Since the launch of GPT‑4, OpenAI has collaborated with a range of external partners to test and assess our models. Broadly, our third-party collaborations take three forms:

This blog outlines how we use each of these forms of external assessment, why they matter, how they have shaped deployment decisions, and the principles we use to structure these collaborations. In the spirit of transparency, we’re also sharing more about the confidentiality and publication terms that govern our collaborations with third party testers.

Third party assessors add an independent layer of evaluation alongside our internal work, strengthening rigor and providing additional protections against self-confirmation. Their input provides additional evidence alongside our own assessments, helping to inform responsible deployment decisions for powerful systems.

We also see third party assessments as part of building a resilient safety ecosystem⁠. Our teams conduct extensive internal testing across capability and risk areas, but independent organizations bring additional perspectives and methodological approaches. We work to support a diverse group of qualified assessor organizations who can regularly evaluate frontier models alongside us.

Finally, we aim to be transparent about how this input helps shape our safety process. We regularly make third party assessments public—for example, by including summaries of pre-deployment evaluations in system cards, and supporting assessor organizations in publishing more detailed work following confidentiality and accuracy review. This transparency builds trust by showing how external input shapes our capability evaluations and safeguards.