Laguna XS.2Poolside
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LeanstralMistral AI
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Related Products
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About
Laguna XS.2 is Poolside’s open-weight agentic coding model, built as the lightest and fastest model in the Laguna family. It is a 33B total-parameter Mixture of Experts model with 3B activated parameters, trained completely in-house on 30T tokens. As Poolside’s newest generation model open to the community, Laguna XS.2 is a second-generation architecture and the company’s first open-weight model, built on the lessons learned from training Laguna M.1 across synthetic data and reinforcement learning. The model is designed for agentic coding workflows, where it can code, act, iterate quickly, and perform best inside Poolside’s coding agent. Laguna XS.2 is positioned as a strong model for rapid agentic iteration, especially for developers and teams that need a compact, efficient coding model rather than a heavier frontier system. It is released under an Apache 2.0 license, allowing the community to evaluate, fine-tune, quantize, serve, and build on the weights.
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About
Leanstral is an open-source code agent developed by Mistral AI specifically designed to work with the Lean 4 proof assistant. The model focuses on generating code while also formally verifying its correctness against strict mathematical or software specifications. Unlike traditional coding assistants, Leanstral integrates directly with formal proof systems to ensure that generated code satisfies defined logical requirements. Its architecture is optimized for proof engineering tasks and operates efficiently with sparse model parameters. Leanstral is released under the Apache 2.0 license, making it freely accessible for developers, researchers, and organizations to use and customize. The model is designed to operate within real-world formal repositories rather than isolated problem environments. By combining code generation with formal verification, Leanstral aims to reduce the need for manual human review in complex software and mathematical development.
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Platforms Supported
Windows
Supported
Mac
Supported
Linux
Supported
Cloud
Not Supported
On-Premises
Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Platforms Supported
Windows
Supported
Mac
Supported
Linux
Supported
Cloud
Not Supported
On-Premises
Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
AI developers and engineering teams that need efficient AI models for agentic coding, long-horizon software development, and rapid coding iterations
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Audience
AI researchers, software engineers, and developers working with formal verification, proof assistants, and mathematically rigorous software development
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Not Supported
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API
Offers API
Supported
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API
Offers API
Not Supported
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Screenshots and Videos |
Screenshots and Videos |
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Pricing
Free
Free Version
Supported
Free Trial
Not Supported
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Pricing
Free
Open source
Free Version
Supported
Free Trial
Not Supported
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
Supported
Live Online
Not Supported
In Person
Not Supported
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
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Company InformationPoolside
Founded: 2023
United States
www.poolside.ai/models
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Company InformationMistral AI
Founded: 2023
France
mistral.ai
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Alternatives |
Alternatives |
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Categories |
Categories |
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Integrations
Agent Client Protocol (ACP)
Supported
Cline
Supported
Hermes Agent
Supported
Hugging Face
Supported
IntelliJ IDEA
Supported
Kilo Code
Supported
Mistral AI
Not Supported
Mistral AI Studio
Not Supported
Mistral Vibe
Not Supported
Nous Portal
Supported
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Integrations
Agent Client Protocol (ACP)
Not Supported
Cline
Not Supported
Hermes Agent
Not Supported
Hugging Face
Not Supported
IntelliJ IDEA
Not Supported
Kilo Code
Not Supported
Mistral AI
Supported
Mistral AI Studio
Supported
Mistral Vibe
Supported
Nous Portal
Not Supported
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