Core Insights - The first AI large model real-time investment competition "Alpha Arena" concluded on November 4, featuring six top models from China and the US, each starting with $10,000 in a real market environment [1][2] - Qwen3-Max emerged as the champion with a return of $12,200, exceeding 20% profit, while DeepSeek v3.1 secured second place with a net value of $10,490, making them the only two profitable models [2] Group 1 - The competition was initiated by Nof1 on October 18, involving models such as DeepSeek v3.1, Qwen3-Max, GPT-5, Gemini2.5Pro, Claude Sonnet4.5, and Grok4 [1] - In the early stages, DeepSeek v3.1 led the competition, attracting significant international attention, while Grok4, backed by Elon Musk, narrowed the gap to just $1 at one point [1][2] - A turning point occurred between October 21 and 22, when Grok4 and Claude Sonnet4.5 experienced significant losses, leading to a day where all six models reported negative returns [1][2] Group 2 - Following the losses of other models, DeepSeek v3.1 and the previously underperforming Qwen3-Max adjusted their investment strategies, resulting in a rise in their net value [2] - The competition ultimately became a contest between Qwen3-Max and DeepSeek v3.1, with both models frequently exchanging the lead [2] - The four US models, including GPT-5, Gemini2.5Pro, Claude Sonnet4.5, and Grok4, ended up with losses, with GPT-5 suffering a decline of over 60% [2]

Core Insights - The "Alpha Arena" AI investment competition initiated by the US research lab nof1.ai is becoming a public test to observe the autonomous trading capabilities of AI models [1][7] - Six major AI models are participating, including Qwen3Max, which currently leads in returns, showcasing its ability to self-optimize through real-time reinforcement learning [1][2] Performance Comparison - Qwen3Max has a return of +19.57%, with an account value of $11,957, outperforming other models significantly [3] - In contrast, Gemini2.5Pro and GPT-5 have experienced losses exceeding 50%, indicating a more aggressive strategy that led to poor performance [2][3] - Qwen3Max's trading behavior reflects a balance of efficiency and stability, with an average holding period of about 7 hours and a return increase from 8.43% to 13.41% [2][3] Strategy and Risk Management - Qwen3Max focuses on opportunity capture and risk balance, executing trades quickly during market volatility while maintaining a low-risk exposure [2] - The competition highlights the differences in risk management and strategy adjustment mechanisms among the AI models, with Qwen3Max demonstrating superior performance [2][4] Technological Advancements - The competition reveals the advantages of reinforcement learning and real-time decision-making capabilities in AI models, which adapt to high-volatility environments [4][7] - Qwen series models are evolving towards a multi-modal capability, enhancing their ability to generate strategies, control risks, and self-correct in complex trading environments [4][7]