Both models are sparse Mixture-of-Experts, but at very different scales. [Mixtral 8x22B Instruct](/models/mistralai--mixtral-8x22b-instruct) has 141B total parameters with ~39B active per token across 8 experts. [DeepSeek V3](/models/deepseek--deepseek-v3) runs 671B total with ~37B active, using a finer-grained 256-expert routing scheme. Active parameter counts are roughly comparable, but DeepSeek V3's larger total capacity enables substantially higher benchmark scores — roughly 10–15 points ahead on MMLU and HumanEval in most published evals. Pricing splits along provider maturity: Mixtral 8x22B is a well-established model with broad provider support and competitive spot rates often under $0.50/M input tokens. DeepSeek V3 rates vary more widely by provider, ranging from ~$0.14/M at the cheapest to $1+/M at providers without dedicated MoE kernels. Mixtral 8x22B Instruct holds a latency advantage on providers with mature vLLM deployments — its smaller total weight footprint makes cold starts and autoscaling faster. For low-latency classification, routing, or short-form generation workloads where first-token latency matters, Mixtral's smaller footprint can win despite lower benchmark ceiling. DeepSeek V3 is the choice for quality-sensitive tasks: complex code generation, multi-step reasoning, and long-document summarization where the larger total capacity produces measurably better outputs. At the cheapest providers, you get significantly higher reasoning quality at comparable or lower cost per token. Pick Mixtral 8x22B Instruct if you need mature provider tooling, low first-token latency, or consistent autoscaling. Pick DeepSeek V3 if output quality is the priority and you can tolerate more provider variability in exchange for a higher reasoning ceiling.