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DeepSeek-R1 vs. OpenAI o1: Which AI Reasoning Model Dominates in 2025?
- DeepSeek-R1 and OpenAI o1 are leading examples of a new generation of large language models (LLMs) that prioritize complex reasoning capabilities.
- DeepSeek-R1 utilizes a Mixture-of-Experts (MoE) approach, activating only 37 billion of its 671 billion parameters for each token processed.
- DeepSeek-R1’s training involves a multi-stage pipeline that combines reinforcement learning (RL) with supervised fine-tuning (SFT).
- OpenAI o1 also leverages chain-of-thought reasoning, enabling it to decompose problems systematically and explore multiple solution paths.
- Both DeepSeek-R1 and OpenAI o1 utilize chain-of-thought (CoT) reasoning as a core element of their architecture and training.
- DeepSeek-R1 dominates with 97.3% accuracy on MATH-500, slightly outperforming OpenAI o1 (96.4%).
- DeepSeek-R1 offers a significantly more cost-effective solution compared to OpenAI o1.
- DeepSeek-R1 excels in cost efficiency, mathematical reasoning, and open-source flexibility, while OpenAI o1 leads in general knowledge and enterprise integration.
- Developers and researchers should choose based on their priorities.
- Try DeepSeek-R1’s API (10K free tokens) or explore OpenAI o1’s playground to test these models firsthand.
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