Remember the flood of open-source Chinese models that disrupted the GenAI industry earlier this year? While DeepSeek took most of the headlines, Kimi K1.5 was one of the prominent names in the list. And the model was quite cool. Learn all about it in our detailed blog on Kim k1.5. 7 months later, Moonshot is back with its new agentic open-source model: Kimi K2. It delivers a cutting-edge performance with its 1 trillion total parameters and 32 billion activated Mixture-of-Experts (MoE) architecture. Let’s learn more about it!

Table of contents

• What is Kimi K2?
• Benchmark and Performance
• How Kimi K2 Learns (Pre-training and Post-training)?
• How Kimi K2 Learns to Use Tools (Like a Developer)?
• How to Access?
  • Try Kimi K2 Online
  • Use Kimi K2 via API
  • Run Kimi K2 Locally or On Your Own Server
• Task 1: Research and Create a Report
• Task 2: Book Flight Tickets
• Conclusion
• Frequently Asked Questions

What is Kimi K2?

As mentioned above, Kimi K2 is a powerful new open-source model built to handle complex tasks. With its advanced architecture and smart decision-making abilities, it doesn’t just respond to prompts, it takes real action. From coding to data analysis, it’s designed to make high-level AI tools available to everyone.

It comes in 2 variations:

• Kimi-K2-Base: A robust foundation model ideal for researchers and developers who want full customization and fine-tuning capabilities.
• Kimi-K2-Instruct: A post-trained, instruction-following model for general-purpose chat and reflex-grade agentic tasks.

Benchmark and Performance

Kimi K2 delivers state-of-the-art and open-source leading results in the following benchmarks:

• SWE-bench Verified: 65.8% single-attempt accuracy
• SWE-bench Multilingual: 47.3% (best among tested models)
• LiveCodeBench v6: 53.7%
• OJBench: 27.1%
• Tau2-bench (weighted average): 66.1%
• AceBench (en): 80.1%
• AIME 2025: 49.5%
• GPQA-Diamond: 75.1%

These scores highlight Kimi K2’s strength in agentic coding, tool use, and complex STEM tasks, frequently outperforming or matching proprietary models such as Claude and GPT-4.

Don’t know how these benchmarks work? Checkout our detailed guide on top LLM Benchmarks.

How Kimi K2 Learns (Pre-training and Post-training)?

Imagine teaching a robot by feeding it a giant library of books. This is called pre-training. Kimi K2 read 15.5 trillion tokens, basically the internet many times over. It tries to guess the next word, checks if it was right, and improves over time. The more it reads, the better it gets.

But there’s a catch, human-written data is limited. So instead of only reading, Kimi K2 starts?doing?things on its own to learn. This is called post-training. It learns from experiences it creates for itself, like trying out tools or solving tasks and judging how well it did.

To make sure it doesn’t get confused while learning huge amounts of data, Kimi K2 uses a special optimizer called MuonClip. Think of it like a training coach that keeps everything balanced. Other models sometimes “blow up” during training, meaning their internal math gets too extreme. MuonClip prevents this by gently controlling the parts that are likely to go out of control (query/key matrices), keeping everything smooth and reliable.

How Kimi K2 Learns to Use Tools (Like a Developer)?

Let’s say you want your AI assistant to book a flight or write code. To do that, it needs to know how to use tools. Kimi K2 learns this through simulation.

Here’s how it works, step by step:

• Start with a goal (like answering a question).
• Create a domain (topic or environment).
• Add real or simulated tools.
• Build hundreds of agents that try to complete tasks using the tools.
• Simulate users who interact with these agents.
• A smart AI judge checks their work and filters out the bad ones.

This helps Kimi K2 practice thousands of different tool-use scenarios before ever helping a real user.

Kimi K2 also uses reinforcement learning. It’s like learning to play a game where you get points for good moves. For tasks like math or coding, it can check whether it’s right. But for tasks like writing or helping users, there’s no “correct” answer. So Kimi K2 acts as its own reviewer. It judges its own performance, gives itself feedback, and keeps learning from that. It also uses clearly correct tasks (like math) to improve how well it can score the fuzzier ones.

How to Access?

You can access?Kimi K2?in several ways, depending on whether you’re a casual user, a developer, or running your own infrastructure:

Try Kimi K2 Online

• Website: https://www.kimi.com/
• Choose?Kimi K2?from the model selector (usually shown as “Kimi-K2” or “K2”)
• No installation required; just start chatting or uploading tasks

Use Kimi K2 via API

• Visit the?Moonshot Platform:?https://platform.moonshot.cn
• The API is?compatible with OpenAI/Anthropic formats
• Supports?tool use?and?agentic workflows
• Includes endpoints for chat, file tools, and agent orchestration

Run Kimi K2 Locally or On Your Own Server

• Model Weights: Open-sourced on?GitHub?and/or Hugging Face (soon)
• Recommended inference engines:
  • vLLM
  • SGLang
  • KTransformers
  • TensorRT-LLM

This is ideal if you’re fine-tuning, doing research, or scaling in-house.

In the next section, I am going to do some tasks using this model and give you my take on the same.

Task 1: Research and Create a Report

Prompt: “Based on the latest trends in Generative AI and Agentic AI, give me a report of which skills will be relevant in 2025 for working professionals across marketing, banking, social media, product management, software development, content, HR and manufacturing.“

Output:

Observation:

The research part was well done, and the language used in the report felt natural, with a human touch to the overall conversation. However, it struggled to generate the output in PDF format.

Task 2: Book Flight Tickets

Prompt: “I’m based in Delhi and will be traveling for the DataHack Summit this August. Could you share what to expect at the conference, and also help me find the cheapest flight options?“

Output:

Observation:

The event details were accurate, and the hotel and flight information provided was spot on. It was incredibly helpful for planning the trip. The best part? It did all of this completely free of cost.

Also Read: Top 5 General AI Agents that Can Make Your Life Easy!

Conclusion

’m impressed with the way Kimi K2 responds to queries—it almost feels like communicating with a human. What sets it apart is that most of its advanced features are available free of cost, unlike other platforms like Manus, Genspark, or OpenAI’s Operator that require paid subscriptions. The responses are quick, and its ability to handle diverse tasks shows that it’s truly a powerful agentic model. Combining large-scale training, tool use, and adaptive intelligence, it paves the way for general AI systems that think, act, and adapt.

Whether you’re building a coding agent, doing real-world data science, or crafting the next-gen interface, Kimi K2 gives you the power to create.

Try it today and let me know your thoughts in the comments below.

Frequently Asked Questions

A. Kimi K2 stands out for its agentic capabilities, meaning it can take actions using tools, not just generate text. It’s also one of the few models with a Mixture-of-Experts architecture and open-source availability.

Yes, many of Kimi K2’s features are available for free through its website and app, unlike other platforms that charge for similar capabilities.

Developers can integrate Kimi K2 into their apps using the API, run it on local hardware, or fine-tune the base model for custom tasks. It’s compatible with major inference engines like vLLM and TensorRT-LLM.

Absolutely. Kimi K2 can execute shell commands, edit and deploy code, build interactive websites, and even work with game engines. It’s optimized for both tool interaction and software development.

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