Mixtral 8x7B Instruct, Phi-3.5 MoE Instruct, and Qwen 3 32B Instruct each use mixture-of-experts routing to decouple total parameter count from per-token inference cost, but they target noticeably different niches and were released across a span of nearly a year. Mixtral 8x7B Instruct from Mistral AI has 46.7B total parameters and activates roughly 13B per token across 2 of 8 experts. Released December 2023, it was among the first open-weights models to reach GPT-3.5-class quality, and its Apache 2.0 license made it the de facto open MoE baseline for 2024. The 32K context window is adequate for most RAG workloads. As of mid-2026 it remains broadly hosted — Fireworks, DeepInfra, Groq — but Mixtral 8x22B and Mistral Small 3 have displaced it for new deployments. Phi-3.5 MoE Instruct from Microsoft has 41.9B total parameters and 6.6B active per forward pass across 16 experts, released August 2024. The 16-expert routing with a smaller active footprint than Mixtral 8x7B gives it a more favorable cost profile on hosted inference, while the 131K context window is a genuine step up. Benchmarks on reasoning tasks land closer to 14B dense models than 6B baselines. MIT license, no commercial friction. Provider coverage is thinner than Mixtral equivalents, with Azure AI as the primary route. Qwen 3 32B Instruct from Alibaba is a 32B model with 131K context, strong multilingual breadth, and one of the widest provider footprints in this parameter class. On general benchmarks it substantially outperforms both Mixtral 8x7B and Phi-3.5 MoE. Released under the Qwen license with commercial terms. Pick Mixtral 8x7B for Apache 2.0 licensing on a budget with solid coverage. Pick Phi-3.5 MoE for reasoning-heavy workloads where Azure AI is your primary provider. Pick Qwen 3 32B when quality, multilingual support, and long context are all required.