Mixture-of-Experts (MoE)

Mixture-of-Experts (MoE) is a modern architecture principle for Large Language Models that has fundamentally changed the AI landscape in 2026. The core principle: A model possesses hundreds of billions of parameters, but only a fraction of them are active for each request. A learned router network decides in real-time which specialized expert modules are activated for the respective task. The result: the quality of a massive model at the inference costs of a small one.

Concretely, this means: DeepSeek V3 has 671 billion parameters total but only activates 37 billion per request. Qwen3-235B possesses 235 billion parameters but only uses 22 billion simultaneously. And Llama 4 Maverick works with 400 billion parameters at a fraction of active capacity. Each expert module specializes in specific task types — coding, mathematics, creative writing or fact recall.

The counterpart is dense models like Claude Opus or GPT-5.2, where all parameters are active for every request. Dense models often deliver the highest quality but are significantly more expensive in inference. MoE models close this gap: They achieve 90-95% of dense model quality at 60-80% lower costs. This is the main reason why open-source models like DeepSeek V3 can suddenly compete with the most expensive proprietary models.

MoE enables scaling without proportional cost increase: A model can be trained on more and more knowledge (more parameters = more capacity) without the cost per request increasing proportionally — because only a small portion of parameters are actually computed. For companies, this means: frontier quality at mid-tier prices.