Benchmarking Secure-and-Functional Remediation and How Snyk Agent Fix Lifts Frontier-Model Fix Rates by over 14%

We benchmarked how well leading models produce vulnerability fixes that are both secure and functional, across ~150 real vulnerable code samples in JavaScript, Java, and Python. We ran each model on its own and with Snyk Intelligence (the new agentic Agent Fix architecture). The headline findings: Out-of-the-box frontier models cluster at 72–75%. Gemini 3.1 Pro, Claude Sonnet 4.6, and Claude Opus 4.6 all land within a few points of each other. On secure-and-functional fixing, model choice barely moves the number. Out-of-the-box frontier models cluster at 72–75%. Gemini 3.1 Pro, Claude Sonnet 4.6, and Claude Opus 4.6 all land within a few points of each other. On secure-and-functional fixing, model choice barely moves the number. Snyk Intelligence moves the same models clear of that cluster. Opus 4.6 rises from 74.6% to 85.4%, a 10.8-point gain (14.48% more samples fixed). The variable is the security context, not the model. Snyk Intelligence moves the same models clear of that cluster. Opus 4.6 rises from 74.6% to 85.4%, a 10.8-point gain (14.48% more samples fixed). The variable is the security context, not the model. The lift is largest where the model is weakest. Opus alone fixes only 64% of Python samples; with Snyk Intelligence,…