What does the The Rise of Mixture-of-Experts cover?

MoE architectures deliver frontier performance at a fraction of the cost. How they work and who leads.

How often are these reports updated?

Reports are published regularly and use live data that refreshes hourly. The analysis and rankings reflect the most current information available.

How are AI models scored in this report?

We use a composite scoring system weighing benchmark performance (90%) from MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations, with capabilities and context window as tiebreakers (10%). Scores are normalized to a 0-100 scale.

Can I compare specific models mentioned in this report?

Yes - click any model name to see its full profile, or use our comparison tool at /compare to see side-by-side analysis of any two models.

The Rise of Mixture-of-Experts

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, 0 use MoE or sparse architectures.

MoE Models

100

Dense Models

MoE Avg Score

/100

Dense Avg Score

/100

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.

Key Takeaways

0 MoE models are now competitive in the coding category, up from near-zero two years ago.

MoE architectures offer 3-10x cost reduction per token compared to equivalent dense models.

The quality gap between MoE and dense models has effectively closed for most practical applications.

Expect MoE to become the default architecture for new model releases in 2026.