[Mixtral 8x22B Instruct](/models/mistralai--mixtral-8x22b-instruct) activates ~39B of its 141B total parameters per token via MoE routing; [Qwen 2.5 72B Instruct](/models/alibaba--qwen-2.5-72b-instruct) is a 72B dense model. Despite Mixtral 8x22B's larger total parameter count, per-token inference cost on most providers is comparable or slightly lower — MoE's active-parameter efficiency offsets the larger memory footprint. Expect pricing within 10–20% of each other depending on provider and batch size. Where they diverge is in throughput and domain strength. Mixtral 8x22B's sparse routing allows high tokens-per-second on throughput-optimized hardware. Qwen 2.5 72B's dense architecture, paired with Alibaba's extensive post-training on code and math, delivers measurably better results on coding benchmarks and multilingual tasks across 29+ languages. **Where Mixtral 8x22B Instruct wins:** Batch workloads prioritizing raw throughput — content pipelines, document processing at scale, classification tasks — where tokens-per-second and provider availability matter most. Mixtral 8x22B has strong support across EU and US providers, useful for data-residency requirements. **Where Qwen 2.5 72B Instruct wins:** Code generation, math reasoning, and non-English workloads where Alibaba's post-training investment shows in practice. If your product handles multilingual queries or developer tooling, the dense model's domain depth pays off. Pick Mixtral 8x22B Instruct for high-throughput batch pipelines where provider variety and tokens-per-dollar are the main levers. Pick Qwen 2.5 72B Instruct for coding, math, or multilingual tasks where quality per query matters more than raw throughput.