Frontier Model Landscape
The model layer is the most rapidly evolving part of the AI stack. New models ship monthly, benchmarks shift, and pricing drops constantly. This section cuts through the noise - covering what actually matters when you choose a model, what the major families are, and how open-weight and proprietary approaches compare in practice.
In This Section
The Major Model Families
OpenAI GPT series, Anthropic Claude, Google Gemini, Meta LLaMA, Mistral, DeepSeek - what each family stands for and when to reach for it.
Open-Weight vs Proprietary
The real trade-offs between closed APIs and open-weight models - cost, control, data residency, customisation, and sovereignty.
Choosing the Right Model
A decision framework for picking models by task type, latency, cost, data sensitivity, and context window requirements.
Model Benchmarks & Leaderboards
What the major leaderboards measure, how to read them, and why high benchmark scores don't always translate to production wins.
Open-Source LLM Comparison 2026
Llama 4, Mistral, DeepSeek, Qwen, Gemma - side-by-side across capability, context window, license, and deployment requirements.
AI Benchmarks Deep Dive
MMLU, MATH-500, SWE-bench, HLE, GPQA, ARC-AGI - what each measures, how hard it is, and which frontier models top each.
Why Benchmarks Lie
Benchmark saturation, data contamination, Goodhart's Law, and how to evaluate models for your actual use case instead.