AISLE, a startup specializing in AI security, has demonstrated that smaller, cost-effective AI models can match the capabilities of Anthropic's Mythos in identifying security vulnerabilities. After Mythos autonomously discovered significant vulnerabilities in FreeBSD and OpenBSD, AISLE tested these findings on cheaper models, including DeepSeek R1, which successfully identified the same vulnerabilities at a fraction of the cost. The results challenge the notion that only advanced models like Mythos can autonomously detect vulnerabilities. AISLE's approach, which involves using multiple models dynamically, highlights that AI security effectiveness depends more on system design than model size. This suggests a shift towards a more collaborative ecosystem in AI security, where diverse AI models and expertise can collectively enhance software safety.