Why Is DeepSeek Giving Away the Best AI Model for Free?

The DeepSeek R1 Model

DeepSeek recently released an AI model called R1 that competes directly with OpenAI's O1 — a reasoning model that "takes more time to think before responding." The R1 comes with an MIT Open License, which permits free use, modification, and distribution with attribution required.

The DeepSeek V3 mixture-of-experts model reportedly delivers performance similar to OpenAI's O1. Some skepticism exists regarding real-world effectiveness, particularly given reports that parts of DeepSeek's model were trained using responses from ChatGPT, raising concerns about originality and reliability.

The Strategic Logic Behind Free Distribution

DeepSeek's free model encourages mass adoption across the industry. This widespread adoption enables early bug detection and community contributions to enhance the model. Over time, this strategy could shift developer and company resources away from paid APIs and models, potentially straining the finances of larger, closed-source competitors and reducing competitive intensity in the AI market.

Revenue Model: API Pricing

Rather than profiting from the free model itself, DeepSeek monetizes through affordable APIs. For startups needing scalable AI infrastructure without expensive on-premises hardware, these APIs offer a compelling alternative. The APIs are 90–95% more affordable than OpenAI's, making them attractive as companies scale.

The Critical Barriers: Trust and Privacy

Despite cost advantages, significant obstacles remain for enterprise adoption. DeepSeek's privacy policy indicates that user data may be stored on servers in China, raising compliance concerns around GDPR and other data protection regulations. Additionally, the policy grants DeepSeek an "unconditional, irrevocable license" to use and modify user inputs and outputs — a potential dealbreaker for organizations with strict data protection requirements.

The cost advantage is real. The trust problem is also real. Which one wins will depend on how much risk enterprises are willing to absorb to save money on inference.