The GPT-OSS family of models natively supports 128k of ultra-long contextual processing, which is critical for applications that need to process complex documents or long conversations. The models implement an innovative chained reasoning feature that offers low, medium and high inference intensity options, allowing developers to flexibly adjust the balance of performance and latency according to task requirements. The high-intensity mode is particularly suited to complex tasks that require in-depth analysis, such as mathematical reasoning or scientific problem solving, while the low-intensity mode is suitable for instant Q&A scenarios that require a fast response time.
The implementation of Chained Reasoning employs a unique staged processing mechanism that ensures optimal results at different reasoning intensities. This feature design gives developers unprecedented flexibility in handling different types of tasks, allowing them to make precise trade-offs between computational resource consumption and inference quality according to actual needs.
This answer comes from the articleGPT-OSS: OpenAI's Open Source Big Model for Efficient ReasoningThe