Most teams still buy inference the same way they always have. Pick a model, wire it in, hope it stays good enough long enough to justify the work. Then a better model ships, a pricing sheet changes, or a provider quietly shifts behavior, and the whole exercise starts again. That
Every few weeks, another closed-source, branded LLM launches - GPT-5.4 Pro. Claude Opus 4.5. Gemini 3.1 Pro - and so on. Each one comes with impressive benchmarks and a price tag to match, carrying the implicit message that top intelligence costs top dollar. At The Grid, we
Most teams build around one model. Find the best, lock in, optimize around it. We tested the opposite: what happens when you route across multiple cost-efficient open-source models instead? On over 21,000 simulations of one of the hardest agentic benchmarks available, that blend scored 90.9%, within 7 points
The gap between the #1 and #10 AI model shrank from 11.9% to 5.4% in a single year. Between #1 and #2? From 4.9% down to 0.7%. This data is reported by Stanford's AI Index, tracking Chatbot Arena scores. The most comprehensive public model