AI Model Crypto Trading Competition: DeepSeek and Grok Lead the Way in Yield
TL;DR
In the Alpha Arena AI trading competition, launched by nof1 Research Lab, DeepSeek Chat V3.1 led with a return of +39.61% , followed by Grok-4 at +35.01% . As of 09:02 UTC on October 20, 2025, six major AI models had grown their total assets from $60,000 to approximately $140,000 through real money cryptocurrency perpetual contracts trading on the Hyperliquid platform, achieving an overall return of 130%.
Core Analysis
Competition Overview and Rules
The Alpha Arena competition officially launched on October 18, 2025. Six top AI models - DeepSeek Chat V3.1, Grok-4, Claude Sonnet 4.5, Qwen3 Max, GPT-5 and Gemini 2.5 Pro - each allocated $10,000 USDC funds to trade fully autonomous cryptocurrency perpetual contracts on the Hyperliquid decentralized exchange.
The competition emphasizes transparency and autonomy:
- Trading assets : BTC, ETH, SOL, BNB, DOGE, XRP six major cryptocurrencies
- Leverage range : Up to 25x leverage, AI models typically use 10-25x leverage
- Execution method : Completely on-chain execution, no manual intervention, all transactions can be verified through blockchain browsers
- Strategy restrictions : Ensemble learning or fine-tuning for trading is prohibited, and basic AI capabilities are tested.
Real-time rankings and performance indicators
| Ranking | AI models | Account Value | Yield | Total P&L | Transaction fees | Win rate | Sharpe Ratio | Number of completed transactions |
|---|---|---|---|---|---|---|---|---|
| 1 | DeepSeek Chat V3.1 | $13,961 | +39.61% | +$3,961 | $104.53 | 16.7% | 0.022 | 6 |
| 2 | Grok-4 | $13,501 | +35.01% | +$3,501 | $9.18 | 0% | 0.023 | 1 |
| 3 | Claude Sonnet 4.5 | $12,438 | +24.38% | +$2,438 | $115.23 | 20% | 0.025 | 5 |
| 4 | Qwen3 Max | $10,835 | +8.35% | +$835 | $230.82 | 37.5% | 0.018 | 8 |
| 5 | GPT-5 | $7,368 | -26.32% | -$2,632 | $89.86 | 0% | -0.022 | 12 |
| 6 | Gemini 2.5 Pro | $6,955 | -30.45% | -$3,045 | $447.85 | 19.1% | -0.019 | 47 |
Trading strategy analysis
DeepSeek Chat V3.1 (Leading Strategy) :
- Core Strategy : Adopting the "optimal long" strategy, establishing 10-15x leveraged long positions on all six assets
- Key decision : Heavy XRP long (the only model with a heavy position in this currency), with a floating profit of over $800
- Risk control features : Lowest transaction frequency (only 6 completed transactions), effectively controlling handling fee costs
- Position management : Cash reserves of US$2,840 to provide flexibility for market fluctuations
Grok-4 (efficient strategy) :
- Core strategy : opportunistic reversal strategy, accurately grasp the market timing
- Key decision : Successfully buy the dips BTC on October 19th, flipping from a short position to a long position, capturing a $3,500 profit.
- Execution efficiency : Very few transactions (only 1 completed transaction) and the lowest transaction fee cost ($9.18)
- Historical Performance : Grows from $200 to $1,000 in a single day (+400%) in private testing
Poorly performing models :
- GPT-5 : Over-reliance on short-selling strategies led to significant losses during market upturns, with all 12 completed trades resulting in losses.
- Gemini 2.5 Pro : Excessive trading (47 trades), fees as high as $447.85, and aggressive 15-25x leverage strategies that backfired
Social media response
There was a lot of interest in the results of the competition on social media, with key topics of discussion including:
Praise for the leaders :
- DeepSeek's leading position shocked the community and was praised as a "dark horse win," validating the practical advantages of open source models.
- Grok has been praised for its conservative risk-reward balance and precise market timing.
- The community sees this as "real money proof" of AI's trading capabilities .
Criticism of laggards :
- Gemini 2.5 Pro was ridiculed for its massive 42% loss, with its aggressive pattern matching strategy deemed unsuitable for the unpredictable nature of cryptocurrencies.
- Users question "high-end" AI's failure in basic risk management x.com
Overall narrative theme :
- Open Source vs. Giant Dynamics : DeepSeek's Lead Challenges the Hype of GPT-5 and Claude
- The value of transparency : Hyperliquid’s on-chain visibility hailed as a game-changer
- AI's Limitations : Failure Cases Highlight AI's Struggles with Emotion-Driven Fluctuations
Technical infrastructure
The competition is based on the high-performance DeFi transaction infrastructure of the Hyperliquid Layer-1 blockchain:
Platform features :
- Execution speed : Sub-millisecond execution speed, supporting high-frequency decision-making of AI agents
- Liquidity depth : supports 100+ perpetual contracts, with daily trading volume exceeding US$5 billion
- Risk control : Built-in automatic liquidation (ADL) mechanism to prevent chain liquidation
Data supply :
- Real-time data : price, candlestick chart, trading volume, order book depth
- Update frequency : sub-minute real-time updates, supporting dynamic analysis
- Data source : Hyperliquid on-chain data, supplemented by Chainlink and other oracles
On-chain activity analysis
Overall activity pattern :
- Initial funding : 10,000 USDC per dedicated vault for each AI model
- Leverage used : Average leverage of 15x, total notional trading volume estimated to be over $1 million
- Position characteristics : short-term holding (several hours to several days), high-frequency adjustment strategy
Key chain trends :
- October 18 : Initial 10,000 USDC deposited into each model vault
- October 19th : Collectively capturing the BTC/ETH rebound, DeepSeek’s XRP longs made a profit of $800
- Capital Flow : No significant capital outflow, all capital remains in the vault for trading
in conclusion
DeepSeek Chat V3.1 established a clear advantage in the AI crypto trading competition with its comprehensive long-term strategy and precise risk control. Grok-4 followed closely behind with efficient trade execution and precise market timing. This competition not only demonstrated the potential of AI in financial markets but also exposed significant differences between different AI models when handling highly volatile assets. The competition's transparency and on-chain execution set a new benchmark for AI-DeFi integration and bodes well for the future of autonomous trading agents.