Kimi K2 by Moonshot AI is a new 1T parameter open-source MoE model (32B active). It delivers state-of-the-art performance on coding, reasoning, and agentic tasks. Base and Instruct models are now available.
The 1T parameter open model for agentic intelligence
Kimi K2 by Moonshot AI is a new 1T parameter open-source MoE model (32B active). It delivers state-of-the-art performance on coding, reasoning, and agentic tasks. Base and Instruct models are now available.
Hi everyone! A 1T parameter open-source base model... with benchmark results this impressive. I really hope the delay of OpenAI's next open model isn't because they saw Kimi K2 coming🤔 But regardless, I know the Moonshot team has been putting in the work for this moment, and they absolutely deserve it. They've given the world an incredible open-source model. As far as I know, many dev communities are already incredibly impressed with its agentic coding capabilities. A heads-up though: given this
Kimi K2's impressive performance in coding and agentic tasks is a great breakthrough for the open - source AI community! For developers who want to fine - tune Kimi K2 for specific applications, are there any detailed documentation or tutorials available to guide them through the process?
Incredible work, Moonshot team! A 1T parameter MoE model stepping into the arena feels like a true dharma-yuddha of innovation. Kimi K2’s leap in coding and reasoning benchmarks shows not just scale, but thoughtful engineering. May this open-source offering inspire both progress and ethical wisdom as we chart the next frontier of agentic AI. Excited to see how the community builds upon this luminous foundation! 🚀
this is awesome! specially for less price sensitive companies that care about both performance and privacy
It tried it yesterday and I was impressed with the deep research product. It was visually better than the deep research work product from other LLMs and it was more comprehensive. I wish that I had more than 5 deep research reports but I was impressed regardless. If you haven’t tried it for deep research, I would recommend that you give it a try.
Categories come from the product's launch tags. Most products appear in 2-3 categories. The primary category is listed first.
The scores reflect launch-period engagement. Historical data is preserved and doesn't change retroactively. The build date at the bottom shows when the index was last refreshed.
Check the similar products section on this page, or browse the category pages linked in the tags above. Each category page shows all products for a given year, sorted by engagement.
A measure of community engagement at launch. Higher means more people noticed and interacted with the product. It's a traction signal, not a quality rating.