Kimi K2
Kimi K2 is a state-of-the-art Mixture-of-Experts large language model with 32 billion activated parameters and 1 trillion total parameters, optimized for agentic tasks. It excels in frontier knowledge, math, coding, and tool use, enabling it to not just answer but act autonomously, making advanced agentic intelligence accessible for researchers and developers.
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Quick Overview
Best for: Business Operations
What it does
AI software for decision-makers comparing workflow fit and alternatives.
Best fit
Business Operations
Pricing snapshot
Free
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Kimi K2
Kimi K2 is a cutting-edge Mixture-of-Experts model designed to deliver state-of-the-art performance in knowledge-intensive tasks, mathematics, and coding among non-thinking models. It features 32 billion activated parameters and a total of 1 trillion parameters, making it one of the largest and most powerful models available. Beyond answering queries, Kimi K2 is meticulously optimized for agentic tasks, meaning it can autonomously understand and execute complex workflows by interacting with tools and environments. The model is open-sourced with two main variants: Kimi-K2-Base, which offers full control for fine-tuning and custom solutions, and Kimi-K2-Instruct, a post-trained model optimized for general-purpose chat and reflexive agentic experiences. Kimi K2 is designed to be accessible to researchers, developers, and builders aiming to create advanced AI applications that require both reasoning and action capabilities.
Kimi K2 is a 1 trillion parameter open-source Mixture of Experts (MoE) model delivering state-of-the-art performance on coding, reasoning, and agentic tasks. It offers both base and instruct models for advanced AI applications.
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Claim this listingKey Features
Mixture-of-Experts Architecture
Utilizes a large-scale MoE model with 32 billion activated parameters and 1 trillion total parameters for efficient and powerful performance.
Agentic Intelligence
Optimized for agentic tasks, enabling the model to autonomously understand and use tools to complete complex workflows without manual scripting.
Two Model Variants
Includes Kimi-K2-Base for researchers needing fine-tuning capabilities and Kimi-K2-Instruct for drop-in, general-purpose chat and agentic applications.
Advanced Tool Use Learning
Trained on large-scale agentic data synthesis with diverse tool sets and multi-turn interactions, enabling sophisticated tool use.
General Reinforcement Learning
Incorporates a self-judging critic mechanism for scalable, rubric-based feedback on both verifiable and non-verifiable tasks.
MuonClip Optimizer
Employs a novel optimizer that stabilizes training by controlling attention logits, enabling stable large-scale training on 15.5 trillion tokens.
Open Source and Accessible
Open-sourced with deployment instructions and compatible with popular inference engines like vLLM, SGLang, KTransformers, and TensorRT-LLM.
API Compatibility
Offers an OpenAI/Anthropic compatible API interface for easy integration and building of agent applications.
Pricing
Kimi K2 is currently available for free use on the Kimi web and mobile platforms, with ongoing development of additional features.
Use Cases
Automated Data Analysis
Kimi K2 can autonomously analyze complex datasets, generate statistical evidence, and create rich visualizations, as demonstrated in the remote-work salary interaction effect analysis.
Interactive Webpage Generation
Generates professional, interactive web content including data visualizations and personalized simulators based on user input.
Agentic Task Automation
Automatically understands and executes multi-step workflows by orchestrating multiple tools and commands without requiring explicit scripting.
Software Development Automation
Manages code rendering, testing, debugging, and iterative improvements, exemplified by automating Minecraft development in JavaScript.
Research and Experiment Analysis
Extracts insights from language model experiments using tools like Weights & Biases and generates polished analysis reports.
Codebase Refactoring and Benchmarking
Systematically refactors projects (e.g., converting Flask to Rust) and runs performance benchmarks to ensure robust results.
Integrations
OpenAI/Anthropic Compatible API
Allows easy adaptation of existing applications to use Kimi K2 with familiar API interfaces.
Inference Engines
Supports deployment on vLLM, SGLang, KTransformers, and TensorRT-LLM for flexible serving options.
Weights & Biases (wandb)
Used for data reading and extracting insights from language model experiments.
Model Context Protocol (MCP) Tools
Integrates with real and synthetic MCP tools for large-scale agentic data synthesis and tool use learning.
Benefits
Limitations
Frequently Asked Questions
What is the difference between Kimi-K2-Base and Kimi-K2-Instruct?
How can I deploy Kimi K2 on my own infrastructure?
Does Kimi K2 support vision features?
Is there an API available for Kimi K2?
What are the main limitations of Kimi K2?
Getting Started
- 1 Visit https://www.kimi.com to try Kimi K2 for free on web and mobile platforms.
- 2 Explore the Researcher feature for an early look at agentic capabilities (note: vision features not yet supported).
- 3 Use the Kimi Platform API at https://platform.moonshot.ai for integrating Kimi K2 into your applications.
- 4 Deploy Kimi K2 on your own infrastructure using recommended inference engines such as vLLM, SGLang, KTransformers, or TensorRT-LLM.
- 5 Refer to the GitHub repository (https://github.com/MoonshotAI/Kimi-K2?tab=readme-ov-file#4-deployment) for detailed deployment instructions.
- 6 Stay tuned for upcoming features including advanced thinking and visual understanding capabilities.
Support
Documentation
Comprehensive deployment and usage instructions available on the GitHub repository.
Website
Access to Kimi K2 and related resources at https://www.kimi.com and https://platform.moonshot.ai.
Community
Community support and updates through GitHub and platform channels.
API
API documentation and integration details are available at https://platform.moonshot.ai.
Not specified in the provided content.
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