Part II: Understanding LLMs
The large language model ecosystem has grown at a breathtaking pace. Closed-source frontier models from OpenAI, Anthropic, and Google push the boundaries of capability, while open-weight releases from Meta, DeepSeek, Mistral, Alibaba, and Microsoft have democratized access to powerful models that anyone can download, fine-tune, and deploy. Meanwhile, a new class of reasoning models has emerged, shifting compute from training time to inference time through extended chains of thought, process reward models, and tree search over candidate solutions.
This chapter surveys the current landscape across four complementary perspectives. We begin with the closed-source frontier (Section 7.1), examining the capabilities, pricing, and architectural hints available for GPT-4o, Claude, Gemini, and their competitors. Section 7.2 dives deep into open-source and open-weight models, with particular attention to architectural innovations like DeepSeek V3's Multi-head Latent Attention, FP8 training, and auxiliary-loss-free Mixture of Experts. Section 7.3 explores the paradigm shift toward reasoning models and test-time compute scaling. Finally, Section 7.4 addresses the multilingual and cross-cultural dimensions that determine whether these models serve a global audience or remain English-centric tools.