[WizardLM-2 8x22B](/models/microsoft--wizardlm-2-8x22b) is a sparse mixture-of-experts (MoE) model with ~141B total parameters but only ~39B active per forward pass. [Qwen 3 72B Instruct](/models/alibaba--qwen-3-72b-instruct) is dense at 72B active parameters. This architectural difference has direct pricing implications: MoE models often cost more per request at providers that price on total memory allocation, not active FLOPS — WizardLM-2 8x22B typically runs $0.50–0.90/M tokens versus Qwen 3 72B's $0.40–0.70/M. On reasoning benchmarks, Qwen 3 72B is more recent and generally scores higher on MMLU (86–88%) and math (GSM8K ~90%) than WizardLM-2 8x22B. WizardLM-2 was Microsoft's instruction-tuning research model; its strength is complex instruction-following and alignment with human preference tasks like creative writing and nuanced Q&A, where the MoE architecture provides specialization breadth. WizardLM-2 8x22B performs well on open-ended creative generation and long-form dialogue, areas where the MoE routing can activate specialized expert pathways. It's also a reasonable choice where Microsoft's model provenance matters for enterprise procurement. Qwen 3 72B Instruct is the better pick for math-heavy, code-adjacent, or structured reasoning pipelines where pure benchmark performance drives model selection. Its cost-per-token is lower than WizardLM-2 8x22B at most providers, and it benefits from a more active maintenance track. Pick Qwen 3 72B Instruct for reasoning and structured tasks. Pick WizardLM-2 8x22B if complex instruction alignment or creative generation quality justifies the higher serving cost.