## Llama 3.3 70B Instruct vs WizardLM-2 8x22B [WizardLM-2 8x22B](/models/microsoft--wizardlm-2-8x22b) is a Mixtral 8x22B fine-tune from Microsoft's Wizard team, specializing in complex instruction following and multi-step reasoning. [Llama 3.3 70B Instruct](/models/meta--llama-3.3-70b-instruct) is Meta's dense 70B model with broad provider coverage. On pricing, Llama 3.3 70B runs $0.20–$0.40/1M tokens; WizardLM-2 8x22B sits at $0.45–$0.90/1M tokens, reflecting the larger underlying architecture. WizardLM-2 8x22B was fine-tuned specifically to improve on multi-step reasoning and complex task decomposition. On MT-Bench and complex reasoning evaluations, it scores 2–4 points higher than Llama 3.3 70B. It also inherits Mixtral 8x22B's multilingual strengths. The tradeoff is provider availability — WizardLM-2 8x22B is served by fewer inference endpoints, limiting redundancy options. Llama 3.3 70B has significantly broader provider support (Groq, Together, Fireworks, AWS Bedrock, Azure) and the open Meta license means self-hosting on your own infrastructure is straightforward for compliance-sensitive deployments. **Where Llama 3.3 70B wins:** Cost-optimized production systems, any architecture requiring provider failover, and workloads with compliance requirements that favor self-hosted open weights over third-party API dependencies. **Where WizardLM-2 8x22B wins:** Agentic workflows requiring deep multi-step task decomposition, complex role-playing or scenario simulation, and pipelines where the quality lift on hard reasoning tasks justifies 2× higher spend. Pick Llama 3.3 70B for cost, availability, and licensing flexibility. Pick WizardLM-2 8x22B if complex multi-step reasoning is your bottleneck and provider optionality is secondary.