AI Models Achieve 130% Growth in Real-Money Crypto Trading Competition on Hyperliquid

Executive Summary

Six major AI models commenced a real-money cryptocurrency trading competition on the Hyperliquid decentralized exchange, collectively increasing their initial $60,000 capital by over 130% within 48 hours, highlighting the rapid advancement of AI in financial applications.

The Event in Detail

An unprecedented on-chain AI trading competition is underway, featuring six major general-purpose AI models including DeepSeek Chat V3.1, Grok4, Claude Sonnet4.5, Qwen3Max, GPT5, and Gemini2.5Pro. Each model began with an initial allocation of $10,000 and uniform trading instructions. The competition, hosted on the Hyperliquid perpetual futures platform, involves autonomous trading of BTC, ETH, and SOL cryptocurrencies. Real-time data from October 18 to October 20 demonstrates significant performance, with the total assets across the six AI accounts growing from approximately $60,000 to $140,000, representing an increase of over 130% in just two days. DeepSeek Chat V3.1 currently leads the competition with a balance of $12,700, closely followed by Grok4 at $12,470. Claude Sonnet4.5 ranks third with $10,934. The remaining models, Qwen3Max ($9,584), GPT5 ($7,552), and Gemini2.5Pro ($6,726), showed varying results within the same period. Analysis of the trading strategies revealed significant differences, with some models favoring high-frequency arbitrage operations and others adopting long-term holding strategies. Multiple AI models successfully captured short-term rebound opportunities during periods of BTC price fluctuations.

Financial Mechanics

Each of the six participating AI models was allocated $10,000 in initial capital, totaling $60,000 for the competition cohort. The trading activity is conducted on Hyperliquid, a decentralized exchange known for its focus on perpetual contracts, providing the high liquidity and low latency necessary for high-frequency trading. The models are tasked with buying and hedging BTC, ETH, and SOL. As of October 20, DeepSeek Chat V3.1 demonstrated a 27% return on its initial capital, reaching $12,700. Grok4 achieved a 24.7% return, holding $12,470. Claude Sonnet4.5 posted a 9.34% return with $10,934. Conversely, Qwen3Max recorded a -4.16% return ($9,584), GPT5 a -24.48% return ($7,552), and Gemini2.5Pro a -32.74% return ($6,726). Collectively, the total assets under AI management surged from $60,000 to $140,000, marking a 130% increase within 48 hours.

Business Strategy & Market Positioning

This real-money trading experiment serves as a critical benchmark for evaluating the raw trading capabilities of large AI models in a transparent, on-chain environment. It aims to showcase the practical applications and potential of AI and DeFi integration, positioning AI as a potentially composable layer for various crypto protocols. The initiative highlights a broader trend in the Web3 ecosystem where AI-driven agents are moving beyond traditional bots, analyzing data, reasoning, and making autonomous trading decisions. This strategic move aligns with the increasing interest in agentic AI in blockchain, where intelligent systems are being leveraged to automate trading, manage on-chain operations, and contribute to decentralized governance structures. The competition framework, with its uniform prompt settings and traceable on-chain real trading, ensures a fair starting point for all participants and aims to avoid bias in evaluating performance.

Broader Market Implications

This competition holds significant implications for the broader Web3 ecosystem, potentially driving innovation in DeFAI (Decentralized Finance Artificial Intelligence) and fostering the development of new AI-driven trading strategies and protocols. The convergence of AI and DeFi promises substantial improvements in execution, security, and capital efficiency across financial markets. This includes the emergence of advanced agent frameworks capable of placing orders, real-time anomaly detection through oracles, continuous portfolio management, and compute networks that offer model inference as a service. While the experiment demonstrates the impressive capabilities of AI in generating returns, it also underscores the growing autonomy of these systems. As AI agents become more sophisticated and integrated into financial markets, the need for robust governance frameworks to mitigate potential systemic risks and prevent market instability will become increasingly critical. The transparent and auditable nature of this on-chain competition contrasts with opaque centralized AI evaluation systems, aligning with the principles of projects like Recall Network, which aim to build trustworthy, neutral decentralized AI competition platforms with community-driven on-chain record-keeping and knowledge sharing.