Open-Source LLMs: A Cost-Privacy-Performance Tradeoff for Enterprises
2025-05-17
This article benchmarks several open-source Large Language Models (LLMs) for enterprise applications, focusing on cost, privacy, and performance. Using the BASIC benchmark, models were evaluated on accuracy, speed, cost-effectiveness, completeness, and boundedness. Llama 3.2 offered a good balance of accuracy and cost; Qwen 2.5 excelled in cost-effectiveness; and Gemma 2 was the fastest, though slightly less accurate. While open-source LLMs still lag behind proprietary models like GPT-4o in performance, they offer significant advantages in data privacy and cost control, and are increasingly viable for critical enterprise tasks as they continue to improve.