These Open-Source AI Agents Are INSANE! (Best of 2025) 🤯

Open- source AI just beat the big guys. No joke. These models crush GPT4 and Claude. And they're totally free. I'm going to show you five agents that will blow your mind, plus the one that's secretly the best. Hey, if we haven't met already, I'm the digital avatar of Julian Goldie, CEO of SEO agency, Goldie Agency. Whilst he's helping clients get more leads and customers, I'm here to help you get the latest AI updates. And today, we're talking about something huge. Open- source AI agents just changed the game. I'm talking about models that beat Claude, beat Gemini, beat everything you're paying for right now, and they're free. So, here's what's happening. The arena rankings just dropped for end of 2002. These are the real rankings, not marketing hype, actual user votes. And guess what? Top the list, not OpenAI, not Anthropic, not Google. Open source models. GLM 4. 7 hit number one. Kimmy K2 took second place. Deep Seat crushed third. These aren't toys. They're production ready. agents that actual developers are using right now. So, I'm going to break down the top five models you need to know about, what they're good at, when to use them, and which one is the absolute best for building AI agents. Let's go. GLM 4. 7, the king.

First up, JLM 4. 7. This is the number one ranked open source model right now. Made by GPU AI, MIT, licensed, completely free. And here's why it won. It's balanced, like perfectly balanced, great at reasoning, strong at coding, handles long conversations without losing context. Most open source models are good at one thing. GLM 4. 7 is good at everything. Think about it like this. You want to build an AI agent that researches competitors for your business. It needs to read long articles, understand context, pull out insights, maybe even call some tools to grab data. GLM 4. 7 does all of that. Here's a real example. Let's say you're running the AI profit boardroom. You want an agent that can analyze your competitor's content strategy. You'd feed it 10 competitor websites. Ask it to find patterns, identify what's working, suggest content gaps you should fill. GLM 4. 7 handles that easily. It doesn't just summarize. It thinks, it connects dots. It gives you actual strategy. And because it's MIT licensed, you can use it commercially, build products with it, sell services around it. Zero restrictions. That's why developers love it. If you want one open- source model for everything, this is it. But wait till you see what's next. Next up, Kim K2 thinking. This

one's different. It's not trying to be fast. flashy. It's built for one thing, planning. Kim K2 uses a thinking first approach. Before it answers, it plans out the steps. Kind of like how 01 works from OpenAI, but open source. So, when is this useful? Anytime you need multi-step reasoning. Let's say you want to build an automation for the AI profit boardroom. You need an agent that can take a customer question, figure out which tool to use, gather the info, and send a response. That's a planning problem. Kim K2 breaks it down. Step one, understand the question. Step two, identify the right tool. Step three, execute. Step four, format the response. It doesn't skip steps. It doesn't guess. And that makes it way more reliable for complex workflows. Here's another example. You want to automate content creation, but not just generate one article. You want a full content calendar. Kim K2 can plan that out, research topics, identify search intent, map out a schedule, even suggest which articles to write first based on keyword difficulty. It thinks like a strategist, that's the whole point. If you're building agents that need logic and planning, Kim K2 is your go-to. And it gets even better. Third on

the list, Deepseek V3. 2. If you've been following AI for a while, you know Deepseek. They've been crushing coding benchmarks for years, and V3. 2 just made it even better. Faster inference, cleaner code, better instruction following. This is the model you use when your agent writes code. Let me give you a real use case. Say you're building an automation for the AI profit boardroom. You want an agent that can take a customer request, write a Python script to pull data, run it, and send back results. That's pure coding. Deepseek v3. 2 handles that better than almost anything else. It doesn't just write code. It debugs. It refactors. It optimizes. I've seen people use this to automate entire GitHub workflows. issues come in, DeepSeek reads them, writes the fix, submits a pull request, all automated. That's insane. And because it's open source, you can run it locally. No API costs, no rate limits. If you're a developer, this is your dream model. But the next one, that's where things get really interesting. Real quick, if you're watching this and thinking, I want to actually use these tools to automate my business, you need to check out the AI Profit Boardroom. is our community where we teach you how to save time and work smarter with agents like these. We've got templates, workflows, and real examples of how to automate lead generation, content creation, and customer support. All with tools like GLM 4. 7 and Kim K2. We show you the exact systems that let you focus on growing your business instead of getting stuck in repetitive tasks. Links in the description. Join us. All right, back to the models. And this next one is

the Dark Horse. Now, here's the one nobody's talking about. Miniax M2. 1. This model didn't just compete with the big guys. It beat them on S. WE. is number one. That's the hardest real world coding benchmark on Vibe. It's number one. That's agent execution. It even beat Gemini 3 Pro. Beat Claudson 4. 5 and it's open source. Here's what makes it different. Miniax isn't built for chat. It's built for agents. It uses a mixture of experts architecture. 10 billion active parameters, 230 billion total. That means it's fast but still powerful and it's ridiculously good at tool use. Let me show you why this matters. Say you want to build an agent that monitors the AI profit boardroom community. It checks for new questions, finds relevant answers from past threads, post helpful responses automatically. That's a multi-tool workflow. Miniax M2. 1 is built for exactly that. It doesn't just call one tool. It orchestrates multiple tools reliably and you can run it locally. That's huge for privacy, for cost, for speed. If you're serious about building production agents, Miniax M2. 1 is the best option right now. Julian Goldie reads every comment, so make sure you comment below which model you're most excited about. Last one, Gemma 327B.

This isn't the flashiest model. It's not the most powerful, but it's the most deployable. Google built Gemma to be clean, predictable, efficient. It's not trying to wow you with crazy reasoning. It's trying to just work and that's really valuable. Here's when you'd use it. You want to build a simple assistant for the AI profit boardroom. Something that answers common questions, helps members find resources, maybe root support tickets. You don't need the most powerful model. You need reliability. Gemma 327B gives you that. It's also great for fine-tuning. You can train it on your own data without needing massive compute. So, if you have specific use cases, Gemma adapts easily. It's not the hero model, but it's the one that ships.

All right, five models. Let's break it down. If you want the best all-around model, use GLM 4. 7. It's number one for a reason. If you need planning and logic, use Kim K2. It thinks before it acts. If you're building coding agents, use DeepSeek V3. 2. It writes better code than most closed models. If you need real agent execution, use Miniax M2. 1. Is built for workflows. And if you need something clean and deployable, use Gemma 327B. It just works. Here's the crazy part. All of these are free. All of them are open source. All of them run locally if you want. That means zero API costs. Complete control. No vendor locking. Compare that to paying for GPT4 or claude. This is a huge shift. Open source just became production ready. So

what happens in 2026? Honestly, I think we're going to see even more competition. Ma's working on Llama 4. China's pushing hard on open models. Even smaller labs are releasing agents that beat the big guys. The gap between open and closed is shrinking fast. And that's amazing for developers, amazing for businesses, amazing for anyone building with AI because now you have options. You don't have to pay thousands a month for Claude. You can run Miniaax locally and get better results. That's the future. All right. If you want to actually start building with these models, join the AI profit boardroom. We've got full guides on how to set these up, how to build agents, how to automate your business with open source AI. We're not just talking theory. We're showing you real workflows that save time and let you focus on what matters. Links in the description. And if you want the full process, SOPs, and 100 plus AI use cases like this one, join the AI success lab. It's our free AI community. You'll get all the video notes from there, plus access to our community of 40,000 members who are crushing it with AI. Links in the comments and description. Thanks for watching. I'll see you in the next one.