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https://www.wired.com/story/deepseek-china-model-ai/ [wired.com]
https://web.archive.org/web/20250125102155/https://www.wired.com/story/deepseek-china-model-ai/ [archive.org]

On January 20, DeepSeek, a relatively unknown AI research lab from China, released an open source model that’s quickly become the talk of the town in Silicon Valley. According to a paper authored by the company, DeepSeek-R1 beats the industry’s leading models like OpenAI o1 on several math and reasoning benchmarks. In fact, on many metrics that matter—capability, cost, openness—DeepSeek is giving Western AI giants a run for their money.

https://arstechnica.com/ai/2025/01/china-is-catching-up-with-americas-best-reasoning-ai-models/ [arstechnica.com]

The releases immediately caught the attention of the AI community because most existing open-weights models—which can often be run and fine-tuned on local hardware—have lagged behind proprietary models like OpenAI's o1 in so-called reasoning benchmarks. Having these capabilities available in an MIT-licensed model that anyone can study, modify, or use commercially potentially marks a shift in what's possible with publicly available AI models.

https://github.com/deepseek-ai/DeepSeek-R1 [github.com]

We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning. With RL, DeepSeek-R1-Zero naturally emerged with numerous powerful and interesting reasoning behaviors. However, DeepSeek-R1-Zero encounters challenges such as endless repetition, poor readability, and language mixing. To address these issues and further enhance reasoning performance, we introduce DeepSeek-R1, which incorporates cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks. To support the research community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based on Llama and Qwen. DeepSeek-R1-Distill-Qwen-32B outperforms OpenAI-o1-mini across various benchmarks, achieving new state-of-the-art results for dense models.

NOTE: Before running DeepSeek-R1 series models locally, we kindly recommend reviewing the Usage Recommendation section.

Check leaderboard and compare at Chatbot Arena: https://lmarena.ai/ [lmarena.ai]

Original Submission