploitation-chain reasoning, with safety evaluation and hardening planned before open-weight release.

About

Hydrogen-1.0 is an open-weight scientific language model developed by Deep Principle through post-training GLM-5.3 on chemistry, materials science and life-sciences data, laboratory records and research trajectories. It is intended to help researchers address scientific questions, explain derivations, explore reaction pathways, analyze spectra, plan experiments and develop materials hypotheses. Demonstrations include scientific computing workflows, reaction-network exploration, NMR structure analysis, experimental planning and materials discovery. The model is available as downloadable weights through Hugging Face and ModelScope, through API access on NeuroSpark, and in Deep Principle's Mira AI Scientist. It can also be run locally using compatible hardware and software. Hydrogen-1.0 supports research workflows; users should review its outputs against relevant evidence and scientific constraints.

Platforms Supported

Windows Supported
Mac Supported
Linux Supported
Cloud Supported
On-Premises Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Audience

Software engineers, coding agent builders, AI researchers, ML infrastructure teams, security researchers, developer tool teams, automation teams, technical leaders, and organizations that need frontier coding models, long-horizon reasoning, production-style software engineering, benchmark-driven model evaluation, reinforcement learning research, coding-agent integrations, reasoning effort controls, ZCode workflows, and advanced technical task automation

Audience

Researchers and scientific R&D teams working in chemistry, materials science, and life sciences who need assistance with scientific questions, research workflows, experiment planning, or materials exploration.

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Not Supported

API

Offers API Supported

API

Offers API Supported

Screenshots and Videos

Screenshots and Videos

No images available

Pricing

Free
Open source
Free Version Supported
Free Trial Not Supported

Pricing

No information available.
Free Version Supported
Free Trial Not Supported

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 5.0 / 5

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

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Pros & Cons from Real Users

Pros

  • The thing I like most is that GLM-5.3 feels aimed at serious engineering work, not casual prompting. It is built around coding agents, long-running software tasks, debugging, and the kind of multi-step execution that actually matters when you are working inside real repos. The post-training jump is also interesting. Z.ai is not just talking about a bigger model; it is pushing the idea that better training on agentic coding and cyber workflows can make the model more useful in practice.

Cons

  • The cybersecurity angle is impressive, but it is also where I would be most cautious. Strong vulnerability discovery and cyber reasoning can be useful for defense, audits, and secure engineering, but I would want very clear controls around how it is used.

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

Z.ai
Founded: 2023
China
z.ai/

Company Information

Deep Principle
Founded: 2024
China
blog.deepprinciple.com/introducing-hydrogen-1.0/

Alternatives

Alternatives

No Alternatives
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Categories

Categories

AI Models Supported
AI Science Supported

Integrations

APIFree Supported
Augment Code Supported
C# Supported
Cheaper Inference Supported
Claw Code Supported
Cosmos Supported
GLM Coding Plan Supported
Go Supported
Hugging Face Not Supported
JavaScript Supported
JetBrains Junie Supported
ModelScope Not Supported
OpenCode Go Supported
Python Supported
R Supported
SQL Supported
Swift Supported
Wafer Supported
Yonoo Supported
pandas Supported

Integrations

APIFree Not Supported
Augment Code Not Supported
C# Not Supported
Cheaper Inference Not Supported
Claw Code Not Supported
Cosmos Not Supported
GLM Coding Plan Not Supported
Go Not Supported
Hugging Face Supported
JavaScript Not Supported
JetBrains Junie Not Supported
ModelScope Supported
OpenCode Go Not Supported
Python Not Supported
R Not Supported
SQL Not Supported
Swift Not Supported
Wafer Not Supported
Yonoo Not Supported
pandas Not Supported
Claim GLM-5.3 and update features and information
Claim GLM-5.3 and update features and information
Claim Hydrogen-1.0 and update features and information
Claim Hydrogen-1.0 and update features and information