What is DeepSeek AI? Learn about this domestic large model sweeping through cryptocurrency trading

In recent cryptocurrency trading experiments, a Chinese AI model named DeepSeek performed remarkably well. During the Alpha Arena cryptocurrency trading competition, it grew the initial capital from $10,000 to $22,500 in just 9 days, achieving an astonishing 125% return rate.

This achievement even surpassed Alibaba’s Qwen 3 Max model, making DeepSeek a rising star in the AI trading field.

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The Origin and Development of DeepSeek

DeepSeek (Deep Exploration) is an AI company based in Hangzhou, China, founded in 2023 and invested in by the well-known quantitative asset management giant 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-R 1, a model with performance in mathematics, coding, and natural language reasoning comparable to OpenAI’s GPT-4. The latest release, DeepSeek-V 3.2, has attracted widespread attention, reducing AI reasoning costs to 1/6 to 1/7 of V 3.1, with long context acceleration of 2 to 3 times.

DeepSeek Model Family and Technological Evolution

Model Architecture Innovation

DeepSeek’s technical architecture integrates variants of Transformer structures with dynamic attention mechanisms, achieving a balance between semantic understanding and generation through multi-scale feature fusion.

Its core advantages are reflected in three aspects:

• Dynamic Sparse Attention Mechanism: By introducing gating units to dynamically allocate attention weights, it maintains long-text processing capabilities while reducing computational complexity. When handling documents of 100,000 tokens, computation is reduced by 42% compared to standard Transformers.
• Mixture of Experts System: Uses a routing mechanism with 16 expert modules, activating only 2 to 3 experts per token, increasing model capacity while controlling inference costs.
• Progressive Training Strategy: Conducts phased pretraining, instruction fine-tuning, and human feedback reinforcement learning. In code generation scenarios, synthetic data augmentation increased code correctness to 89.7%.

Performance Excellence

On the MMLU benchmark, DeepSeek-72B scored 81.3 in STEM fields such as mathematics and physics, surpassing GPT-4’s 79.8.

In code completion tasks, Pass@1 reached 68.2%, a 12 percentage point improvement over Codex.

DeepSeek’s Performance in Cryptocurrency and Financial Markets

Excelling in Cryptocurrency Trading Competitions

In the Alpha Arena cryptocurrency investment project launched by Nof1, DeepSeek’s Chat V 3.1 demonstrated outstanding trading ability.

The competition involved six AI models starting with $10,000 each, competing under the same market information conditions by trading digital assets like Bitcoin, Ether, and Dogecoin for the highest returns.

As of October 28, DeepSeek achieved a 125% return, far exceeding other international competitors.

In comparison, OpenAI’s GPT-5 lost nearly 60% of its funds, dropping to about $4,000; Google DeepMind’s Gemini 2.5 Pro also suffered a 57% loss.

On the prediction platform Polymarket, traders’ forecast probability of DeepSeek winning reached 61%, far higher than Alibaba’s 29%.

The US stock market trading performance is equally impressive

Not only in the cryptocurrency market, but DeepSeek also performs well in US stock trading.

In the “AI-Trader” open-source experiment led by the University of Hong Kong, DeepSeek achieved a 10.61% annualized return during nearly a month of testing, winning the competition, while the Nasdaq 100 index benchmark, tracking technology stocks, only returned 2.13% during the same period.

This means DeepSeek’s return rate is nearly five times higher than the benchmark.

DeepSeek’s API Price Advantage and Open Source Strategy

Significant Price Drop

On September 29, 2025, DeepSeek released the DeepSeek-V 3.2-Exp model and announced a substantial reduction in API prices.

Under the new pricing policy, the cost is 0.2 yuan per million tokens for cached hits, 2 yuan per million tokens for cache misses, and 3 yuan per million tokens for output, representing a reduction of over 50% compared to previous rates.

The latest DeepSeek-V 3.2 model further drops AI reasoning costs to 1/6 to 1/7 of V 3.1, with API pricing at $0.28 / $0.028 / $0.42 per million input/cached/output tokens.

Open Source Strategy and Local Deployment

DeepSeek adopts the MIT license, optimized for Huawei and Chinese chips, facilitating deployment within China’s local computing environment.

This open-source approach allows developers to deploy DeepSeek models for free and privately, providing more possibilities for enterprise applications.

Future Outlook

As AI trading technology continues to evolve, we have reason to believe that domestic large models like DeepSeek will play an increasingly important role in the future of cryptocurrency markets and broader financial sectors.

For cryptocurrency traders, following DeepSeek’s development is not only about staying at the forefront of AI technology but also about seizing potential investment opportunities in future financial markets.