Model Economics
2026 is the year AI practitioners stopped asking “which model is best?” and started asking “which model is right for this specific task at an acceptable cost?” Frontier models are powerful but expensive — and often unnecessary. Understanding token pricing, capability tiers, and optimization patterns is now a core engineering skill. The difference between a naive implementation and an optimized one is often 10–90% cost reduction.
In This Section
Why Model Selection Matters
The 2026 cost landscape, why frontier models are overused, real cost differences between tiers, and how to think about model economics.
Understanding Token Costs
Input vs output pricing, context window costs, prompt caching (90% off cached tokens), batch discounts (50% off), and cost estimation techniques.
Model Capability Tiers
Tier 1 (frontier) through Tier 4 (on-device) — what each tier costs, what it can do, and which tasks actually need each tier.
Latency vs Quality Tradeoffs
Real-time vs async use cases, streaming considerations, reasoning model latency overhead, and when latency matters more than quality.
Model Selection Cheat Sheet
Task-by-task model recommendations — summarization, coding, RAG, classification, agentic tasks, vision, creative writing, and complex reasoning.
Cost Optimization Patterns
Prompt caching, batch processing, model routing, fine-tuning for narrow tasks, and how to stack discounts for maximum savings.