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ease
features
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support

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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

Leanstral 1.5 is a model licensed under Apache-2.0, designed for effective proof engineering in Lean 4, aimed at enhancing the capabilities and accessibility of formal verification. It boasts a total of 119 billion parameters, with 6 billion of them being active, marking a significant improvement in performance for tasks such as theorem proving, agent-based proof engineering, and the verification of practical code. The development of Leanstral 1.5 involved a comprehensive three-stage training process, which included mid-training, supervised fine-tuning, and reinforcement learning utilizing CISPO. In a multiturn environment, the model is tasked with receiving a theorem statement, submitting a proof, and refining its approach based on feedback from the Lean compiler until the proof is either successfully compiled or the available resources are depleted. In the code agent setting, Leanstral functions similarly to a developer navigating a raw filesystem, allowing it to edit files, execute bash commands, and interact with the Lean language server to monitor goals, errors, and type information in real time. This innovative approach not only streamlines the proof engineering process but also significantly enhances the user experience in formal verification tasks.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

APIFree
Cherry Studio
Claude Code
Claw Code
Cline
Dessix
GLM Coding Plan
GLM-5-Turbo
Kilo Code
Ollama
OpenClaw
OpenRouter
Oxlo.ai
Qoder
Roo Code
Shiori
Sup AI
Tabbit Browser
Yonoo
Zo Computer

Integrations

APIFree
Cherry Studio
Claude Code
Claw Code
Cline
Dessix
GLM Coding Plan
GLM-5-Turbo
Kilo Code
Ollama
OpenClaw
OpenRouter
Oxlo.ai
Qoder
Roo Code
Shiori
Sup AI
Tabbit Browser
Yonoo
Zo Computer

Pricing Details

Free
Free Trial
Free Version

Pricing Details

Free
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Zhipu AI

Founded

2023

Country

China

Website

z.ai/

Vendor Details

Company Name

Mistral AI

Founded

2023

Country

France

Website

mistral.ai/news/leanstral-1-5/

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