Kimik2
Kimi K2 is an open-source, agentic intelligence model from Moonshot AI built with a mixture-of-experts architecture for advanced reasoning, tool use, and large-context tasks. It targets researchers, developers, and teams needing high-performance reasoning, coding, and multi-step planning capabilities.
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Quick Overview
Best for: Education & Research
What it does
AI Agents software for decision-makers comparing workflow fit and alternatives.
Best fit
Education & Research
Pricing snapshot
Freemium from $9.9 USD (one-time)
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Kimik2
Kimi K2 is a state-of-the-art AI model designed for deep reasoning and agentic behavior. It uses a mixture-of-experts (MoE) architecture (384 experts) and is described as having 1 trillion total parameters with 32 billion activated parameters, enabling scalable, efficient inference and advanced problem solving. Kimi K2 is pre-trained on 15.5 trillion tokens and employs the MuonClip optimizer to improve token efficiency and training stability.
The project is open-source: both base and instruct variants are available for download and self-deployment via GitHub and Hugging Face. Users may access Kimi K2 through kimik2.com, Moonshot AI's platform API (platform.moonshot.ai), or by deploying the model locally if they have sufficient compute resources.
Kimi K2 is an open-source, agentic intelligence model from Moonshot AI built with a mixture-of-experts architecture for advanced reasoning, tool use, and large-context tasks. It targets researchers, developers, and teams needing high-performance reasoning, coding, and multi-step planning capabilities.
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Claim this listingKey Features
Mixture-of-Experts (MoE) Architecture
Uses a MoE design with 384 experts to enable scalable model capacity and efficient compute usage during inference and training.
Large Parameter Scale
Described as 1 trillion total parameters with 32 billion activated parameters, designed for high-capacity reasoning and knowledge representation.
Extensive Pretraining
Pre-trained on 15.5 trillion tokens to provide broad knowledge and performance across search, coding, and reasoning tasks.
MuonClip Optimizer
Employs the MuonClip optimizer to enhance token efficiency and prevent training instabilities such as logit explosions.
Agentic Capabilities & Tool Use
Built for autonomous problem-solving, tool use, planning, exploring, and executing complex multi-step tasks.
Long Context Support
Supports very long context windows (context length reported as 128K tokens) for extended multi-step reasoning and long-document tasks.
Open-Source Variants
Both base and instruct versions are publicly available for community use, fine-tuning, and local deployment.
Multilingual Performance
Performs well on multilingual benchmarks (e.g., SWE-bench Multilingual) and supports multi-language use cases.
Pricing
Open-source model weights are free to download and run locally; new users on the hosted platform receive 3 free credits.
Starter
$9.9 USD (one-time)- 400 credits, valid for 1 month
- Minimum 1 conversation per credit
- Access to all major AI models
- Ultra-fast response speed
Pro (Most Popular)
$49.9 USD (one-time)- 3000 credits, valid for 6 months
- Access to all major AI models
- Access to NanoBanana Pro
- Extended credit validity period
Ultimate
$99.9 USD (one-time)- 6000 credits, valid for 1 year
- Minimum 1 credit per conversation
- Access to all major AI models
- Access to NanoBanana Pro
Use Cases
Complex Reasoning & Research
Large-context, chain-of-thought planning and deep reasoning for academic research, analysis, and decision support.
Coding & Development Assistance
High performance on coding benchmarks and tasks, useful for code generation, debugging assistance, and developer tooling.
Autonomous Agents & Tooling
Agentic capabilities enable automation of multi-step workflows, tool orchestration, and integration with external services.
Search & Knowledge Retrieval
Enhanced search and exploration tasks via extended context handling and strong knowledge retention from large-scale pretraining.
Local & On-Prem Deployment
Open-source releases allow organizations with adequate compute to self-deploy and run models locally for privacy or cost control.
Multilingual Applications
Supports multi-language workloads for global products and services, demonstrated via multilingual benchmark performance.
Integrations
Major Language Models
Hosted offering integrates multiple major LLMs such as DeepSeek, Claude, GPT, Gemini, Qwen, and Grok for comparative or complementary use.
GitHub & Hugging Face
Model code and weights are published on GitHub and Hugging Face for download, community contributions, and self-deployment.
Moonshot AI Platform API
Official API available at platform.moonshot.ai for hosted model access and programmatic integration.
Benefits
Limitations
Frequently Asked Questions
What is the difference between Kimi-K2-Base and Kimi-K2-Instruct?
How can I access Kimi K2?
What are the system requirements for running Kimi K2 locally?
Is Kimi K2 free to use?
How does Kimi K2 compare to other AI models?
Can Kimi K2 be used for commercial purposes?
What is the context length of Kimi K2?
Does Kimi K2 support multilingual capabilities?
How was Kimi K2 trained?
Is technical support available for Kimi K2?
Getting Started
- 1 Self-Deploy: Download Kimi-K2-Base or Kimi-K2-Instruct from the project's GitHub or Hugging Face and run locally if you have sufficient compute.
- 2 Use Official API: Integrate via Moonshot AI's platform API at platform.moonshot.ai for hosted access and programmatic integration.
- 3 Use on kimik2.com: Try the hosted interface on kimik2.com (includes chat, NanoBanana Pro, image generator, and dashboard features).
Support
Technical support and inquiries: [email protected]
docs
Model code, weights, and documentation on GitHub and Hugging Face (links available on the project site).
platform
API access and developer integration via Moonshot AI platform at platform.moonshot.ai.
API
Official API available at platform.moonshot.ai (documentation and integration details provided there).
Not available
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