Both [Mixtral 8x7B Instruct](/models/mistralai--mixtral-8x7b-instruct) and [Phi-3.5 MoE Instruct](/models/microsoft--phi-3.5-moe-instruct) are MoE architectures, but they operate at different scales. Mixtral 8x7B activates ~13B of 47B total parameters per token. Phi-3.5 MoE activates ~6.6B of 42B total parameters — smaller active footprint, lower per-token compute cost. On most providers, Phi-3.5 MoE prices 15–30% below Mixtral 8x7B on output tokens. Microsoft's Phi-3.5 MoE training pipeline focused heavily on synthetic high-quality data, which yields strong results on reasoning and language benchmarks despite the smaller active parameter count. It compresses capability into fewer active FLOPs more efficiently than Mixtral 8x7B's architecture, particularly on English-language reasoning tasks. **Where Mixtral 8x7B Instruct wins:** Multilingual workloads and use cases requiring broader language coverage. Mistral's training data distribution gives 8x7B stronger non-English performance. It also has longer market presence and broader provider availability, making it easier to source at spot pricing or with specific geographic routing. **Where [Phi-3.5 MoE Instruct](/models/microsoft--phi-3.5-moe-instruct) wins:** English-language reasoning, coding assistance, and instruction-following tasks where synthetic data quality pays off. Its lower active-parameter count means faster inference and lower cost per token — an attractive combination for cost-sensitive English-centric products. Pick Mixtral 8x7B Instruct if multilingual support or broad provider availability are requirements. Pick Phi-3.5 MoE Instruct if you're running English-language workloads at scale and want better reasoning quality at a lower per-token cost.