AI Crypto Trading Challenge: DeepSeek Leads Viral Competition

The viral Alpha Arena competition, organized by AI research lab Nof1, has captured attention across social media by pitting seven leading AI models against each other in a real-money crypto trading challenge. The competitors include DeepSeek Chat V3.1, Claude Sonnet 4.5, GROK 4, QWEN3 MAX, Gemini 2.5 PRO, and GPT5, each operating with a $10,000 trading account on the Hyperliquid platform.

According to Nof1 founder Jay Azhang, this represents the first season of what the team plans to be an ongoing experiment. The research lab believes financial markets serve as “the best training environment for the next era of AI” and “the ultimate world-modeling engine.” All trading activity is verifiable on-chain through Hyperliquid, providing complete transparency into each model’s performance and strategy.

The competition rules are straightforward: each AI model operates independently, and only completed trades are counted in performance calculations. Active positions remain excluded from the statistics until closed, meaning the leaderboard can shift dramatically as positions are realized.

After four days of trading, DeepSeek and Claude have emerged as clear frontrunners, both showing approximately 10% gains on their total portfolio value in realized P&L. DeepSeek’s success appears driven by a strong long bias, with the model currently holding six active long positions on major cryptocurrencies including XRP, DOGE, BTC, ETH, SOL, and BNB.

Analysis of DeepSeek’s trading history reveals that five of its last six trades were long positions, with only one short trade. The model’s most profitable trade came from longing XRP at $2.29 and closing at $2.45, netting nearly $1,500 in profit. This long-oriented strategy, however, proved vulnerable during recent market downturns, particularly when BTC’s price dropped approximately 3.5% over a 24-hour period.

While the exact training parameters for each model remain undisclosed, the trading dashboard reveals that models appear to use technical analysis indicators such as moving averages and MACD. The transparency extends to seeing each trade’s exit plan, though the reasoning behind individual trading decisions remains opaque.

The competition highlights stark differences in how various AI models approach crypto trading. Gemini has adopted a high-frequency trading strategy, executing trades much more frequently than other competitors. Despite closing one massive winning trade for a profit of $18,076, Gemini currently sits on a negative P&L of approximately $4,000 due to seven other losing trades.

In contrast, ChatGPT’s performance has been particularly concerning, with the model failing to execute a single successful trade out of its last 25 attempts. DeepSeek, while leading the competition, has taken far fewer trades and has realized only one major winning idea—the profitable XRP trade that accounts for most of its gains.

Grok demonstrated the potential volatility of AI trading strategies when it shifted to a full long position several days ago, rapidly climbing the leaderboard. However, when the market turned downward, most of its profits were wiped out, highlighting the challenge AI models face in adapting to changing market conditions.

The Alpha Arena challenge represents more than just a viral competition—it provides valuable insights into how different AI architectures perform in real financial market conditions. The varying success rates and trading approaches among DeepSeek, Claude, Gemini, Grok, and the other competitors suggest that AI model design significantly impacts trading effectiveness.

The experiment raises important questions about AI adaptability, particularly whether these models can effectively switch biases based on current market events. So far, only Grok has demonstrated a major strategic turnover, though the timing proved unfortunate given subsequent market movements.

As Nof1 plans future seasons that will include human traders and their “homegrown” models, the competition promises to yield increasingly valuable data about AI capabilities in financial markets. The current results suggest that while some AI models show promise in crypto trading, consistent profitability remains elusive, and market adaptability represents a significant challenge for even the most advanced systems.