In a recent cryptocurrency trading experiment, a Chinese AI model called DeepSeek delivered an impressive performance—within just 9 days, it grew its initial capital from $10,000 to $22,500 in the Alpha Arena crypto trading competition, achieving a remarkable 125% return.
This result even surpassed Alibaba’s Qwen 3 Max model, positioning DeepSeek as a rising star in the AI trading sector.
The Origins and Development of DeepSeek
DeepSeek is an AI company based in Hangzhou, China, founded in 2023 with investment from the renowned quantitative asset management firm High-Flyer.
The company is dedicated to developing advanced large language models and related technologies, having released several models including DeepSeek LLM, DeepSeek Coder, DeepSeekMath, and DeepSeek-VL.
On January 20, 2025, DeepSeek officially launched DeepSeek-R1, a model that matches OpenAI O1 in tasks involving mathematics, coding, and natural language reasoning. The latest release, DeepSeek-V3.2, has garnered significant attention for reducing AI inference costs to just one-sixth to one-seventh of V3.1, while accelerating long-context processing by two to three times.
DeepSeek Model Family and Technical Evolution
Innovative Model Architecture
DeepSeek’s technical architecture combines variants of the Transformer structure with dynamic attention mechanisms, achieving a balance between semantic understanding and generation through multi-scale feature fusion.
Its core strengths are highlighted in three key areas:
- Dynamic Sparse Attention Mechanism: By introducing gated units that dynamically allocate attention weights, DeepSeek maintains strong long-text processing capabilities while reducing computational complexity. When handling documents with 100,000 tokens, computation is reduced by 42% compared to standard Transformers.
- Mixture-of-Experts System: Utilizing a routing mechanism across 16 expert modules, each token activates only 2 to 3 experts, increasing model capacity while controlling inference costs.
- Progressive Training Strategy: DeepSeek employs phased pre-training, instruction fine-tuning, and reinforcement learning from human feedback. In code generation scenarios, synthetic data augmentation boosts code accuracy to 89.7%.
Outstanding Performance
On the MMLU benchmark, the DeepSeek-72B model scored 81.3 in STEM fields such as mathematics and physics, outperforming GPT-4’s score of 79.8.
For code completion tasks, it achieved a Pass@1 rate of 68.2%, a 12-point improvement over Codex.
DeepSeek’s Performance in Crypto and Financial Markets
Standing Out in Crypto Trading Competitions
In the Alpha Arena crypto investment project launched by Nof1, DeepSeek’s Chat V3.1 demonstrated exceptional trading capabilities.
The competition gave six AI models a starting capital of $10,000 each, operating under identical market information conditions to trade digital assets like Bitcoin, Ether, and Dogecoin in pursuit of the highest returns.
As of October 28, DeepSeek had achieved a 125% return, far ahead of its international rivals.
By comparison, OpenAI’s GPT-5 lost nearly 60% of its funds, dropping its balance to around $4,000, while Google DeepMind’s Gemini 2.5 Pro suffered a 57% loss.
On the prediction platform Polymarket, traders gave DeepSeek a 61% probability of winning, significantly higher than Alibaba’s 29%.
Strong Performance in U.S. Stock Trading
DeepSeek has also excelled in U.S. equity trading.
In the "AI-Trader" open-source experiment led by the University of Hong Kong, DeepSeek topped the leaderboard during a month-long test period with an annualized return of 10.61%, compared to just 2.13% for the Nasdaq 100 technology stock benchmark.
This means DeepSeek’s returns were nearly five times higher than the benchmark.
DeepSeek’s API Pricing Advantage and Open-Source Strategy
Significant Price Reductions
On September 29, 2025, DeepSeek released the DeepSeek-V3.2-Exp model and announced major API price reductions.
Under the new pricing policy, input token cache hits are priced at 0.2 RMB per million tokens, cache misses at 2 RMB per million tokens, and output at 3 RMB per million tokens—a reduction of more than 50% compared to previous rates.
The latest DeepSeek-V3.2 model further slashes AI inference costs to just one-sixth to one-seventh of V3.1, with API pricing set at $0.28/$0.028/$0.42 per million input/cached/output tokens, respectively.
Open-Source Strategy and Localized Deployment
DeepSeek uses the MIT license and has been optimized for Huawei and other Chinese chipsets, facilitating deployment in local Chinese computing environments.
This open-source approach allows developers to deploy DeepSeek models for free and privately, unlocking more possibilities for enterprise-level applications.
Looking Ahead
As AI trading technology continues to advance, it’s clear that domestic large models like DeepSeek will play an increasingly important role in the future of cryptocurrency and broader financial markets.
For crypto traders, following DeepSeek’s development is not just about staying at the forefront of AI technology—it’s also about seizing potential investment opportunities in the evolving financial landscape.
