## Llama 3.3 70B Instruct vs Mixtral 8x22B Instruct This is a dense-vs-MoE comparison. [Mixtral 8x22B Instruct](/models/mistralai--mixtral-8x22b-instruct) has 141B total parameters but only activates ~39B per token via its mixture-of-experts routing, giving it a VRAM footprint roughly comparable to a dense 40B model. [Llama 3.3 70B Instruct](/models/meta--llama-3.3-70b-instruct) is a fully dense 70B model. In practice, Mixtral 8x22B costs $0.45–$0.90/1M tokens while Llama 3.3 70B runs $0.20–$0.40/1M tokens — Llama wins on price. Benchmark quality is more competitive. Mixtral 8x22B scores 2–5 points higher on math-heavy benchmarks (GSM8K, MATH) and multilingual tasks, which reflects its larger expert capacity. Llama 3.3 70B closes the gap significantly on English reasoning and instruction-following, scoring above 90% on IFEval. Throughput on shared infrastructure slightly favors Mixtral 8x22B due to sparse activation — providers can serve more requests per GPU-hour — but this efficiency gain is often priced away rather than passed to the user. **Where Llama 3.3 70B wins:** English-language production workloads where cost per token is the primary constraint. The open weights and broad provider availability (Fireworks, Together, Groq) mean you'll find competitive pricing easily. **Where Mixtral 8x22B wins:** Multilingual tasks, math reasoning, and scenarios where you need slightly stronger general-purpose performance and can absorb a 2× price premium. Pick Llama 3.3 70B for cost-optimized English inference. Pick Mixtral 8x22B if multilingual coverage or stronger mathematical reasoning is a hard requirement.