Gemma 4's 26B MoE Beats 4B Speed, Matches 31B Output
Google's Gemma 4 26B MoE model (25.2B params, 3.8B active) runs faster than the E4B while scoring within 2% of the 31B on benchmarks—ideal for high performance at low compute.
Gemma 4 Model Specs and Architectures
Google released four Apache 2.0 Gemma 4 models: E2B (effective 2B, 2.3B actual params for smartphones/CPUs), E4B (effective 4B, 4.5B params needing 8GB RAM for mid-range machines), 26B MoE (25.2B total params but only 3.8B activate per token via Mixture of Experts routing to specialist layers, mimicking 26B output at 4B compute), and 31B Dense (full dense model). All free for commercial use.
Hands-On Benchmark Results
Local tests across identical tasks showed the 26B MoE outperforming expectations: faster inference than E4B (significantly so) and within 2% of 31B Dense on every benchmark. This MoE design cuts compute by activating only a subset of experts per token, delivering large-model quality without full-network overhead—use it over smaller models for production where speed and capability matter.
Practical Pick: Run the 26B MoE
Skip E2B/E4B for most tasks; deploy 26B MoE on machines handling 4B-class loads to get near-topline results. Matches real-world needs for balancing param count, speed, and output without rate limits or costs.