GLM-5.3

GLM-5.3

Z.ai
SWE-2

SWE-2

Cognition
+
+

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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

SWE-2 is Cognition’s advanced coding model designed to improve software engineering performance while reducing the cost of agentic coding workflows. The model is post-trained from Kimi K3 and uses reinforcement learning to optimize multiple reasoning-effort levels within a single training run. SWE-2 is designed to explore codebases more selectively, begin implementation sooner, and complete tasks with fewer redundant reads and reasoning steps than earlier Cognition models. Its capabilities include code generation, debugging, test creation, verification, repository analysis, and complex terminal-based software engineering tasks. The model also emphasizes stronger engineering judgment, end-to-end test coverage, instruction following, and evidence-based verification of user assumptions. SWE-2 is available through Devin Desktop and Devin CLI, with broader rollout planned across Devin Web and Fusion.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

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

Software developers, engineering teams, AI coding agent users, DevOps professionals, and organizations that need capable agentic software engineering with lower execution cost and more efficient reasoning

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Open source
Free Version
Free Trial

Pricing

$20/month
Free Version
Free Trial

Reviews/Ratings

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

Reviews/Ratings

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

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.

Pros & Cons from Real Users

Pros

  • The biggest thing that stands out is the cost-performance balance. SWE-2 is not just trying to top one benchmark; it is trying to get very close to frontier coding performance at a much lower cost. For developers, that matters a lot. Coding agents can burn through tokens quickly when they are reading files, making edits, running tests, and iterating. A model that performs near the top while being meaningfully cheaper is much easier to use every day. I also like that SWE-2 seems built for real software engineering workflows, not just isolated code snippets. The strong DeepSWE and Terminal-Bench results make it especially interesting for repo-level tasks, debugging, tool use, and longer agent runs.

Cons

  • Benchmarks are useful, but real projects bring messy architecture, flaky tests, undocumented behavior, and weird edge cases.

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Z.ai
Founded: 2023
China
z.ai/

Company Information

Cognition
Founded: 2023
United States
cognition.com

Alternatives

Alternatives

GPT-5.6 Sol

GPT-5.6 Sol

OpenAI
GPT-5.6 Sol

GPT-5.6 Sol

OpenAI
GLM-5

GLM-5

Z.ai
SWE-1.7

SWE-1.7

Cognition
GLM-5.1

GLM-5.1

Z.ai
SWE-1.6

SWE-1.6

Cognition

Categories

Categories

Integrations

C
C#
C++
CSS
Go
HTML
JavaScript
Kotlin
PHP
Python
R
Rust
SQL
Swift
TypeScript
ClinePass
GLM Coding Plan
Solidity
Sup AI
Z.ai

Integrations

C
C#
C++
CSS
Go
HTML
JavaScript
Kotlin
PHP
Python
R
Rust
SQL
Swift
TypeScript
ClinePass
GLM Coding Plan
Solidity
Sup AI
Z.ai
Claim GLM-5.3 and update features and information
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