Gemma 4 MoE: frontier quality at 1/10th the API cost

Gemma 4 MoE: frontier quality at 1/10th the API cost gemma4 #moe #llm #openweights #aiinfra Continuing from Part 1 — once you have a proper state machine architecture, the next question is: which model runs inside it? For high-volume agent workloads, my pick is Gemma 4 26B MoE. Here's the actual reasoning. What MoE means (no marketing) Most LLMs are dense. A 30B dense model activates 30B parameters per token — every single one, every single call. Mixture-of-Experts works differently: Total parameters: ~25B Active parameters per token: ~3.8B A router picks 8 experts out of 128 per token Near-30B quality. ~4B compute per token. Not a trick. Just a better architecture for inference-heavy workloads. The real cost math GPT-4o: $2.50 per 1M input tokens, $10 per 1M output tokens. Gemma 4 is open-weight. Host it yourself on an A100. At volume — thousands of agent runs per day — the math flips hard in your favor. This matters specifically for agents because agents are token-heavy. One agent ru