Meta: Llama Guard 4 12B

Meta's Llama Guard 4 12B, a Llama 4 Scout-derived model fine-tuned for content safety classification, demonstrates exceptional speed and cost efficiency. It consistently ranks among the fastest models and offers highly competitive pricing, placing it in the Infinityth percentile across six benchmarks for both speed and price. This model is specifically designed for content moderation, classifying LLM inputs and responses against the MLCommons hazards taxonomy, and supports multimodal capabilities including text and multiple images across English and other languages. While its core function is safety classification, the provided benchmark results, which appear to test general-purpose LLM capabilities, show a mixed performance. Llama Guard 4 achieved a notable 72.0% accuracy in Reasoning, indicating a strong ability in complex problem-solving, and did so with excellent cost and duration efficiency within that category. However, it scored 0.0% accuracy across Coding, Instruction Following, Email Classification, Ethics, and General Knowledge benchmarks. This suggests that while highly optimized for its specialized safety classification task, it is not intended or optimized for general-purpose generative or analytical tasks typically associated with large language models. Its integration into the Llama Moderations API further solidifies its role as a robust safety tool.