Mixtral 8x22B Instruct vs WizardLM-2 8x22B

[Mixtral 8x22B Instruct](/models/mistralai--mixtral-8x22b-instruct) and [WizardLM-2 8x22B](/models/microsoft/wizardlm-2-8x22b) share identical base architecture — both are MoE models derived from the same 141B/39B-active Mixtral 8x22B weights. The difference is in post-training: WizardLM-2 8x22B was fine-tuned by Microsoft's Evol-Instruct pipeline, which emphasizes complex instruction following, step-by-step reasoning, and chat alignment. Pricing between them is nearly identical across providers since the base compute requirements are the same.

WizardLM-2 8x22B's Evol-Instruct training makes it measurably stronger on complex, multi-constraint instructions and extended reasoning chains. Independent evals consistently show it scoring higher on instruction-following benchmarks than the base Mixtral instruct variant, particularly on tasks requiring multiple nested conditions or careful constraint adherence.

**Where [Mixtral 8x22B Instruct](/models/mistralai--mixtral-8x22b-instruct) wins:** Broad general-purpose batch workloads where the post-training difference doesn't surface — translation, summarization, classification, and content generation pipelines. Mixtral 8x22B Instruct also has wider provider support, giving you more options for geographic routing and spot pricing.

**Where WizardLM-2 8x22B wins:** Complex instruction-following tasks, multi-step reasoning, and chat applications where users issue nuanced, multi-constraint prompts. The Evol-Instruct alignment produces noticeably cleaner outputs on hard instruction sets.

Pick Mixtral 8x22B Instruct for simple, high-volume inference with maximum provider flexibility. Pick WizardLM-2 8x22B when your prompts are complex and instruction adherence quality directly affects output usability — at no meaningful cost premium.