Malte Ubl 实测发现 Kimi K3 在网络安全漏洞检测上性价比吊打 GPT 5.6，适合做持续扫描，省大钱。
Kimi K3 在 Deepsec.sh 私有网络安全基准测试中达到最佳性价比，召回率与价格平衡。GPT 5.6 召回率最高但单次运行成本是 Kimi K3 的 7 倍以上。GLM 5.2 价格比 Kimi K3 低 40%，召回率接近。Fable 5 完全拒绝执行，无法用于安全分析。推荐用 GPT 5.6 做一次性基线，用 Kimi K3 做持续分析。
One of the strongest models for cybersecurity, catching the most vulnerabilities for the price. http...
One of the strongest models for cybersecurity, catching the most vulnerabilities for the price. x.com/cramforce/stat… Malte Ubl @cramforce We ran Kimi K3 on a private cybersecurity benchmark. TL;DR: Kimi K3 is the workhorse for cyber security tasks at great recall/precision/price. GPT 5.6 is best recall/precision but at 7x higher cost per run. For context, Deepsec.sh is an open-source cyber harness designed for finding vulnerabilities in large codebases. The eval runs deepsec on an undisclosed open-core application at a git sha before a large number of security issues were fixed. This is a secret eval that cannot be directly benchmark-maxxed. S-Tier: GPT 5.6 Sol: By far the most thorough analysis, but coming in at over 7x the price of the runner up. Best price/recall: Kimi K3. Next tier of recall at a good price Best price at good recall: GLM 5.2 (40% lower price than Kimi K3) GPT 5.5: Only recommended with subscription or high-discount API price. Similar recall to Kimi at much higher list price. Opus 4.8: Only recommended with subscription or high-discount API price. Similar recall to GLM 5.2 at much higher list price. Fable 5: 100% refusal rate. Cannot be used for security analysis. Sol on a large code base will quickly get into 6-figure pricing. This is still affordable relative to the risk of letting security issues unfixed or paying bug bounties. I'd recommend using Sol for a one-time baseline and then using Kimi K3 for continuous analysis. When using open-weight models, make sure to use an inference vendor that supports zero data retention. 🔗 View Quoted Tweet 💬 0 🔄 0 ❤️ 4 👀 1240 ⚡