DeepSeek-R1 In a number of benchmarks, the most recent LLM from the Chinese AI lab DeepSeek has outperformed OpenAI-o1.
DeepSeek, a Chinese AI lab that just released DeepSeek-V3, is back with DeepSeek-R1, another robust reasoning large language model. In tasks like algebra, coding, and general knowledge, the new model performs on par with OpenAI’s frontier model o1, thanks to its similar mixture-of-experts architecture. According to reports, the DeepSeek-R1 is 90–95% less expensive than the O1.
You might think of DeepSeek-R1 as an AI that, like humans, can reason through various issues in addition to providing answers to your inquiries. The Chinese AI business DeepSeek, which created the new open-source reasoning model, gained notoriety earlier this month for its very potent, free, and open-source DeepSeek-V3 AI model, which beat models from Meta and OpenAI at a fraction of their price.
What is DeepSeek-R1 :
DeepSeek’s latest AI model is a cutting-edge reasoning model intended to improve AI systems’ analytical and problem-solving skills. DeepSeek-R1-Zero and DeepSeek-R1 are the two main versions of the new model, according to the research article. Without any supervised fine-tuning, the DeepSeek-R1-Zero version is trained solely through reinforcement learning (RL). The R1-Zero lays the groundwork for the DeepSeek-R1. It uses multi-stage RL and a cold-start phase with carefully selected data to guarantee improved readability and reasoning skills.
How this model perform:
Across benchmarks, the DeepSeek-R1 has demonstrated some impressive performance. The model achieved 79.8% (Passs@1) in mathematics (AIME 2024), which is similar to OpenAI’s o1. The DeepSeek-R1 model outperformed the majority of the benchmarks with an accuracy of 93% on the MATH-500, another mathematics benchmark. The model achieved a rank in the 96.3rd percentile of human participants on Codeforces, a coding benchmark. This further illustrates the model’s expert-level coding skills. Benchmarks like MMLU and GPQA Diamond, DeepSeek-R1 achieved accuracy scores of 90.8% and 71.5% on General Knowledge, respectively. DeepSeek-R1 achieved an 87.6% win rate on the benchmark AlpacaEval 2.0, which evaluates the writing and question-answering abilities of AI models.
It’s Uses :
Given DeepSeek-R1’s ability to solve intricate mathematics and reasoning problems, the AI model is probably going to be a fantastic addition to advanced education or tutoring programs. Given its excellent coding benchmarks, it can be used for software development because it is very good at both code creation and debugging. The model can be useful in research because of its high long-context awareness and question-answering abilities.