Every new model release now lands with the same question: is it actually better for your work, or just better on a leaderboard? This chapter exists because the answer can't be read off a spec sheet — it comes from hands-on testing, and the Every team has run that testing repeatedly across a year of releases. After reading it, you will be able to independently evaluate and compare newly released AI models using hands-on vibe checks: you'll know which benchmarks and informal tests to run, how to tune reasoning effort per task, how to weigh a model's ceiling against its consistency, and how to combine models so their blind spots cover each other. You'll also be able to articulate where specific models — Claude Opus variants, GPT-5.x, Codex, Fable, and Astra — excel or fall short, with the evidence to back it up.[^01a0f15a-89e4-78b7-b60b-4e06b0b83628@506][^01a0f15a-a8f4-737e-b0c4-78fba25148d5@623] The teaching draws on twelve vibe-check sessions; where the team disagreed or hedged, this chapter preserves that disagreement rather than flattening it.