Chinese AI QWEN3 Posts Profits in Bitcoin Trading Challenge, Outpacing ChatGPT

• Qwen3 Max achieved a 7.5% profit, the only model to end positively with $751 gains.

• DeepSeek secured second place, demonstrating efficiency despite lower development costs.

• ChatGPT suffered a 57% loss, dropping from $10,000 to $4,272, highlighting limitations in real-time trading.

Discover how budget Chinese AI models dominated a crypto trading challenge, beating high-profile competitors. Explore implications for AI in finance and stay ahead in crypto trends today.

What is the outcome of the latest AI crypto trading competition?

AI crypto trading competition results reveal that Qwen3 Max, a Chinese-developed model, emerged as the winner by posting the sole positive return of 7.5%, turning an initial $10,000 into $10,751. DeepSeek followed closely in second, while OpenAI’s ChatGPT recorded the worst performance with a 57% decline. This event, hosted on the Hyperliquid decentralized exchange, tested various AI chatbots’ ability to execute autonomous trades using leveraged positions on assets like Bitcoin.

How did Qwen3 Max outperform other AI models in crypto trading?

Qwen3 Max’s success in the AI crypto trading competition stemmed from its strategic use of 20x leveraged long positions on Bitcoin, initiated when BTC was priced at $104,556. According to data from CoinGlass, this model avoided liquidation risks by maintaining thresholds above $100,630, allowing it to capitalize on market upswings. Throughout the challenge, which ran until Tuesday, Qwen3 Max primarily focused on long bets in Bitcoin, Ether, and Dogecoin, adapting to real-time market signals more effectively than peers.

The competition, organized by Alpha Arena, began with $200 per bot but scaled to $10,000 starting capital. All trades occurred on Hyperliquid, emphasizing the models’ autonomous decision-making without human intervention. Qwen3 Max’s approach contrasted sharply with others, as it consistently held bullish positions, leading to its unique profitability.

Experts note that Qwen3 Max’s training, estimated at $10 million to $20 million by machine learning engineer Aakarshit Srivastava, enabled agile responses to crypto volatility. In contrast, more resource-intensive models struggled with execution delays or overly conservative strategies. DeepSeek, trained for just $5.3 million as detailed in its technical paper, also excelled by mirroring similar long-position tactics but with slightly less precision.

This outcome underscores evolving AI capabilities in financial markets. While no model accessed live data feeds during trades—positions opened as of Wednesday—their pre-trained knowledge proved decisive. CoinGlass data confirms Qwen3 Max’s portfolio remained robust, avoiding the drawdowns that plagued competitors.

Broader implications for AI in crypto trading include potential shifts toward cost-effective, specialized models. As per reports from Reuters, OpenAI invested $5.7 billion in R&D during the first half of 2025, yet its flagship ChatGPT faltered, ending with $4,272 from the initial stake—a stark reminder of the gap between funding and practical efficacy.

The event highlights how Chinese AI innovations, often developed on tighter budgets, are challenging global leaders. DeepSeek’s second-place finish, despite minimal costs, suggests that efficient algorithms may trump scale in niche applications like crypto trading. Analysts from CoinGlass emphasize that future competitions could integrate real-time data, further testing these boundaries.

In the crypto ecosystem, where rapid decisions define success, Qwen3 Max’s leveraged Bitcoin bet exemplified calculated risk-taking. By sustaining positions through Ether and Dogecoin exposure, it navigated the $19 billion market correction without major losses, positioning it ahead as Bitcoin eyes potential rises toward $200,000, according to Standard Chartered insights.

Frequently Asked Questions

What made Qwen3 Max the winner in the AI crypto trading competition?

Qwen3 Max won by achieving a 7.5% return through 20x leveraged long positions on Bitcoin, Ether, and Dogecoin, as tracked by CoinGlass. Unlike others, it avoided significant losses during market dips, ending with $10,751 from $10,000. This strategy leveraged its training for volatile crypto environments effectively.

Why did ChatGPT perform poorly in autonomous crypto trading?

ChatGPT underperformed due to conservative strategies that couldn’t adapt to crypto’s fast-paced swings, resulting in a 57% loss per CoinGlass records. Despite OpenAI’s $5.7 billion R&D spend in early 2025, as reported by Reuters, it lacked the real-time edge needed, reducing capital to $4,272.

Key Takeaways

• Budget AIs lead the way: Qwen3 Max and DeepSeek proved that lower-cost models can outperform expensive ones in specialized tasks like crypto trading.
• Leveraged positions pay off: Strategic 20x longs on major assets like Bitcoin helped winners navigate volatility successfully.
• Real-time gaps persist: Even top AIs struggle without live data access, signaling room for AI evolution in finance.

Conclusion

The AI crypto trading competition showcased Qwen3 Max’s dominance, highlighting how Chinese innovations are reshaping autonomous trading landscapes with efficient, profitable strategies. As models like DeepSeek continue to rise, the integration of AI in crypto markets promises enhanced decision-making. Investors should monitor these advancements closely, preparing for a future where AI-driven trades become standard in volatile financial arenas.