France's Mistral hails DeepSeek's AI model
Stay tuned with 24 News HD Android App
French AI startup Mistral on Thursday hailed Chinese competitor DeepSeek's R1 model as "great" for the fast-developing sector, while announcing another new release of its own.
Mistral has in the past been hailed for offering models -- the engines that power generative AIs -- comparable with American giants at a fraction of the cost in energy and cash. DeepSeek claimed that its R1 release pulled off a similar feat, causing near panic in the US this week and battering tech stocks.
R1 is "a great and complementary piece of open-source technology," Mistral said in a statement on its website, referring to the fact that elements of DeepSeek's technology are available for other researchers to use and build on.
The French firm's own Thursday release, "Mistral Small 3", is "competitive with larger models" including Meta's Llama or Qwen, developed by Chinese ecommerce giant Alibaba, Mistral said.
It added that Small 3 was "the most efficient model of its category" and might be used for tasks ranging from customer service to detecting financial fraud or controlling robots.
The new model "complements large open-source reasoning models like the recent releases of DeepSeek, and can serve as a strong base model for making reasoning capabilities emerge," Mistral said. Like DeepSeek, Mistral offers elements of its technology as open-source tools for other developers.
The French underdog shot to prominence following its 2023 founding by Arthur Mensch, Guillaume Lample and Timothee Lacroix, becoming Europe's greatest hope of matching US heavyweights.
Mistral took in 600 million euros (626 million) in a summer fundraising round, bringing its valuation to around six billion euros.
AFP news agency signed a deal with Mistral in mid-January allowing the startup's chatbot to draw on the news agency's articles to formulate responses.
Some in the US have accused DeepSeek of simply piggybacking on the work of American AI developers like OpenAI's ChatGPT models, rather than achieving its high performance independently.