Kimi K2
Kimi K2 is an advanced AI model by MoonshotAI, featuring 128K context length, superior performance in reasoning, coding, and multilingual tasks. Available as open-source models and API.
A startup from China.
Kimi K2 is an advanced AI model by MoonshotAI, featuring 128K context length, superior performance in reasoning, coding, and multilingual tasks. Available as open-source models and API.
A startup from China.
This page is designed to help you find out whether Kimi K2 is good and if it is the right choice for you.
Kimi K2 is our latest Mixture-of-Experts model with 32 billion activated parameters and 1 trillion total parameters. It achieves state-of-the-art performance in frontier knowledge, math, and coding among non-thinking models. But it goes further — meticulously optimized for agentic tasks, Kimi K2 does not just answer;
Listed in
1.open-source. 2.Open Agentic Intelligence
Give Kimi K2 your tools and describe your task. It automatically understands how to use the tools and gets the job done. You don't have to write any complex workflow for agentic applications.
1.Salary Data Analysis: Imagine using Kimi K2 to explore remote-work salaries with the Salary Data Analysis example, where 16 IPython calls generate stats, visualizations, and an interactive webpage of insights. Dive into the Stanford NLP Genealogy, and get Kimi K2 to generate an interactive site built through 5 web searches, 4 browsings, 3 clicks, 5 scrolls, 6 edits, and 2 deployments. Or plan your dream Coldplay Tour 2025 in London with Kimi K2, it crafted the plan for you through 17 seamless tool calls spanning search, calendar, Gmail, flights, Airbnb, and restaurant bookings.
2.JavaScript Minecraft: For clarity, the terminal displays only a summary of each trajectory. In every example, Kimi K2 orchestrates multiple tools and commands behind the scenes to accomplish complex objectives. For instance, Kimi K2 can automate Minecraft development in JavaScript: it manages rendering, runs and debugs test cases, captures logs on failure, and iteratively improves the code until all tests succeed. For pre/post norm analysis, Kimi K2 uses the Weights & Biases (wandb) data reader to extract insights from language model experiments and generates a polished analysis report. When converting a Flask project to Rust, Kimi K2 systematically refactors the codebase and runs performance benchmarks to ensure robust results.
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