Bitcoin News Today: Specialized AI Outperforms ChatGPT in Crypto Trading, Challenging Generalist Dominance

Generated by AI AgentCoin WorldReviewed byAInvest News Editorial Team
Tuesday, Nov 4, 2025 6:21 am ET1min read
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Aime RobotAime Summary

- Chinese AI models QWEN3 MAX and DeepSeek outperformed ChatGPT in a crypto trading competition, generating 7.5% returns vs 57% losses.

- QWEN3 MAX's $10M training cost contrasted with ChatGPT's $5.7B R&D spend, highlighting specialized AI's cost-efficiency advantage.

- The contest reignited debates on task-specific AI vs generalist models, with experts citing tailored data as key to performance gaps.

- Hyperliquid's platform recorded $21.43M BTC liquidation, reflecting volatile markets amid Fed rate uncertainty and geopolitical shifts.

- Results challenge OpenAI's dominance, suggesting niche-optimized AI may lead in high-speed financial decision-making environments.

China's Budget AI Bots Outperform ChatGPT in Crypto Trading Showdown

In a striking demonstration of cost-efficiency and real-time adaptability, two Chinese-developed artificial intelligence models—QWEN3 MAX and DeepSeek—surpassed OpenAI's ChatGPT in a high-stakes cryptocurrency trading competition. The contest, hosted by decentralized exchange Hyperliquid, saw QWEN3 MAX generate a 7.5% return on a $10,000 initial investment, earning $751 in profit, while ChatGPT suffered a 57% loss, reducing its capital to $4,272, according to a

Cointelegraph report
. The results highlight a growing divide between AI models optimized for specific tasks and those relying on broad, resource-intensive training.

QWEN3 MAX's success stemmed from a 20x leveraged long position on

Bitcoin
(BTC), initiated at $104,556, with the bot maintaining exposure to
BTC
, Ether (ETH), and
Dogecoin
(DOGE) throughout the competition, as reported by Cointelegraph. By contrast, ChatGPT's underperformance despite a $5.7 billion R&D investment in 2025's first half underscores limitations in its ability to execute dynamic, real-time trading strategies. DeepSeek, which finished second, achieved its results with a mere $5.3 million in training costs, according to its technical paper.

The competition's outcome has reignited debates about the value of specialized versus generalist AI models. Machine learning engineer Aakarshit Srivastava estimated QWEN3 MAX's training costs at $10 million to $20 million, a fraction of OpenAI's expenditure, the Cointelegraph piece noted. "The difference lies in the data each model has been trained on," said Nicolai Sondergaard, a research analyst at Nansen, noting that tailored datasets and prompt engineering could enhance models like ChatGPT and Google's Gemini, according to a

Cointelegraph report
.

Hyperliquid's role as the trading platform added another layer of complexity. The exchange, known for its high-volume futures trading, recorded a $21.43 million BTC liquidation—the largest single trade in the competition—highlighting the platform's growing influence in volatile markets. Meanwhile, broader crypto markets mirrored the bots' turbulence, with $1.134 billion in 24-hour liquidations as investors grappled with Federal Reserve rate uncertainty and geopolitical trade developments.

The victory of QWEN3 MAX and DeepSeek also raises questions about the future of AI in finance. While OpenAI's recent focus on a potential $1 trillion IPO—amid a global AI arms race—suggests confidence in its long-term vision, the trading competition reveals gaps in practical, real-world applications, the report observed. For now, cost-effective, niche-optimized models appear to hold an edge in environments demanding rapid decision-making and risk management.