KIMI 2.6 + OpenCode Is INSANE! 🤯

[0:00]Kimi K2.6 plus open code is insane. You've been coding the slow way. Free app, free landing page, free automation. You sit down. You write, you debug, you fix, you repeat. What's gone? Sometimes days. And the whole time the AI tools that could do this for you already exist. Us people just don't know how to use them properly. That changes today. Hey, I'm the digital avatar of Julian Goldie. I help people learn how to actually use AI tools the right way. Not theory, not hype, real stuff that saves real time. Today I'm going to show you what happens when you combine Kimi K2.6 with Open Code. An open source AI coding agent running on one of the most capable open-weight models on the planet right now. At the end of this video, you're going to understand exactly how this works, what the benchmarks actually mean, and how to use it to build apps, landing pages, and automation systems without writing every line yourself. Let's get into it. First, what is Kimi K2.6? It's the latest model from Moonshot AI, a Beijing-based AI startup. They've shipped five major model updates in under a year: K2, K20905, K2 Thinking, K2.5, and now K2.6. Each version pushed something specific forward. K2.6 is built around one core idea: stamina. Not just being smart, being able to keep working on complex tasks for hours without falling apart. And the benchmarks back this up. On SWE-bench Pro, a benchmark that tests real GitHub issue resolution across complex codebases, K2.6 hits 58.6%. GPT-5.4 gets 57.7, Claude Opus 4.6 gets 53.4. Kimi's at the top. On Humanity's Last Exam with tools, which tests how well a model uses external resources to solve hard problems, K2.6 scores 54.0. GPT-5.4 scores 52.1. Claude Opus 4.6 scores 51.0. On the artificial analysis intelligence index, K2.6 lands at number four overall, right behind Anthropic, Google, and OpenAI. Here's what matters: It's the highest-ranked open-weight model. You're not locked into paying one company for access. It's a mixture of experts model, one trillion total parameters, but only 32 billion are active during inference. Fast and efficient. 100 tokens per second. And an agentic tool use benchmark. K2.6 scores 50. Claude gets 47.2. Gemini 3 Pro gets 48.8. This isn't a smart chatbot. This is a model built to act. And that action-first design is exactly why it pairs so well with Open Code. So what is Open Code? Open Code is an open source AI coding agent. It lives in your terminal, works as a desktop app, or as an IDE extension for VS Code and Cursor. It supports over 75 different AI models: Claude, OpenAI, Gemini, local models and yes, Kimi K2.6. Right now it has over 146,000 GitHub stars. That's not a niche tool. That's a movement. It has two main modes: build mode gives the agent full access. It can edit files, run commands, and take action across your codebase. Plan mode is read-only, so it can analyze your project before touching anything. Developers love it because it doesn't lock you into one model or one provider. You pick the model, keep control. When you run Kimi K2.6 inside Open Code, you're running one of the best agentic coding models available inside one of the best open source coding environments available. That's the combo. Let me show you what this actually looks like in practice. Moonshot AI published two real world demos with K2.6 that are hard to ignore. In the first, K2.6 was tasked with optimizing local AI model inference on a Mac using Zig, a niche low-level systems language. It ran for over 12 hours, made 4,000 plus tool calls, and improved throughput from around 15 to around 193 tokens per second. That's roughly 20% faster than LM Studio's performance. Zero human intervention. In the second, K2.6 autonomously overhauled Exchange Core, an eight-year-old open source financial matching engine. Over a 13-hour run, it tested 12 different optimization strategies, made over 1,000 tool calls, and modified more than 4,000 lines of code. Medium throughput jumped to 185%. These are vendor-reported demos from Moonshot's official blog. The numbers are specific and corroborated by multiple independent sources, and they point at something real. This model doesn't quit halfway through a complex task. Kilo Code summed it up well. K2.6 has sheer stamina and reliability for continuous, long-horizon coding tasks. K2.6 also showed more than 50% improvement on NextJS benchmarks specifically. So if you're building modern web apps, it's strong there too. When I first started working with agentic coding tools, I kept running into the same problem. The tools were powerful, but I didn't know which workflows actually saved time versus which ones just looked cool and wasted time anyway. That's when I built a community called AI Profit Boardroom. Over 2,000 members all focused on learning AI together and sharing what actually works. It helped me figure out which prompts get results, which tools are worth the setup time, and which ones to skip entirely. If you're serious about using AI to improve your work and your skills, check it out. Link in the description. Let's talk about content automation because this is where the combo really starts to compound. Think about a content pipeline. You've got a source, a YouTube transcript, a blog post, a set of notes. You want to turn that into multiple formats: email, social posts, a formatted report. For Open Code running K2.6, you describe the pipeline in plain language, then the agent builds it. Not just the logic, the actual working scripts, the file handling, the error handling. K2.6 handles Python, Go, Rust, JavaScript, front-end and back-end, and has been tested across all of them. And because it's open weight, you can run it on your own infrastructure. For teams with data privacy requirements, that matters a lot. The most important thing about prompting for agentic tasks: Be specific about the outcome, not the steps. If you say, "Build a landing page," the agent will make decisions, and some might not be what you wanted. If you say, "Build a landing page with a hero section, three feature blocks, a pricing comparison, and a contact form using Tailwind CSS and NextJS," now the agent has real constraints. Kilo Code pointed this out in their write up on K2.6. The model can be creative, and that creativity is a strength, but only when you give it clear direction. Left out, it'll solve the problem, just maybe not the way you expected. Plan mode in Open Code is great for this. Run it in Plan Mode first. Let it read the codebase and tell you what it's going to do, and switch to Build Mode once you're aligned. That two-step process saves a lot of back-and-forth. Here's the bigger picture. Shift from AI assistant to AI agent happened in 2025. An assistant helps you write code. An agent plans multi-step tasks, edits files across an entire codebase, runs tests, and works toward a goal with minimal direction. The shift is done. It's here. The question now isn't whether to use an AI coding agent. It's which one matches your workflow, and which model gives you the best results for your use case. K2.6 is the leading open-weight model right now. Open Code is the most flexible open source coding agent environment available. Together they give you frontier-level capability without provider lock-in. Here's where to start. Head to Opencode.ai and get it set up, terminal, desktop app, or inside VS Code. Connected to Kimi K2.6. You can access the model through Kimi.com, through the Moonshot API, or through any of the five API providers that support it. Start with something simple. A landing page. A small automation script. A tool to clean up a data set. Use Plan Mode first. Get the output. Make sure the agent understands what you're building. And let it run. If you want to dive deeper into AI tools and actually implement them in your work, I recommend AI Profit Boardroom. Over 2,000 people learning how to use AI effectively. Everyone shares real experiences, what's working, what's not, which tools are worth your time, which ones to skip, hype. It's practical guidance from people doing the work. Link in the description if you want to check it out. And if you want the full process, SOPs, and 100 plus AI use cases like this one, join the AI Success Lab. Link's in the comments and description. You'll get all the video notes from there, plus access to our community of 58,000 members who are crushing it with AI. If this was useful, hit the like button. It helps more people find this content. See you in the next one.