If you follow AI, you know the story: every few months, a new language model drops with more parameters and splashier headlines. But as Sebastian Raschka highlights in “The Big LLM Architecture Comparison: From DeepSeek-V3 to Kimi K2: A Look At Modern LLM Architecture Design,” the biggest lesson from this new wave of open-source LLMs is how much has not fundamentally changed. Underneath it all, the progress is less about radical reinvention and more about clever architectural tweaks—optimizing memory, attention, and stability to make bigger, faster, and more efficient models.

At the core, the 2017 transformer blueprint is still powering everything. What’s new? A handful of impactful upgrades:

• Smarter attention (like Multi-Head Latent Attention and Grouped-Query Attention) slashes memory requirements.
• Mixture-of-Experts (MoE) lets trillion-parameter giants run without melting your GPU by only activating a fraction of the network at a time.
• Sliding window attention makes long contexts feasible without hogging resources.
• Normalization tricks (RMSNorm, Post-Norm, etc.) are now essential for training stability at scale.

Today’s best open models—DeepSeek, Kimi, Llama 4, Gemma, OLMo 2, Qwen3—are all remixing these tools. The differences are in the fine print, not the fundamentals.

But what about OpenAI’s GPT-4/4o or Anthropic’s Claude 3.5? While the specifics are secret, it’s a safe bet their architectures look similar: transformer backbone, MoE scaling, memory-efficient attention, plus their own proprietary speed and safety hacks. Their big edge is polish, robust APIs, multimodal support, and extra safety layers—perfect if you need instant results and strong guardrails.

So, which should you pick?

• Want transparency, customization, or on-prem deployment? Open models like OLMo 2, Qwen3, or Gemma 3 have you covered.
• Building for research or scale (and have massive compute)? Try DeepSeek or Kimi K2.
• Need to serve millions, fast? Lighter models like Mistral Small or Gemma 3n are your friend.
• If you want the “it just works” experience with best-in-class safety and features, OpenAI and Anthropic are still top choices—just expect less control and no deep customization.

In the end, all the excitement is really about optimization, not paradigm shifts. Progress now means making LLMs faster, stabler, and easier to use. Or as Raschka puts it: “Despite all the tweaks and headline-grabbing parameters, we’re still standing on the same transformer foundation—progress comes from tuning the architecture, not tearing it down.”

If you want the deep technical dive, read Raschka’s full “The Big LLM Architecture Comparison.” Otherwise, just remember: the transformer era isn’t over—it’s just getting a whole lot more interesting.