Kimi K2 Thinking is a reasoning-focused large language model developed by the Chinese AI laboratory Moonshot AI. Released as an evolution of the Kimi K2 series, the model is specifically optimized for complex problem-solving through extended test-time reasoning. It utilizes a large-scale reinforcement learning (RL) framework to develop internal chain-of-thought processes, allowing the model to explore multiple reasoning paths for logic, mathematics, and programming tasks.
The model is built on a Mixture-of-Experts (MoE) architecture, featuring a total of 1 trillion parameters with 32 billion activated per token. It was pre-trained on a dataset of 15.5 trillion tokens using the MuonClip optimizer, a technique designed to maintain training stability at massive scales. Kimi K2 Thinking supports an expanded context window of up to 256,000 tokens, enabling it to process and maintain coherence across long documents and multi-step reasoning trajectories.
A primary feature of the model is its agentic intelligence, which allows it to autonomously interact with external tools. Kimi K2 Thinking can execute between 200 and 300 sequential tool calls—such as web searches, code execution, or database queries—to verify information and solve open-ended research problems. Its performance benchmarks show competitive results in expert-level reasoning and software engineering tasks, frequently compared to high-tier proprietary reasoning systems.