New Chinese AI DeepSeek is INSANE!

Deepseek v3. 2 models are live in Code Arena. Deepseek v 3. 2 just dropped in Code Arena and it's crushing everything. I'm talking about a model that costs pennies and beats GPT4. This thing writes code better than models that cost 20 times more. And I'm going to show you exactly what makes it so special. So, here's what just happened. Deepseek dropped their V3. 2 models into Code Arena and everyone's losing their minds. Why? Because this model is doing something no one expected. It's beating Claude 4. 5 in coding tests. It's competing with GPT4 Turbo. And get this, it costs almost nothing to run compared to OpenAI Anthropic. 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. Now, before I go deeper, let me tell you what Code Arena actually is. Code Arena is like a fighting ring for AI models. Developers throw coding problems at different AI models and see which one solves them best. It's anonymous testing, so there's no bias. The model either writes working code or it doesn't. Simple as that. And right now, Deepseev 3. 2 is climbing the ranks faster than anyone predicted. Let me break down what makes Deepseek v 3. 2 different. First off, this is an open-source model. That means anyone can look at how it works. Anyone can run it on their own servers. You're not locked into one company's API. You're not paying crazy fees every time you use it. The model has 671 billion parameters. But here's the trick. It only uses 37 billion at a time. This is called mixture of experts architecture. Think of it like having a team of specialists. When you ask a coding question, only the coding expert wakes up and answers. When you ask about data analysis, a different expert handles it. This makes the model super fast and super cheap to run. The training data is massive. Deepseek trained this thing on 14. 8 trillion tokens. That's an insane amount of text and code. For comparison, that's like reading every book in multiple libraries combined with every piece of code on GitHub. And they didn't just throw random data at it. They used something called FP8, mixed precision training. This is a technical way of saying they made the model learn efficiently without wasting computer power. Most companies use FP16 or BF-16 precision which needs way more computing power. Deepseek figured out how to cut that in half. Here's where it gets interesting. Deepseek fee 3. 2 has two versions. There's the base model which is the raw AI brain. Then there's the instruction tuned version which is trained to follow commands and write better code. The instruction tuned version is what's competing in code arena right now. And it's not just competing, it's winning against models that cost way more to build and run. Let me talk about the actual performance numbers because this is where your jaw drops. On the human evil benchmark, which tests how well AI writes Python code, Deepseek V3. 2 scores 90. 2%. That's higher than GPT4 Turbo. It's neck andneck with clawed Sonnet 4. 5. On the MBP PPP benchmark, which is another coding test with more complex problems, it scores 80. 5%. These aren't small improvements. These are massive jumps in capability. And if you want to dive deeper into AI automation, I've got something special for you. I run a community called the AI Profit Boardroom. The best place to scale your business, get more customers, and save hundreds with AI automation. Learn how to save time and automate your business with AI tools like DeepSeek V3. 2 and Code Arena. The link is in the comments and description. It's at school. com/aiiprofitlab. But here's what really matters. The model doesn't just write code that looks good. It writes code that actually works. A lot of AI models can generate code that seems right but breaks when you run it. Deep Seat V 3. 2 has something called multi-token prediction during training. This means while it's learning, it doesn't just predict the next word. It predicts multiple words ahead. This helps it understand code structure better. It understands how functions connect. It understands how variables flow through programs. It writes code that compiles and runs correctly the first time more often than other models. Now, let's talk about what this means for Code Arena rankings. Code Arena uses L ratings just like chess. Every time a model wins against another model, it rating goes up. Every time it loses, the rating drops. Deep Seek V3. 2 entered the arena and started beating models left and right. is currently sitting in the top tier alongside Claude Sonet 4. 5 and GPT for Turbo. Some tests show it actually beating Claude in specific coding tasks. That's wild because Claude has been the gold standard for code generation for months. The really cool part is how fast it responds because of that mixture of experts architecture I mentioned earlier. Deepseek v 3. 2 generates code faster than GPT4. When you're writing code and waiting for AI suggestions

speed matters. If the AI takes 10 seconds to respond, you lose your flow. Deepseek responds in 2 to 3 seconds for most queries. That's a gamecher for developers who use AI coding assistance all day. Let me get specific about what kinds of code it writes. Well, Python is obviously strong since that's what most AI models focus on. But Deepseek via 3. 2 also handles JavaScript really well. It understands React components. It can write Vue. js code. It handles TypeScript with proper type definitions. For back-end work, it writes clean Node. js code. It understands Express routing. It can set up database connections properly. When it comes to systems programming, it handles C++ and Rust better than most models. It understands memory management. It writes code that doesn't leak memory or cause segmentation faults. Here's something most people don't talk about. Deepsee fake 3. 2 is really good at understanding context. If you show it your existing codebase and ask it to add a feature, it matches your coding style. It uses the same naming conventions. It follows the same patterns. A lot of AI models write code that feels foreign to your project. Deepseek v 3. 2 writes code that looks like you wrote it. This matters because when you're building real projects, consistency is everything. The model also handles debugging really well. Uh you can paste an error message and your code and it figures out what's wrong. It doesn't just guess. It traces through the logic. It identifies where variables might be undefined. It spots off by one errors in loops. It catches type mismatches and it explains the fix in plain English before showing you the corrected code. This is huge for learning because you're not just copying fixes. You're understanding why the code was broken. Now, I want to talk about the actual architecture because this explains why it performs so well. Deepseek fee 3. 2 2 uses something called multi head latent attention. Standard attention mechanisms in AI look at every word in relation to every other word. This works, but it's slow and expensive. Multi-head latent attention compresses information first, then does the attention calculation. It's like summarizing the important parts before analyzing them. This makes the model way faster without losing accuracy. They also use deepsee which is their custom mixture of expert system. Most systems have routing problems where the model always uses the same experts and ignores others. Deepseek fix this with auxiliary loss functions that force the model to use all experts evenly. This means the model actually gets smarter because it's using its full brain power instead of just part of it. The training process is fascinating, too. They used a cluster with thousands of GPUs running for months. But here's the smart part. They didn't train one giant model from scratch. They started with Deep Seek Faux3 and fine-tuned it into V3. 2. This is way more efficient than starting over. They focused the new training on coding tasks, mathematical reasoning, and following complex instructions. They used reinforcement learning from human feedback, but they added something extra. They used reinforcement learning from AI feedback, too. The model learns from human experts and from other AI models pointing out mistakes. Let me talk about how it handles different programming paradigms. For object-oriented programming, it writes clean classes with proper inheritance. It uses design patterns correctly. It implements interfaces properly. For functional programming, it writes pure functions. It handles immutability correctly. It uses higher order functions like map, filter, and reduce appropriately. If you want to dive deeper into AI automation, I've got something special for you. I run a community called the AI profit boardroom. The best place to scale your business, get more customers, and save hundreds with AI automation. Learn how to save time and automate your business with AI tools like Deep Seek V3. 2 and Code Arena. The link is in the comments and description. is at school. com/iprofit