In this article, we will cover various open-source models and the engineering bets that define each one.

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A technical breakdown of the architectural choices behind major open-weight LLMs released in 2025-2026, including DeepSeek V3, Kimi K2, Qwen3, Llama 4, GLM-5, and Mistral Large 3. All frontier models now use Mixture-of-Experts (MoE) transformers, but differ in attention strategy (GQA, MLA, or sparse attention), expert count (16 to 384), and post-training approaches (RL with verifiable rewards, distillation, synthetic agentic data). Key engineering contributions include DeepSeek's Multi-Head Latent Attention for KV-cache compression, Kimi K2's MuonClip optimizer for training stability at trillion-parameter scale, and GLM-5's Slime async RL framework. The piece also clarifies the open-weight vs. open-source distinction and the varying license terms across models.

The Architecture Behind Open-Source LLMs

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