The core tradeoff here is parameter count versus price. [Mistral Large 2](/models/mistralai--mistral-large-2) runs 123B parameters; [Qwen 2.5 72B Instruct](/models/alibaba--qwen-2.5-72b-instruct) comes in at 72B. Across commodity providers, Qwen 2.5 72B consistently prices 40–55% cheaper per million tokens on both input and output, simply because it fits on fewer GPUs and competes in a more crowded supply market. Benchmark scores narrow that gap considerably. On MMLU and coding evals, Qwen 2.5 72B punches well above its size — Alibaba's post-training work on math and code reasoning shows in practice. For most general-purpose tasks, quality differences are marginal unless you're doing highly nuanced multi-step reasoning or complex function-call chaining. **Where Mistral Large 2 wins:** Tasks demanding deep reasoning over long contexts (64K+ tokens), multi-document synthesis, and agentic pipelines with dense tool-use schemas. Its larger capacity handles context degradation more gracefully at high fill ratios. **Where Qwen 2.5 72B Instruct wins:** Cost-constrained API products, coding assistants, math-heavy workflows, and any workload where you can validate quality cheaply and scale horizontally. The model's strong multilingual coverage (29+ languages) also makes it preferable for non-English inference at scale. Pick Mistral Large 2 if you need headroom for complex reasoning over very long inputs and budget isn't the primary constraint. Pick Qwen 2.5 72B Instruct if you're running high-volume inference and want solid quality at 40–55% lower cost per token.