Core Insights - The experiment involved six advanced AI models trading in the cryptocurrency market with a starting capital of $10,000 each, aiming to determine which model could generate the highest returns [1][2] - The Chinese model Qwen 3 Max emerged as the top performer, achieving a net value of $15,594, representing a nearly 60% increase, while other models like GPT-5 and Gemini 2.5 Pro suffered significant losses of 72.1% and 61.9% respectively [1][10][11] Group 1: AI Performance - Qwen 3 Max and DeepSeek were the standout performers, with Qwen achieving a net value of $15,594 and DeepSeek at $12,926 by October 24 [10][11] - In contrast, GPT-5 and Gemini 2.5 Pro faced severe declines, with net values dropping to $2,783 and $3,792 respectively, indicating a loss of over 60% of their initial capital [11][12] Group 2: Trading Strategies and Personalities - The AI models exhibited distinct trading styles, with DeepSeek characterized as aggressive, focusing on high-frequency trading and short-term gains [12][13][16] - Qwen 3 Max adopted a more conservative approach, executing only 22 trades with a focus on low volatility and risk management, which contributed to its success [17][18] - In contrast, Gemini 2.5 Pro engaged in excessive trading with high leverage, leading to chaotic decision-making and significant losses [22][24] Group 3: Market Dynamics and AI Adaptability - The experiment highlighted that intelligence alone does not guarantee profitability in trading; adaptability and the ability to self-correct in response to market conditions are crucial [25][26] - The financial market serves as a dynamic environment that tests the judgment, adaptability, and risk management capabilities of AI models, contrasting with more static environments like games [26][27]

创新是人类进步的源泉，青年是创新的重要生力军。 10月13日至15日，来自国内外的1500余支参赛队伍齐聚河南郑州，在中国国际大学生创新大赛（2025）总决赛舞台上同台竞技、各展所长。 从校赛、省赛，到区域赛、总决赛，大赛自今年4月启动以来，共吸引来自国内外161个国家和地区的5673所学校、2443万人次参赛。他们在创 新实践中增本领、长才干，在互学互鉴中增进中外青年的友谊。 | Al业界金标准： ARC-AGI 2 超越 GPT-5/DeepSeek R1 | 9 | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | | ARC-AGI | | | | | | | | | 图片流源: OpenAL发布会 | "OpenAl o3 and e3-mini-12 Days of OpenAl: Day 12" | 运算成本 (cost/task) | 参数大小* | ARC-AGI 2 ** | 模型 | | | | $2.17 | 16% | > 1T | Grok 4 (Thinking) | 8% | <$0.01 | HRM-b ...