Chinese AI lab DeepSeek has unveiled DeepSeek-R1, a reasoning large language model. The model shares the mixture-of-experts architecture seen in the lab’s earlier releases and achieves performance levels comparable to OpenAI’s o1 in mathematics, coding, and general knowledge tasks. DeepSeek-R1 is said to be 90–95% more cost-efficient than o1.

DeepSeek-R1 approaches problem-solving in a way that mimics human reasoning. Developed by DeepSeek, which recently launched the open-source DeepSeek-V3, this new model builds on the lab’s reputation for creating high-performance AI systems at lower costs than competitors like Meta and OpenAI.

What is DeepSeek-R1?

DeepSeek-R1 is a reasoning model aimed at improving analytical and problem-solving abilities in AI. It includes two versions: DeepSeek-R1-Zero and DeepSeek-R1. The R1-Zero version relies entirely on reinforcement learning without supervised fine-tuning, while the R1 version adds a cold-start phase, curated data, and multi-stage reinforcement learning for enhanced reasoning and readability.

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Performance Highlights

DeepSeek-R1 has demonstrated strong results across benchmarks. On the AIME 2024 mathematics test, it achieved a 79.8% Pass@1 score, comparable to OpenAI’s o1. In another math benchmark, MATH-500, the model reached 93% accuracy. For coding, it ranked in the 96.3rd percentile on Codeforces, showcasing expert-level coding skills. General knowledge benchmarks MMLU and GPQA Diamond saw the model scoring 90.8% and 71.5% respectively. On AlpacaEval 2.0, a test of writing and question-answering abilities, it achieved an 87.6% win rate.

Potential Applications

With its capacity for solving complex mathematical and reasoning problems, DeepSeek-R1 could be valuable in education and tutoring systems. Its coding abilities make it a potential tool for software development, including tasks like code generation and debugging. Its proficiency in long-context understanding and question-answering also positions it as a useful resource in research.

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