Earlier this year, Chinese startup DeepSeek made headlines by offering its free AI model DeepSeek-R1 that claimed to match ChatGPT-level performance. Now, Moonshot AI, another Chinese startup, has launched its Kimi K2 Reasoning model, claiming it outperforms OpenAI’s GPT-5 and Anthropic’s Claude Sonnet 4.5 on key benchmarks. Kimi K2 surpasses both GPT-5 and Claude 4.5 on BrowseComp and Seal-O. These benchmarks are used to evaluate the reasoning and browsing abilities of AI agents. Kimi K2, however, trails against the two when tested for coding on some benchmarks. The open-source model is free for users versus ChatGPT Plus and Claude, which charge around $20 per month for premium tiers.
Kimi K2 is a large language model (LLM) trained on around 1 trillion parameters but is designed to use them efficiently. It has a Mixture-of-Experts design, which means only a certain number of parameters are used to process and respond to prompts. Kimi K2 uses only 32 billion activated parameters at a time, due to which it is fast but cheaper to run. On the other hand, dense models like GPT‑5 and Claude Sonnet 4.5 use far more parameters to process prompts, which improves peak performance but usually increases computation, latency, and cost.
After DeepSeek, Alibaba-backed Moonshot is another China-based AI startup that has developed its large language model for far less than their U.S. competitors. According to CNBC, Moonshot trained Kimi K2 for about $4.6 million. Unlike other popular large language models, Kimi K2 isn’t mainstream yet, but there are multiple ways through which you can access it.
Here’s how you can use the Kimi K2 AI model
For general users, the easiest way to access Kimi K2 is through the official chat interface at Kimi.com. You will have to log in to access the model. It offers unlimited usage with no token or time limits. Apart from the official website, you can access the model through Hugging Face platform, which lets users try new open-source AI models and applications. The “Kimi K2 Instruct” space lets users try prompts and receive responses directly in the browser. You can create a Hugging Face account or sign in using an existing one to start using it. Since the platform runs on shared infrastructure, you may find the responses slow. There are some advanced options as well. If you’re comfortable with coding, OpenRouter offers a developer-friendly way to access this model. You will have to sign up at openrouter.ai and create an API key from your dashboard.
For those looking for more control, self-hosting is another option through which you can run the model on your own hardware. For this, download Kimi K2’s model files from Hugging Face and run it on your own hardware using inference tools like vLLM or SGLang. The advantage of self-hosting is that you get better performance from the model with faster responses, and your data remains private as well. However, it’s a demanding and costly process: You will require powerful GPUs, large RAM, and plenty of storage to handle a heavy model such as Kimi K2. This makes the free web interface a far more practical and affordable option for general users who want to test Moonshot’s Kimi K2 model without the headache of investing in expensive hardware or putting time and effort into complicated processes.
