AI Large Model Real Trading Showdown: DeepSeek and Grok Lead the Way, Revealing Different Models' Investment Philosophies
The Alpha Arena live trading competition initiated by the AI research lab nof1.ai places six major AI models in a real trading environment of the cryptocurrency market. Data shows that DeepSeek and Grok-4 have a return rate of nearly 40%, while GPT-5 and Gemini have lost over 25%. Through trading performance and style, the experiment reveals the investment philosophies and risk management capabilities of different AI models, promoting a new paradigm for AI evaluation.Author: Bruce
1. A Real Money AI Trading Showdown
The latest results of the "Alpha Arena" real trading competition initiated by the AI research lab nof1.ai are out, and the performance differences are shocking. As of October 20, 2025, data shows that DeepSeek V3.1 achieved an astonishing return of +39.9%, followed closely by Grok-4 with a return of +35.3%. Meanwhile, two other well-known models, GPT-5 and Gemini 2.5 Pro, performed poorly, recording losses of -26.2% and -30.28%, respectively.
This showdown is not a simulation, but a real money contest. It places the world's top general AI models in the ultimate adversarial environment—the rapidly changing financial market.
2. Background and Rules of the Experiment
This trading competition is hosted by the AI research lab nof1.ai. Its founder, Jay Azhang, has a multidisciplinary background in engineering, finance, and biology, having previously increased a fund's management scale from $3 million to $20 million. His core belief is that the financial market is the "ultimate testing ground" for AI, a dynamic training environment that becomes increasingly challenging as AI improves, making it an excellent place to create a "real-world version of AlphaZero."
The competition rules are as follows:
Participating Models: A total of six top global AI models are involved, including GPT-5, Gemini 2.5 Pro, Grok-4, Claude Sonnet 4.5, DeepSeek V3.1, and Qwen3 Max.
Initial Capital: Each model is allocated $10,000 in real funds.
Trading Targets: Autonomous trading of perpetual contracts for mainstream cryptocurrencies such as BTC, ETH, SOL, BNB, DOGE, and XRP.
Trading Platform: All trades are executed on Hyperliquid, ensuring fund security and trading transparency.
Competition Duration: Started on October 18, 2025, and is ongoing.
3. AI Trading "Personality" Analysis: From Sniper to High-Frequency Trader
More valuable is the fact that this trading competition reveals the distinct trading "personalities" or investment philosophies that have emerged behind different AI models through detailed trading data.
1. Leaders: Patient Snipers and Cautious Holders
DeepSeek V3.1 (+39.9%) and Grok-4 (+35.3%) have very clear successful strategies: high conviction, low frequency.
DeepSeek is known as the "Patient Sniper," completing only 6 trades with an average holding time of over 21 hours, and the vast majority are long positions. This strategy indicates that the model tends to wait for high-certainty opportunities and then let profits run. Notably, although there have been recent criticisms of the DeepSeek model from a U.S. government report, this excellent real trading performance provides market validation of its capabilities.
Grok, on the other hand, is the "Cautious Holder," completing only 1 trade with an average holding time of 54 hours. Its success may stem from its unique architecture, which allows it to gather real-time network information, enabling it to better integrate market sentiment and news events, a capability regarded by the community as a significant advantage in trading.
2. Middle Tier: Agile Bulls and Balanced Opportunists
Claude Sonnet 4.5 (+24.51%) exhibits a completely different style. It acts like an "Agile Bull," with an average holding time of only 3 hours and 40 minutes across 5 trades, and 100% of its positions are long.
Qwen3 Max (+8.43%) resembles a "Balanced Opportunist," completing 8 trades with an average holding time of 7 hours and 24 minutes, showing a more robust strategy.
3. Laggards: Contrarian Bears and High-Frequency Traders
GPT-5 (-26.2%) seems to have a strategy that does not adapt well to the current market environment. Despite completing 12 trades with an average holding time of over 23 hours, its performance is poor, which may reflect deficiencies in its risk management mechanisms.
Gemini 2.5 Pro (-30.28%) is a typical "High-Frequency Trader," completing as many as 47 trades with an average holding time of only 6 hours and 48 minutes. High-frequency trading led to significant transaction costs, ultimately resulting in substantial losses.
4. Data Summary: Comparison of AI Model Trading Performance
The following table summarizes the specific performance of different AI trading strategies as of October 20, 2025 (data source: Alpha Arena by nof1.ai):
From trading only once to trading 47 times, the strategy differences among the models are clear at a glance.
5. Why This Matters: A New Paradigm for AI Capability Assessment
The significance of the Alpha Arena competition goes far beyond a trading contest. It represents a shift in the paradigm of AI assessment, revealing that these large models are forming unique trading "personalities"—from patient value investors to active day traders.
This is not only a Turing test for financial capabilities but, more importantly, it shifts AI evaluation from static, academic benchmark tests to a public, verifiable, and adversarial real-world environment. In this environment, AI models must face market uncertainties, volatility, and competition from other participants, which reflects their true capabilities in complex real-world scenarios more accurately than traditional benchmark tests.
The innovative significance is reflected in three aspects:
Real-time Assessment: Unlike static dataset testing, financial markets provide a continuously changing challenge environment.
Multidimensional Capability Examination: Simultaneously tests various comprehensive skills such as risk management, strategy formulation, and execution ability.
Objective Result Measurement: Uses actual profits and losses as the sole evaluation criterion, avoiding biases from subjective assessments.
The results of this experiment will undoubtedly provide valuable insights for the future application of AI in finance and other dynamic decision-making fields. It not only showcases the capability differences among various AI models but also opens new perspectives for understanding how AI operates in complex, dynamic real-world environments.
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