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Description

GLM-5 is a next-generation open-source foundation model from Z.ai designed to push the boundaries of agentic engineering and complex task execution. Compared to earlier versions, it significantly expands parameter count and training data, while introducing DeepSeek Sparse Attention to optimize inference efficiency. The model leverages a novel asynchronous reinforcement learning framework called slime, which enhances training throughput and enables more effective post-training alignment. GLM-5 delivers leading performance among open-source models in reasoning, coding, and general agent benchmarks, with strong results on SWE-bench, BrowseComp, and Vending Bench 2. Its ability to manage long-horizon simulations highlights advanced planning, resource allocation, and operational decision-making skills. Beyond benchmark performance, GLM-5 supports real-world productivity by generating fully formatted documents such as .docx, .pdf, and .xlsx files. It integrates with coding agents like Claude Code and OpenClaw, enabling cross-application automation and collaborative agent workflows. Developers can access GLM-5 via Z.ai’s API, deploy it locally with frameworks like vLLM or SGLang, or use it through an interactive GUI environment. The model is released under the MIT License, encouraging broad experimentation and adoption. Overall, GLM-5 represents a major step toward practical, work-oriented AI systems that move beyond chat into full task execution.

Description

SWE-1.7 is Cognition’s most capable software engineering model, built to push frontier coding performance while reducing the cost of high-quality agentic rollouts. The model is designed for real-world software development tasks that require extended reasoning, codebase understanding, terminal use, debugging, feature work, migrations, and careful validation. It was trained from a Kimi K2.7 base and improved through Cognition’s reinforcement learning pipeline, including more stable training, stronger infrastructure, better data curation, and long-horizon task techniques. SWE-1.7 is especially optimized for asynchronous software engineering, where an agent needs to work through large projects over longer sessions instead of simply answering short prompts. Its self-compaction capabilities allow the model to summarize its working state and resume from that summary, helping it operate beyond the raw context window on multi-hour tasks. The model is also trained to balance task success with efficiency, using concise reasoning when possible while preserving deeper exploration for harder problems. SWE-1.7 tends to investigate codebases more thoroughly than its base model, reading files, running searches, probing edge cases, and experimenting before making changes. It is available in Devin through web, desktop, and CLI interfaces, with Cerebras serving support at 1000 TPS. SWE-1.7 gives developers and engineering teams a high-performance coding model for complex software projects at a more practical cost.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

C No 
CSS No 
Cheaper Inference Yes 
Dart No 
Dessix Yes 
Devin No 
JSON No 
JavaScript No 
MATLAB No 
Python No 
R No 
Roo Code Yes 
Ruby No 
SQL No 
Scala No 
Shiori Yes 
Tabbit Browser Yes 
Terraform No 
XML No 
YAML No 

Integrations

C Yes 
CSS Yes 
Cheaper Inference No 
Dart Yes 
Dessix No 
Devin Yes 
JSON Yes 
JavaScript Yes 
MATLAB Yes 
Python Yes 
R Yes 
Roo Code No 
Ruby Yes 
SQL Yes 
Scala Yes 
Shiori No 
Tabbit Browser No 
Terraform Yes 
XML Yes 
YAML Yes 

Pricing Details

Free
Open source
Free Trial No 
Free Version Yes 

Pricing Details

$20/month
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Z.ai

Founded

2023

Country

China

Website

z.ai/

Vendor Details

Company Name

Cognition

Founded

2023

Country

United States

Website

cognition.com

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