GPT-OSS is OpenAI’s series of open-weight language models designed for powerful reasoning, agentic tasks, and versatile developer use cases. While the official documentation doesn’t explicitly define what “OSS” stands for, based on the context and OpenAI’s descriptions, it appears to refer to “Open Source Software” or similar terminology related to open-weight models. These models are released under the permissive Apache 2.0 license, allowing developers to build freely without copyleft restrictions or patent risk.
The GPT-OSS models represent a significant departure from OpenAI’s typical approach of keeping their advanced models proprietary. Both models were trained on OpenAI’s harmony response format and should only be used with the harmony format as it will not work correctly otherwise. Unlike traditional closed-source models that only provide API access, GPT-OSS models give developers complete control over deployment, customization, and inference. The models feature configurable reasoning effort levels, full chain-of-thought access for debugging purposes, native agentic capabilities including function calling and web browsing, and support for fine-tuning to specific use cases.
The models use native MXFP4 quantization for the MoE layer, which enables the larger model to run on a single H100 GPU and the smaller model to run within 16GB of memory. This technical approach makes them particularly accessible for developers who want to run powerful language models without requiring extensive infrastructure or dealing with API rate limits.
For more detailed information, see: GPT-oss vs o4-mini: Edge-Ready, On-Par Performance — Dependable, Not Mind-Blowing