Deepseek claims its reasoning-focused ai model can outperform Openai’s O1

Deepsek-R1, a reasoning-focused Artificial Intelligence (AI) Model by the chinese firm deepsek, was released on monday. This is the full version of the open source ai model, which Arrives two months after its preview version was released. The open-source ai model is available to download, and can also be used as a plug -nd-Play Application Programming Interface (API). The Chinese Ai Firm Claimed that Deepsek-R1 Was Able to Outperform OPENAI’s O1 Model in Several Benchmarks for Mathematics, Coding, Coding, and Reasoning-Based Tasks.

Deepseek-R1 AI Models Cost Up to 95 Percent Less Than Openai’s O1

There are two variants in the latest series-Deepsek-R1 and Deepsek-R1-Zero. Both have been disturbed from another large language model (lLM) Developed by the AI ​​FIMM, Dubbed Deepsek v3. The new AI models are based on mixture-of-axperts (moe) Architecture, where Several Smaller Models are Paired Together to Improve the Efficiency and Capability Model.

The Deepsek-R1 AI models are currently available to download via its hugging face ListingThe model come with an mit license that allows both academic and commercial usage. Thos, who do not intend to run the llm locally, can opt for the model api INTEADThe company announced the infection pricing of the model, highlighting that these cost 90-95 percent less less than openai’s o1.

Currently, The Deepseek-R1 API Comes with an input price of $ 0.14 (roughly Rs. 12.10) per million tokens and the output price is set at $ 2.19 (roughly Rs. 189.50) Per Million tokens. In comparison, Openai’s O1 API Costs $ 7.5 (Roughly Rs. 649) per million input tokens and $ 60 (Roughly Rs. 5,190) Per Million Output Tokens.

Not only does the Deepseek-R1 Cost Less, but the company also claims that it offers higher performance than the Openai counterpart. Based on internal testing, the AI ​​firm stated that Deepseek-R1 outperformed O1 in the American Invitation Invitational Mathematics Examination (AIME), Math-500, and Swe-Bench Benchmarks. However, the difference between the models is marginal.

Coming to the post-training, the company said that it is used reinforcement learning (RL) to the base model without any supervised fin-tuning (SFT). This method, also know as pure rl, allows more freedom to the model when Solving Complex Problems Using The Chain-Of-Thought (COT) MECANISM. Deepsek claimed that this is the first open-source ai project to use pure rl to improve reasoning capability.