Kimi K2.5 (Fully Tested): An Open Weights Model beats OPUS 4.5?

[music] Hi, welcome to another video. So, Kimmy is back with another update and this one is pretty massive. They have just launched Kim K 2.5 which they are calling the most powerful open-source model to date. Now, I know you've heard me hype up KI models before, and I was really impressed with K2 and K2.1, but this one takes things to another level. Let me tell you why. So, first of all, this is still the same trillion parameter mixture of experts architecture that we saw in K2 with 32 billion parameters activated at any given time. But what's new is that this is now a native multimodal model. Yes, they finally added vision. They've trained it on about 15 trillion mixed visual and text tokens, which is massive. This means it can now see images, understand videos, and do things like generate code from UI designs or video workflows. They are calling this feature coding with vision and it's pretty great. You can literally show it a website design and it'll generate the code for you or give it a video of a workflow and it'll understand what's happening and write code based on that. The model is also really good at front-end development now with interactive layouts and scroll triggered animations and all that stuff. But the biggest feature here is the agent swarm capability. So they have this thing called a self-directed agent swarm paradigm where the model can basically spin up to 100 sub aents that execute tasks in parallel. It can handle up to 1,500 tool calls in a single session. And they claim it reduces execution time by 4.5 times compared to a single agent setup. They've trained this using something called parallel agent reinforcement learning or parl which is pretty cool from a technical standpoint. This makes it really powerful for complex agentic tasks. Like if you have a really complicated task that would normally take forever with a single agent going step by step, this model can split it up into smaller parallel tasks and get it done way faster. They've also improved the office productivity capabilities. So, it can now handle things like word annotations, financial models with pivot tables, latex equations, and documents with over 10,000 words. They claim about 59% and 24% improvements over K2 thinking on their internal benchmarks for these tasks.