Mixture-of-Experts (MoE) models activate only a subset of their parameters for each token, dramatically reducing inference costs while maintaining quality. Among the 100 coding models we track, 1 use MoE or sparse architectures.
Top MoE Models
These models use sparse mixture-of-experts or similar architectures that activate only a fraction of total parameters per forward pass.
How MoE Works
Traditional dense transformers activate every parameter for every token. MoE models instead use a router network to select a subset of expert layers for each input. For example, a 397B parameter model might only activate 17B parameters per token - delivering the quality of a large model at the inference cost of a small one.
This architecture has been adopted by major players including DeepSeek (V3 series), Qwen (3.5 series), and Meta (Llama 4 Maverick). The trend toward MoE is accelerating as providers seek to offer better quality-per-dollar ratios.
Cost Efficiency
The primary advantage of MoE is cost efficiency. By activating fewer parameters, providers can offer lower token prices while maintaining competitive quality. In our data, MoE models average 79/100 vs 87/100 for dense models - a gap that is narrowing rapidly.