What sets gpt-oss models apart?

 title: 'Figure 3: We evaluate AIME and GPQA using the three different reasoning modes (low , medium , high) and plot accuracy against the average CoT + Answer length. We find that there is smooth test-time scaling of accuracy when increasing the reasoning level.'

The gpt-oss models, specifically gpt-oss-120b and gpt-oss-20b, are notable for their open-weight reasoning capabilities and compatibility with OpenAI's Responses API. They are designed for agentic workflows, featuring strong instruction following, tool use like web search and Python code execution, and adjustable reasoning efforts for varied task complexities. These models are customizable, support full chain-of-thought reasoning, and provide structured outputs, differentiating them from proprietary models which may not offer such flexibility or access to their weights[1].

Additionally, safety is a foundational focus for gpt-oss models, as they present a different risk profile compared to proprietary models. OpenAI emphasizes that developers must implement extra safeguards when using these models to replicate the protections built into their API and products[1].

Space: Let’s explore the gpt-oss-120b and gpt-oss-20b Model Card