Cisco releases small open-weight models to find code vulnerabilities

Cisco Foundation AI released Antares, open-weight 350M- and 1B-parameter models trained to locate known vulnerabilities in codebases. The 1B model scored 0.209 File F1 on a new vulnerability-localization benchmark, beating GLM-5.2 (753 billion parameters) and Gemini 3 Pro, and a full 500-task benchmark sweep costs under $1 on one H100 versus $141 for GPT-5.5.

Why it matters: This is a clear case of a small, specialized post-trained model beating much larger general-purpose models on a narrow task at a fraction of the cost, reinforcing the trend that targeted fine-tuning can outcompete frontier-scale models for well-defined enterprise problems like security scanning.