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DeepSeek rolls out V3 AI model updates in race against OpenAI. What's New?

DeepSeek’s AI ignited a debate on whether cutting-edge platforms can be built for far less than the billions US firms are pouring into data center construction.

Published on: Mar 26, 2025, 13:18:06 IST
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Chinese artificial intelligence (AI) startup DeepSeek has released updates to its V3 model, promising to deliver better programming capabilities.

The DeepSeek logo is seen in this illustration taken on January 29, 2025. (Dado Ruvic/Reuters)
The DeepSeek logo is seen in this illustration taken on January 29, 2025. (Dado Ruvic/Reuters)

The V3-0324 update, which was originally posted on Hugging Face this week without a formal announcement, claims to address real-world challenges while also setting benchmarks for accuracy and efficiency, Bloomberg reported.

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The V3 is actually an older DeepSeek platform. DeepSeek claims that it has made significant improvements in benchmark performance in multiple metrics.

It also claims enhanced style and content quality when it comes to Chinese writing proficiency, improved multi-turn interactive rewriting, optimized translation quality and letter writing, enhanced report analysis requests with more detailed outputs, and increased accuracy in function calling, fixing issues from previous V3 versions.

The startup’s AI services have ignited a debate about whether cutting-edge platforms can be built for far less than the billions that US firms are pouring into data centre construction.

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This also highlights the company's intent to remain a step ahead of competitors, especially from Silicon Valley such as the likes of OpenAI and Google.

This comes after DeepSeek surpassed OpenAI's ChatGPT to become the most popular free app on Apple’s US app store.

DeepSeek's achievements also include the performance of the initial R1 model, which was seemingly at par with well as OpenAI’s best, but at a fraction of the cost.

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The cost part in particular stunned the industry and triggered a selloff of AI and tech-related stocks in US markets. This is because Silicon Valley's best were investing huge amounts of money into their AI projects, but were achieving only similar results.

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