Fetch.ai Introduces ASI-1 Mini, the First Web3-Native AI Model for Agentic Workflows

Key takeaways

• Fetch.ai has launched ASI-1 Mini, a Web3-native large language model (LLM) designed for complex, autonomous workflows.
• The model delivers performance on par with leading LLMs while requiring significantly less hardware, making AI more accessible.
• ASI-1 Mini is the first step in the ASI:<Train/> initiative, enabling the Web3 community to invest in, train, and own AI models.

A step toward decentralized AI ownership

Fetch.ai has announced the launch of ASI-1 Mini, the first large language model (LLM) designed specifically for Web3 and agentic AI. Unlike traditional AI models, ASI-1 Mini integrates seamlessly with decentralized networks, allowing users to interact with AI in a secure and autonomous way.

👀 @Fetch_ai pic.twitter.com/YPIwjQXCO0

— Artificial Superintelligence Alliance (@ASI_Alliance) February 24, 2025

With performance benchmarks matching top-tier AI models, ASI-1 Mini operates at a fraction of the hardware cost, running efficiently on just two GPUs. This efficiency makes high-performance AI more accessible to developers, businesses, and individual users.

"ASI-1 Mini is the first major product from the ASI Alliance’s innovation stack, marking the beginning of the ASI:<Train/> rollout and a new era of community-owned AI. This launch sets the foundation for a decentralized ecosystem where the Web3 community can invest, train, and directly benefit from cutting-edge AI models." — Humayun Sheikh, CEO of Fetch.ai and chairman of the Artificial Superintelligence (ASI) Alliance

ASI-1 Mini’s advanced architecture

ASI-1 Mini is built on a unique AI framework that extends beyond conventional LLMs. Instead of relying on a single monolithic model, ASI-1 Mini employs a combination of specialized AI models (Mixture of Models, or MoM) and autonomous agents (Mixture of Agents, or MoA).

• Mixture of Models (MoM): Instead of a monolithic structure, ASI-1 Mini dynamically selects from multiple specialized models, each optimized for specific tasks or data types. A gating mechanism ensures that only the most relevant models are activated, improving efficiency, speed, and scalability. This is particularly beneficial for multi-modal AI, federated learning, and task-specific pipelines.
• Mixture of Agents (MoA): Autonomous agents, each with independent reasoning, knowledge, and decision-making, collaborate seamlessly to solve complex tasks. A coordination mechanism ensures efficient task distribution, making the system resilient, decentralized, and adaptive. MoA is critical for multi-agent systems, decentralized AI, and collaborative intelligence in dynamic environments. Agents act as the intelligent I/O and execution components of the architecture.

This architecture allows the system to dynamically select the best model for each task, optimizing efficiency and decision-making in real time.

The model also introduces multiple reasoning modes—Multi-Step, Complete, Optimized, and Short Reasoning—allowing it to adapt its approach based on the complexity of the task. Future updates will expand ASI-1 Mini’s capabilities, including an extended context window supporting:

• Up to 1 million tokens – Enabling AI to manage highly complex, multi-layered tasks such as analyzing long documents or detailed technical manuals.
• Up to 10 million tokens – Facilitating large-scale data analysis, including legal records, financial transactions, and comprehensive enterprise datasets.

Enabling community-driven AI development

Powered by $FET through the ASI wallet integration, ASI-1 Mini is the first model in the ASI:<Train/> family, an ASI Alliance initiative set to expand with the upcoming launch of the Cortex group of models - centered around use of large language models and generalized intelligence. This decentralized approach allows individuals to participate in the AI economy, sharing in the financial benefits as these models gain adoption.

As part of its long-term roadmap, ASI-1 Mini will integrate with Fetch.ai’s AgentVerse, an AI agent marketplace where users can create and deploy autonomous agents to complete real-world tasks, from booking reservations to managing financial transactions. These micro-agents will be available for monetization, creating a new ecosystem of open-source AI services.

About Fetch.ai

Fetch.ai Inc. is a Delaware-based AI company and a founding member of the ASI Alliance and it is advancing intelligent automation through its AI-driven agent technology. Built on the Cosmos network, Fetch.ai provides a modular platform that enables developers and businesses to create, deploy, and monetize next-generation AI applications. Its flagship product, Agentverse, combines large language models (LLMs) with autonomous AI agents, offering a dynamic marketplace that improves user interactions and streamlines digital services.

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