Claude Killer? My Review on Kimi K2 Thinking After Days of Testing

A week ago, the Chinese company Moonshot AI decided to introduce new competition by releasing the Kimik K2 thinking model, claiming it to be the best open-source thinking model out there. Priced significantly lower than high-erforming models like Sonnet 4.5 and GPT5, its performance is already on par with the best models from OpenAI and Anthropic. So much so that people are already comparing it to GPT5 with some even calling it the next claude. But is it really? Hey everyone, if you're new here, this is AI Labs and welcome to the first episode of Debunked, a series where we actually take AI tools and AI models, strip away the marketing hype, and show what they can actually do with real testing and honest results. We spent the past 4 days testing this model across multiple uses, and put together this video to see if it's truly worth the hype. So, let's get right into it. You can either test this model on the web interface which offers full integration of the comprehensive tool set or use it in your code editor as a coding agent through a provider like Klein. I also used Klein throughout the video for testing the capabilities of the model. The first test for the model is its UI design capability. Instead of giving it a basic UI task, I provided a componenheavy 3D model for a fashion website prototype to see how well the model performs. I gave the same prompt to Claude as well, so we could compare its performance with the current best model for coding. It took a while to complete the task, but this is the website the Kimmy model produced. The components were interactable, and I was able to explore and play around with them. All the individual products in the list were also interactable. I could change the colors of the products as well. Even though the UI had some broken points, it was a pretty impressive website considering it was produced from just one prompt. Also, right now it's just a prototype with no actual clothing items. The same website was produced by Claude and compared to Kimmy, it took significantly less time and even added a modal functionality for better viewing of each product item. So overall, in terms of UI, both models delivered comparable performance with Claude taking the edge thanks to the modal implementation which Kimmy failed to implement despite being a part of the instructions. There was one area where the Kimmy model outperformed Claude. Context efficiency. It used only 52.5K context window out of its total capacity and cost just 20 cents for the entire task even though it took longer to produce the website. On the other hand, Claude used 37K of context which is 68% of its total context window for the same prompt and it cost more than Kimmy. This means that in terms of context handling and cost efficiency, Kimmy was clearly better. Now, before we move on to the next test, here's a word from our sponsor, make.com. You can visually orchestrate AIdriven workflows, monitor them in real time, and implement changes instantly, all from one intuitive platform. Automate at speed with over 3,000 plus pre-built apps and an AI assisted noode builder. Make the complex simple by orchestrating Gen AI and LLM powered workflows. and scale with control using Make Grid, MCP, and advanced analytics that give you full visibility and precision. With Make AI agents, you can describe goals in natural language. And these agents choose the best path forward, connecting tools, handling edge cases, and adapting as your systems grow. And now, with Make's new built-in sharing feature, you can instantly publish your scenarios directly to LinkedIn, Facebook, Instagram, or even the Make community and blog straight from your dashboard. It's automation that's not only powerful, but proudly sharable. Click the link in the pin comment and start building today. The next test for the model was to assess its development abilities. And for that, I tasked Kimmy to create a 3D pinball style game. While this task also took a long time to complete, it included a simple landing page and game instructions. However, when I started a new game, it ended randomly with nothing showing on the page, and there was no way to go back to the homepage or restart from this point, no matter what key I pressed. On the same prompt, Claude was able to generate something much better. While some options weren't working, the game was somewhat playable, unlike Kimy's, and even included the sound effects from the original 3D space pinball game. Although it wasn't a fully refined game with the same dynamics, this time Claude clearly won with significantly better performance in executing the idea. I tried to iterate the prompt with Kimmy and fix the game's logic, but no matter what instructions I gave, the game stayed at the same stage and didn't progress any further. The only difference was that it was able to add sound effects on key presses. Now, that was an overview of the performance of the Kimik K2 thinking model on a completely new project. But how does it perform when tasked with adding features to an existing project? To test that, I had built a dashboard for project management and wanted to add authentication in it. I asked the Kimmy model through Klene to add Firebase authentication to my existing project and asked Claude as well on the same project. The Kimmy model took quite some time to generate the feature, but in the end it was able to produce results. When I tested it, instead of landing on the homepage, it went straight to a login signup interface. I created a new account and upon account creation I was given access to the dashboard along with all the existing functionality. I didn't receive any verification email but aside from that the feature was working as intended. I was also able to sign out successfully. So overall it was a well-implemented project by Kimmy. When I tested it on Claude we got a similar login signup landing page. I signed in with my account and overall the integration felt seamless. In the new feature integration part, Claude won again since Kimy's generated login signup page didn't follow the UI theme of the website and used colors that didn't match the rest of the site. But still, the functionality was correct. So, after all the tests I ran, Kimmy didn't quite live up to the hype. It was slower than expected, had issues maintaining the design consistencies, and working on complex tasks. It's still far away from being the new GPT5 and Sonnet 4.5 people are claiming it to be. That said, given how cost-effective it is, the performance is genuinely impressive. Moonshot clearly has potential, and if they keep pushing forward, they could very well become the next company to shake up the AI coding space. That brings us to the end of this video. If you'd like to support the channel and help us keep making videos like this, you can do so by using the super thanks button below. As always, thank you for watching and I'll see you in the next