AI Crypto Trading Competition 2025: DeepSeek and Grok Dominate as Gemini and GPT-5 Suffer Major Losses

By Editorial Team - October 29, 2025

In this Article:

AI models Grok, DeepSeek, and Claude are up big in a live, real-money crypto trading showdown.
Google’s Gemini 2.5 Pro is the biggest loser of the bunch, underscoring reliability and transparency risks.
The results deepen the divide between data-specialized and general-purpose AI approaches to finance.

In early innings, Elon Musk's Grok, DeepSeek, and Anthropic's Claude Sonnet 4.5 are emerging as the top performers in a real-money AI crypto trading showdown, each generating returns of over 25% so far while rival models have suffered heavy losses.

The "Alpha Arena," a competition that pits prominent large language models against each other in the live cryptocurrency market, saw OpenAI's GPT-5 and Google's Gemini 2.5 Pro with staggering losses of more than 28% during the same period. (decrypt.co)

In Alpha Arena, launched by US research firm Nof1, six large language models (LLMs) were given US$10,000 each to invest in six cryptocurrency perpetual contracts on the decentralised exchange Hyperliquid, including bitcoin and solana.

The other LLMs included in the first batch of models for the experiment, which runs until November 3, are Alibaba Cloud’s Qwen 3 Max, Anthropic’s Claude 4.5 Sonnet, Google DeepMind’s Gemini 2.5 Pro and xAI’s Grok 4. Alibaba Cloud is the AI and cloud computing unit of Alibaba Group Holding, owner of the Post.

Each AI model was given a starting capital of $10,000 to trade cryptocurrency perpetual contracts on the Hyperliquid exchange, betting on assets including Bitcoin, Dogecoin, and Solana.

The stated objective for the models is to maximize their risk-adjusted returns. The rules emphasize autonomy, requiring each AI to independently generate its trading ideas, size and time its trades, and manage its own risk, with all model outputs and corresponding trades made public for transparency.

Season 1 of the contest began October 17 and runs to November 3. Here's the real-time leaderboard.

Note that the rankings are very much in flux, and possibly too preliminary to matter much. Jay Azhang, who founded Nof1, an AI research firm that hosts the contest, told Decrypt that based on previous tests, he was unsurprised by the current standings: It "usually ends up between Grok and DeepSeek," he said, but "occasionally Gemini and GPT."

Notably, GPT-5 was down over the same period by about 29%. According to Nof1, the model adopted a distinctly cautious and risk-averse strategy. Unlike the aggressive bullish bets of the winners or the erratic trading of the biggest losers, GPT-5 remained largely inactive, placing only a few small trades.

This conservative approach effectively took it out of the running for major gains, but also protected it from the significant downturns experienced by some of its rivals, positioning it as a more stable, if unprofitable, participant. Meanwhile, Claude Sonnet was comfortably in third place among the six contenders.

The results could be sending a complex signal to Wall Street, as the two frontrunners represent two vastly different potential futures for artificial intelligence in finance. DeepSeek is reportedly backed by a Chinese quantitative hedge fund, suggesting its success may stem from specialized financial data and expert fine-tuning—an evolutionary step for today's data-driven firms.

By contrast, Grok's strong performance implies that a powerful, general-purpose AI may be capable of successfully navigating markets on its own—a potentially disruptive development for the entire industry.

Still not ready for primetime

Proponents of AI trading argue that the ability of LLMs to rapidly process and analyze vast, unstructured datasets like news and social media represents the next frontier in trading. They see a future where AI can unlock new forms of alpha and democratize sophisticated market analysis.

However, the catastrophic losses of models like Gemini highlight the significant risks that make financial institutions wary. A primary concern is the "black box" nature of these systems, where the reasoning behind a trade is often opaque and unexplainable. This lack of transparency is a major hurdle for regulatory compliance and risk management, as establishing trust in a model's decisions is a critical and ongoing effort.

Beyond opacity, there are fundamental concerns about reliability. These models are known to be prone to hallucinations—fabricating convincing but false information—which could be catastrophic in a live trading environment.

Furthermore, a 2024 paper exploring the implications of LLMs in financial markets warns of a novel systemic risk: if multiple, seemingly independent AI agents are built on the same underlying foundation models, they might react to market events in a correlated way, potentially "amplifying market instabilities" and creating unforeseen flash crashes.

The Gemini 2.5 Pro model's chaotic performance in the Alpha Arena, where it reportedly engaged in frequent, erratic trading—switching from bearish to bullish stances at great loss—serves as a stark, real-world example of these dangers. Its failure highlights the unpredictability that makes the heavily regulated financial industry wary.

For now, Wall Street remains in a state of cautious exploration. While a recent report from Gilbert + Tobin suggests a rush of adoption may be coming in the next two years, it also notes that current use is mainly for "risk-free tasks with heavy human assistance, such as text summarization."

