Mistral Large 2 vs Qwen2.5-72B-Instruct

On shared benchmark coverage, HumanEval has Mistral Large 2 at 84.8 and Qwen2.5-72B-Instruct at 92.7, with Qwen2.5-72B-Instruct ahead by 7.9 points; Chatbot Arena has Mistral Large 2 at 1265 and Qwen2.5-72B-Instruct at 1270, with Qwen2.5-72B-Instruct ahead by 5 points; Massive Multitask Language Understanding has Mistral Large 2 at 84 and Qwen2.5-72B-Instruct at 88.2, with Qwen2.5-72B-Instruct ahead by 4.2 points. The largest visible gap is 7.9 points on HumanEval, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.

The capability footprint differs most on vision: Mistral Large 2, multimodal input: Mistral Large 2, function calling: Mistral Large 2, and tool use: Mistral Large 2. Both models share structured outputs, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.

For cost, Mistral Large 2 lists $0.48/1M input and $2.4/1M output tokens, while Qwen2.5-72B-Instruct lists $0.12/1M input and $0.39/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen2.5-72B-Instruct lower by about $0.86 per million blended tokens. Availability is 4 providers versus 7, so concentration risk also matters.

Choose Mistral Large 2 when vision-heavy evaluation are central to the workload. Choose Qwen2.5-72B-Instruct when provider fit, lower input-token cost, and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.