로컬 Qwen은 더 나쁜 Opus가 아니라 다른 도구다

This article repositions local Qwen models not as inferior alternatives to Claude Opus, but as tools optimized for privacy, cost predictability, and vendor risk mitigation. Benchmark comparisons alone fail to capture real-world performance across different programming languages and system architectures, exposing the limitations of direct performance rankings. As AI commoditization accelerates in 2026, 'good enough' local solutions become strategically valuable for organizations with data sovereignty constraints and cost-sensitive deployment scenarios.