Moonshot AI

Reuters reported that Moonshot confidentially filed for a Hong Kong IPO and was targeting a roughly $3 billion offering.

Moonshot closed a $3.5 billion financing round, valuing the company at $35 billion.

Moonshot published the full Kimi K3 weights and implementation resources.

Moonshot temporarily paused new Kimi subscriptions as demand for K3 strained available capacity.

Kimi K3 launched with native vision, agentic capabilities and a one-million-token context window.

Moonshot introduced Kimi K3, its 2.8T-parameter native multimodal frontier model family.

Moonshot previewed an intermediate K2-generation model before Kimi K3.

Kimi added agentic presentation and slide creation capabilities.

Kimi expanded its workspace features for producing and editing work artifacts.

Moonshot documented Kimi Researcher as a deep-research workflow within its agent suite.

Kimi expanded its multi-agent swarm system to coordinate parallel specialist agents.

Moonshot released Kimi K2.6 with improved agent capabilities and published model weights.

Moonshot published its distributed model-checkpointing engine.

Moonshot published optimized kernels for Kimi Delta Attention.

Moonshot released its expert-parallel communication library for large MoE training and inference.

Moonshot published Mixture-of-Block Attention code and research for efficient long-context modeling.

Moonshot opened public beta access to Kimi Claw, a persistent cloud agent workspace.

Moonshot released Kimi K2.5, adding native multimodal capabilities and stronger agentic coding.

Moonshot completed a reported $500 million financing at a $4.3 billion valuation.

Moonshot released the reasoning-focused K2 Thinking model for long-horizon agentic tasks.

Moonshot released its open-source Kimi coding agent and command-line interface.

Moonshot published a 48B Kimi Linear checkpoint.

Moonshot introduced Kimi Linear and Kimi Delta Attention as a hybrid linear-attention approach.

Kimi added the OK Computer agent mode for executing computer-oriented tasks.

Moonshot released a K2 checkpoint with improved coding, instruction following and long-context performance.

Moonshot launched Kimi Agent alongside the updated K2 model experience.

Moonshot published the post-trained Kimi K2 Instruct checkpoint for agentic and coding tasks.

Moonshot published the base pretrained Kimi K2 checkpoint.

Moonshot introduced Kimi K2, a one-trillion-parameter mixture-of-experts model with 32B active parameters.

Moonshot released a 72B software-engineering model optimized using reinforcement learning.

Moonshot published a seven-billion-parameter formal theorem-proving model.

Moonshot published the seven-billion-parameter Kimi-Audio checkpoint.

Moonshot introduced an open audio-language model family covering audio understanding and generation.

Moonshot published the reasoning-oriented Kimi-VL A3B Thinking checkpoint.

Moonshot published the instruction-tuned Kimi-VL A3B checkpoint.

Moonshot introduced Kimi-VL, an open-weight mixture-of-experts vision-language family.

Moonshot released a preview checkpoint for formal mathematical theorem proving.

Moonshot introduced the Kimina-Prover line of open models for formal theorem proving.

Moonshot released Moonlight, a 16B MoE model used to evaluate Muon optimization at scale.

Moonshot released Kimi k1.5, a multimodal reasoning model trained with reinforcement learning.

Moonshot introduced its open k-series reasoning work through the Kimi k1.5 release.

Moonshot described k1 as a long-context reinforcement-learning reasoning system.

Moonshot disclosed k0-math as an experimental reinforcement-learning model for mathematical reasoning.

Moonshot raised a reported $300 million round that valued the company at $3.3 billion.

Kimi opened a beta supporting up to two million Chinese characters of context.

Moonshot released Mooncake, a KV-cache-centric architecture for disaggregated large-model serving.

Moonshot began publishing open systems research for efficient long-context model training and serving.

Moonshot raised about $1 billion in a financing round led by Alibaba, reaching a reported $2.5 billion valuation.

Moonshot expanded Kimi from the web to mobile applications.

Moonshot exposed a 128K-context text model through its API.

Moonshot exposed a 32K-context text model through its API.

Moonshot introduced developer API access to its language models.

Moonshot exposed an 8K-context text model through its API.

The web-based Kimi conversational assistant became publicly available with long-context document capabilities.

Moonshot launched Kimi as a long-context AI assistant for the Chinese market.

Moonshot AI was founded in Beijing by Yang Zhilin, Zhou Xinyu and Wu Yuxin to develop frontier foundation models.