Frontier model evaluation is the systematic assessment of the capabilities, limitations, and risks of the most advanced AI systems, including dangerous-capability and safety testing. It uses benchmarks, red-teaming, and threat-model-driven evaluations to inform deployment decisions and regulation. Such evaluation is increasingly required by AI governance regimes before high-capability models are released.
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- Evaluations span standard capability benchmarks, adversarial red-teaming, and assessments of dangerous capabilities such as cyber-offence, biological uplift, and autonomous replication. Results feed safety cases and responsible-scaling policies that set thresholds for additional safeguards. Independent third-party evaluation and pre-deployment testing are central to frontier AI safety frameworks.