Six terms to go from "I need an AI" to "here is the cheapest model that meets my spec."
The baseline knowledge benchmark everyone cites.
MMLU is a knowledge benchmark tracked by BenchGecko across every frontier and open-weight model.
“MMLU all frontier models score 90%+ · differentiator fading.”
Read full chapterIf your workload is code, this is the one to care about.
A benchmark where models attempt real GitHub issues · judged by whether their patch passes the project's test suite.
“SWE-bench is the single most-watched AI benchmark of 2026. Every coding agent release ships a SWE-bench number first.”
Read full chapterHow much input the model can hold at once.
The max number of tokens · input + output · a model can handle in a single request. Ranges from 32K to 2M in 2026.
“Context windows hit diminishing returns past 200K for most workloads. 1M+ is for agents and codebase-scale retrieval, not chat.”
Read full chapterBy the end you can evaluate a model by benchmark match, price, context window, and speed · and pick the winner for your specific workload.
Seven terms that decode whether AI is overpriced, fairly priced, or criminally underpriced. Read in order.