做安全测试或 AI 落地的团队，这个案例直接告诉你：开源模型在真实生产代码中能低成本挖出高危漏洞，值得在预算有限时优先尝试。
Ramp Labs 在自家后端部署了 1 万个 AI 智能体进行安全测试，发现开源模型（Kimi K2.6 和 DeepSeek V4 Pro）在 Fireworks 上运行，能以比 GPT 5.5 低约 5 倍的 token 成本，成功发现 7 个高危漏洞。Ramp 表示如果重做，会更依赖开源模型。这为开源权重模型在安全领域的价值提供了有力证据，表明在 GPU 资源稀缺的背景下，成本和效果需要平衡。
Another proof point for the open-weights thesis. From @RampLabs: "If we built this again, we'd lea...
Another proof point for the open-weights thesis. From @RampLabs : "If we built this again, we'd lean more on open-weight models." Ramp pointed 10K agents at their own backend. Kimi K2.6 and DeepSeek V4 Pro on Fireworks recovered 7 high-severity vulnerabilities at ~5x lower cost per token than GPT 5.5. In a world of scarce (GPU) resources, both cost and value matter. AI leaders today are finding the right balance btw open & closed. "On balance, the hard cases reward a frontier model, but cheaper open-weight models still find high-severity security issues in production code at meaningful rates." Ramp Labs @RampLabs x.com/i/article/2059… 🔗 View Quoted Tweet 💬 3 🔄 2 ❤️ 21 👀 4268 📊 6 ⚡