Moonshot recently announced the release and open-sourcing of its Kimi K2 model, which has demonstrated exceptional performance in industry benchmark tests—arguably making it one of the most capable open-source AI models available today.
Until now, the Kimi series had remained proprietary and was primarily deployed within Moonshot’s own Kimi platform. At a time when competition in the AI sector is becoming increasingly fierce, Moonshot’s decision to open source such a high-performing model is both rare and commendable.
Aravind Srinivas, CEO of the AI search engine Perplexity, revealed on X/Twitter that the Kimi K2 model had performed impressively during internal evaluations. As a result, Perplexity AI plans to begin post-training based on the K2 model in the near future.
Post-training refers to refining a base model for specific applications and typically precedes fine-tuning. For Perplexity AI, whose core offering is search functionality, a post-trained K2 model could significantly enhance the quality and relevance of search results delivered to users.
While post-training and fine-tuning are less complex than full-scale model training, they are still time-consuming processes. Therefore, it is unlikely that Perplexity will adopt the K2 model immediately. Once the post-trained version is ready, a formal announcement is expected to follow.
Aravind Srinivas congratulated Moonshot on delivering such an outstanding open-source model, underscoring the remarkable strength of the open-source community. Since its release, Kimi K2 has swiftly gained traction and adoption across multiple organizations.
Notably, on OpenRouter, the Kimi K2 model has already surpassed xAI’s Grok in usage, ranking ninth overall. At least for now, user feedback from testing and deployment suggests that the K2 model is earning widespread acclaim.
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