In January 2025, Chinese AI lab DeepSeek released R1 — an open source reasoning model that matched OpenAI's o1 on major benchmarks. The AI world took notice. Not because another model hit a benchmark, but because of what it represented: the end of American monopoly on frontier AI.
What DeepSeek R1 Achieved
The numbers were hard to dismiss:
- AIME 2024: 79.8% (o1: 79.2%)
- MATH-500: 97.3% (o1: 96.4%)
- Codeforces: 96th percentile
- GPQA Diamond: competitive with o1
All of this from an open source model with openly published training methodology. DeepSeek did not just match o1 — they showed you how they did it.
The Training Innovation
What made DeepSeek R1 remarkable was not just the results but the approach. The team published a detailed technical report describing how they trained the model to reason using reinforcement learning, without relying on supervised chain-of-thought data from a stronger model.
Key innovations included:
Group Relative Policy Optimization (GRPO) — a more efficient alternative to standard RLHF that reduced training costs significantly.
Emergent reasoning — rather than training on human-written reasoning chains, the model developed its own reasoning strategies through RL. It learned to verify its answers, break down problems, and correct mistakes — all emergent behaviors.
Distillation to smaller models — DeepSeek showed that you could distill R1's reasoning abilities into models as small as 1.5B parameters, with the distilled Qwen-based 32B model outperforming OpenAI's o1-mini.
The Cost Question
Perhaps the most discussed aspect was cost. Reports suggested DeepSeek trained their model for a fraction of what US labs spend — potentially under $10 million in compute, compared to the hundreds of millions estimated for models like GPT-4.
While exact comparisons are difficult (different accounting methods, hardware costs vary by region, and DeepSeek had access to prior research), the broader point stands: you do not need unlimited compute budgets to build frontier models.
What This Means Globally
DeepSeek R1's release had several implications:
Open source wins again — the model is freely available under an MIT license. Anyone can download, run, fine-tune, and deploy it.
AI is global — frontier capabilities are no longer locked behind US-based companies. China, France (Mistral), and others are producing world-class models.
Competition drives progress — more players means faster iteration, lower prices, and more diverse approaches to solving AI problems.
Export controls have limits — despite US restrictions on selling advanced AI chips to China, DeepSeek found ways to train competitive models with available hardware.
For the Turkish AI Community
DeepSeek's success is encouraging for every AI community outside the US:
- You do not need unlimited resources to build powerful AI systems
- Open source models from diverse sources give you more options than ever
- Novel training techniques can compensate for compute disadvantages
- Regional adaptation of these models for Turkish language and use cases is more viable than ever
The message is clear: the AI frontier is not owned by any single country or company. It is open territory, and the Turkish AI community has every opportunity to participate.