[Mistral Large 2](/models/mistralai--mistral-large-2) is a 123B dense model; [Qwen 3 72B Instruct](/models/alibaba--qwen-3-72b-instruct) is Alibaba's third-generation 72B model with a hybrid thinking architecture that can toggle extended chain-of-thought reasoning per request. Pricing on Qwen 3 72B typically lands 35–50% below Mistral Large 2 on output tokens — but that gap narrows significantly when you enable thinking mode, which multiplies token count. Qwen 3 72B's thinking mode changes the comparison dynamic. For tasks like multi-step math, competitive coding, and complex logical deduction, it can match or exceed Mistral Large 2 quality by spending more tokens internally. In non-thinking mode it's a cost-efficient general model; in thinking mode it trades latency and cost for depth. **Where [Mistral Large 2](/models/mistralai--mistral-large-2) wins:** Latency-sensitive production workloads where you can't afford 2–5x token overhead from extended reasoning. Dense document processing, real-time chat, and structured extraction tasks where consistent, fast inference matters more than occasional deeper reasoning. **Where Qwen 3 72B Instruct wins:** Developer environments, agentic loops requiring genuine multi-step problem solving, and code generation tasks where you can selectively enable thinking mode for hard subtasks. Its stronger math and reasoning benchmarks in thinking mode make it the right pick when answer quality outweighs inference cost. Pick Mistral Large 2 if you need predictable low-latency outputs across varied workloads without per-request reasoning overhead. Pick Qwen 3 72B Instruct if you're building reasoning-heavy agents or want a dual-mode model that scales quality with compute budget.