Source: https://decrypt.co/345006/ai-crypto-trading-showdown-deepseek-grok-winning-gemini-implodes

Frequently Asked Questions (FAQ)

1. What is the competition being discussed and how does it work?
The competition is the Alpha Arena Crypto Trading Competition (hosted by Nof1) where six major AI models each started with equal capital (USD 10,000) and autonomously traded cryptocurrencies (such as BTC and ETH) without human intervention. South China Morning Post
Each model was given the same market data and instructions: to maximise trading returns by deploying strategies in the volatile crypto futures market. ForkLog

2. Which AI models are participating and how are they performing so far?
Some of the key models in the competition include:

DeepSeek Chat V3.1 — reportedly turned USD 10,000 into over USD 22,900 (a ~126 % gain) in under two weeks. Cryptonews
Qwen 3 Max (by Alibaba Group) — achieved doubled capital (100 %+ return) in the early phase. South China Morning Post
Others like GPT‑5 (by OpenAI) and Gemini 2.5 Pro (by Google DeepMind) lagged behind, some even posting losses. ForkLog
Because the competition is live and evolving, final results may change.

3. What are the main goals and significance of this competition?
The goals are multiple:

To test how well advanced AI can operate in real-world crypto markets — including pricing, risk, leverage, volatility. icobench.com
To compare different AI models’ trading strategies (long vs short, leverage, position sizing) under identical conditions.
From a broader perspective, the event showcases how AI is entering the autonomous finance and algorithmic trading space — which may have implications for future trading systems, hedge funds, and market structure.

4. What are the major risks or caveats investors/readers should be aware of?
While this competition is interesting, it comes with important caveats:

Past performance even in the contest doesn’t guarantee future results in live investing — the environment is specific and controlled.
Leverage is used heavily in some models; heavy losses (or full draw-downs) are possible. Some models in the competition already suffered big losses.
Crypto markets are extremely volatile, illiquid at times, and subject to external shocks (regulation, exchange risk, hacks). An AI doing well in a contest may still fail in real-world scalable conditions.
There may be differences between contest conditions and actual trading (fees, slippage, market depth, human supervision).
Hence, one should view the competition as a learning/benchmark exercise, not a ready-made “AI trading strategy you can copy”.

5. How can an investor use the insights from this competition in their own crypto strategy?
Here are some actionable take-aways:

Study what worked: e.g., which models favoured long vs short positions, how they sized trades, how they reacted to market moves.
Consider developing or selecting algorithmic strategies that emphasise risk management (draw-down protection, position limits) — since even AI models failed when the market turned.
Use the competition as a signal of technological trends: AI in trading may become more prominent, so staying informed may give you an edge.
But don’t blindly replicate: ensure you understand the underlying logic, fit it to your own risk tolerance, capital size, and market access.

6. Is this competition something that retail investors can participate in or be affected by?
Direct participation: Not really — the contest is between big AI models with large computational resources and predefined capital. Retail investors cannot join the same format.
Indirectly: Yes — outcomes may influence the broader market. For example, if AI-driven algorithms generate unusual flows, they might affect market volatility, liquidity, or the strategies of professional trading firms.
Also: If you hear claims that “AI beat the market so join us and we’ll automate your trading” — you should be cautious and do your own due diligence (see also risk of scams below).

7. Could this competition lead to more widespread automated/AI trading in crypto markets?
Quite possibly. The competition demonstrates proof-of-concept of autonomous AI trading in a high-volatility asset class. As infrastructure (cloud, exchanges, data feeds) gets cheaper and regulation advances, more firms may adopt such systems.
However: The scale and robustness required for live markets is far higher than a contest environment. Variables like slippage, market impact, liquidity crunch, regulatory risk remain challenging. So while the competition signals a trend, it should not be seen as full-scale deployment yet.

8. What do the results suggest about which strategies (e.g., long/short, leverage) are working in the contest?
From the early reports:

Models like DeepSeek used both long and short positions across multiple tokens with up to 10× leverage and had strong returns in favourable market conditions.
Qwen took a heavy long position (e.g., 25× long on ETH) when the market moved up, which gave strong gains but also increased risk.
Models that mis-timed the market (shorting during a rally) or used high-frequency trading without strong risk control incurred large losses. South China Morning Post
Thus: while leverage and aggressive strategies can magnify gains, they also magnify losses. Timing and strategy are still key.

9. How final are the results, and when will we know the winner?
The competition is still live (as of late October 2025) and scheduled to end on November 3, 2025.
Thus, any rankings or reported returns up to now are interim — final results may differ as market conditions shift. For the reader: treat current standings as “snapshot” data, not final verdicts.

10. What should I do next if I want to follow or learn from this competition?
Here are some suggestions:

Monitor official updates from the competition organiser (Nof1) and credible crypto news outlets for final results and strategy post-mortems.
Read analyses of which AI models performed best and why (e.g., strategy breakdowns, timing, risk).
Stay current on broader AI-in-trading developments: algorithmic systems, regulatory changes, exchange infrastructure.
If intrigued: explore algorithmic trading basics — strategy development, back-testing, risk control — rather than jumping into untested “AI trading bots” or services.
Always apply strong risk controls: maintain diversified holdings, limit exposure to speculative/trading-heavy strategies, and ensure you understand what you are doing.

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