No BS: Kimi K2 Review
There's a lot of hype around AI so we decided to look deeper, with no BS or exaggeration 🚀 This week, we're diving into Moonshot AI's Kimi K2 model, which is being marketed as the best open-source reasoning model available today. In this post, we'll explore what Kimi K2 is, see it in action, and discuss its impact so far.
What is Kimi K2?
Kimi K2 Instruct is Moonshot AI's instruction-following model. It's available for download on Hugging Face. The model page highlights its key features.
Key Innovations
Model Size: Kimi K2 is a 1 trillion parameter Mixture of Experts (MoE) model, making it the largest open-source model released to date. This architecture is inspired by and similar to DeepSeek V3, showcasing how open-source development builds upon itself for better results.
MuonClip Optimizer: Moonshot AI claims to have developed a new optimizer called "Muon clip." The idea is that there's a finite supply of high-quality tokens that we can use to power our LLM training, akin to the finite supply of fossil fuels used to power our economies. This optimizer supposedly allows for more efficient training, leading to better results with a fixed amount of tokens.
Agentic Coding and Tool Usage: Kimi K2 is designed to excel at agentic coding, reasoning, and using tools. This means it should effectively utilize Model Context Protocol (MCP) servers, whether built with Gradio or other frameworks, to complete tasks.
Benchmarks
Moonshot AI has provided benchmarks comparing Kimi K2 against DeepSeek, Qwen, and some proprietary models. The short version is that Kimi K2 outperforms all other open-source models and OpenAI's GPT-4.1, and is nearly on par with Claude 4 on SWE-bench benchmarks. It's also touted as the best on Live Code Bench v6, indicating its strength in agentic use cases and coding.
Kimi K2 in Action
Let's see Kimi K2 perform two agentic coding tasks using applications hosted on Hugging Face.
Example 1: Interactive World Map with Deepsite
We used Deepsite, an application for generating code with open-source models, and selected Kimi K2 Instruct. We prompted it to create an interactive world map where hovering over a country would display basic facts like capital, flag, and population, all in an old-school map aesthetic.
The model generated the map incredibly quickly, within a minute, which is very impressive for a 1 trillion parameter model. While there were some minor bugs, such as "unknown" data for capital and population and no flags, the map itself was accurate and adhered to the requested style. These data issues could easily be fixed with further iteration.
Example 2: Website Redesign with AnyCoder
Next, we tried AnyCoder, another Hugging Face-hosted application excellent for redesigning websites. We used Kimi K2 and asked it to redesign my personal website with a cyberpunk aesthetic.
The model's generation was quick and the result was impressive, fitting the cyberpunk aesthetic well. Comparing it to the original website, the redesign was a significant improvement. These examples demonstrate Kimi K2's proficiency in generating code from simple prompts, with the ability to iterate further on the results.
Kimi K2's Impact So Far
While the benchmarks and practical results for Kimi K2 are strong, its reception has been somewhat lukewarm. Looking at Hugging Face download counts, DeepSeek V3 had over 400,000 downloads in the last month, whereas Kimi K2 had less than 100,000.This significant gap indicates that Kimi K2 hasn't been as widely adopted as one might expect.
Furthermore, most popular AI coding assistants, such as VS Code, Cursor, and Replit, have not yet added first-party support for Kimi K2. While it's possible to configure these tools to use a custom model, the lack of native support likely contributes to lower adoption. The reasons for this are not entirely clear; it could be due to the model's large size making inference more challenging, or perhaps it's simply a matter of time before the community ralies around this model.
Conclusion
In conclusion, Kimi K2 is a highly impressive model from an equally impressive AI lab. It shows great promise, particularly in agentic coding and reasoning tasks. It will be exciting to see what Moonshot AI releases next and how the landscape of open-source LLMs continues to evolve.
Correction, the Kimi K2 download counts have jumped to 200k over the last month!