DeepSeek and Moonshot reportedly used GPT-6 Astra outputs for model training
Chinese AI developers DeepSeek and Moonshot AI are reportedly using outputs from OpenAI's GPT-6 Astra to train their latest models as competition in advanced artificial intelligence sharpens. The reported use of distillation adds to tensions over how quickly rival firms can replicate high-end AI capabilities at lower cost.
Highlights
- DeepSeek and Moonshot AI used OpenAI's GPT-6 Astra outputs via distillation to train DeepSeek V4.1-Flash 1017 and Moonshot K3.1 Pro models, according to The Information.
- OpenAI linked a major distillation campaign, which began in July, to individuals associated with Moonshot AI in a September 30 blog post.
- Anthropic claims new security measures cut data harvested in distillation attacks targeting Claude AI models by about 90% to 99% amid rising concerns over AI copying in U.S.-China tech competition.
Reported use of distillation in recent model releases
As reported by Reuters, citing The Information, DeepSeek and Moonshot AI relied heavily on outputs from OpenAI's GPT-6 Astra model to train their newest systems.Moonshot AI's K3.1 Pro model, released last week, and DeepSeek's upgraded V4.1-Flash 1017 model, launched on October 17, both benefited from distilling Astra, the report said. OpenAI, DeepSeek and Moonshot AI do not immediately respond to Reuters requests for comment.
In a September 30 blog post, OpenAI says it links a core part of a distillation campaign that began in July to individuals associated with Moonshot AI.
Industry pressure grows over AI copying risks
Distillation has become a flashpoint for leading AI companies as U.S.-China technology competition intensifies, because firms argue the practice lets rivals reproduce advanced capabilities more quickly and more cheaply.Companies including Anthropic, OpenAI and Google argue that the technique helps competitors narrow performance gaps without bearing the full cost of original model development. Earlier this month, Anthropic says new measures designed to prevent rivals from copying the capabilities of its Claude AI models reduce data harvested in so-called distillation attacks by about 90% to 99%.
Our earlier coverage of U.S.-China AI safety coordination highlighted how governments and major developers are weighing stronger safeguards as AI capabilities accelerate. We noted proposals for an incident-reporting channel between the two countries and discussions about using the UK as a neutral venue to coordinate international AI risk management.
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