Core Insights - The first Nof1 AI model trading competition concluded with unexpected results, showcasing the investment capabilities of AI models in cryptocurrency trading [1][5][9] Group 1: Competition Overview - The competition was designed as a benchmark test for AI investment capabilities, referred to as the "Turing Test of the cryptocurrency world," initiated by Nof1.ai from October 17 to November 3, 2025 [1] - Six AI models participated, including DeepSeek Chat V3.1, Grok 4, Gemini 2.5 Pro, GPT-5, Qwen3 Max, and Claude Sonnet 4.5, representing the latest technology from both Chinese and American suppliers [1][3] - Each model started with $10,000 in initial capital and traded autonomously on Hyperliquid, focusing on six popular cryptocurrencies: BTC, ETH, SOL, BNB, DOGE, and XRP [3][4] Group 2: Trading Performance - Qwen3 Max ranked first with a return of 22.3%, total profit of $2,232, and a win rate of 30.2% over 43 trades [5][7] - DeepSeek Chat V3.1 secured second place with a return of 4.89%, total profit of $489.08, and a win rate of 24.4% over 41 trades [5][7] - The remaining models, including Claude Sonnet 4.5, Grok 4, Gemini 2.5 Pro, and GPT-5, experienced significant losses, with returns of -30.81%, -45.3%, -56.71%, and -62.66% respectively [6][15] Group 3: Model Characteristics - Qwen3 Max exhibited an aggressive trading strategy with a high return and significant trading frequency, while maintaining a Sharpe ratio of 0.273 [13] - DeepSeek Chat V3.1 demonstrated a more conservative approach with a higher Sharpe ratio of 0.359, indicating better risk management [13] - In contrast, models like Gemini 2.5 Pro and GPT-5 showed poor performance due to excessive trading and lack of effective market judgment, reflected in their negative Sharpe ratios of -0.566 and -0.525 respectively [15][16] Group 4: Market Implications - The competition has garnered significant attention, with industry leaders commenting on the potential impact of AI trading strategies on market dynamics [9][11] - There is speculation that widespread use of similar AI models could influence market behavior, potentially driving prices up through collective demand [10][11]