hen you think of OpenAI, it's unlikely that you associate it with open models, despite the company's name.

However, you may not be aware that OpenAI offers several open-weight models that provide important capabilities to developers and enterprises.

Two days before the company released GPT-5 in August 2025, it quietly announced two open models, GPT-OSS-120B and GPT-OSS-20B. In technical terms, these are Mixture-of-Experts (MoE) models that use chain-of-thought reasoning. That makes them very good at math, programming, and research tasks. The 120B and 20B designations refer to the number of parameters in each model.

Despite flying under the radar, the GPT-OSS models are generally considered state-of-the-art open-weight models and operate under the permissive Apache 2.0 license, which gives developers and enterprises lots of leeway to customize them.

OpenAI told The Deep View that the company released these models because, due to regulatory and security requirements, some organizations must run workloads locally rather than in the cloud. That's where open models have to play a critical role, as they can run in the company's own hardware and data center. In practice, OpenAI reported that many of these organizations operate a hybrid environment, with some workloads running on GPT-OSS and others running on OpenAI's latest frontier models in the cloud.

The two OpenAI models serve different purposes:

• GPT-OSS-20B: The smaller of the two models is designed to run on local hardware, such as a laptop. In fact, it can run in 16 gigabytes of memory and works on nearly any laptop less than five years old. It can save you money in token costs by running locally while running very fast. It's also more secure and lets you run without an internet connection. It's the more popular of the two on Hugging Face.
• GPT-OSS-120B: The larger of the two models offers configurable levels of reasoning and is powerful enough to rival proprietary models such as OpenAI's o4-mini. But it's efficient enough to run on a single 80GB GPU in a data center. It's considered incredibly fast and compute-efficient. It's so efficient that it can even run on a MacBook Pro that has enough memory.

When The Deep View met with the team at OpenAI behind the GPT-OSS models and asked why the company's open models don't get more attention, the team shared a key insight. The broader open-model community tends to favor models that are easy to fine-tune with supervised learning. GPT-OSS behaves more like an OpenAI reasoning model with web search, which makes it more capable but harder to customize. It rewards reinforcement learning instead.

Also, not to be forgotten, three months after releasing these two open models, OpenAI released two more aimed at safety: GPT-OSS-Safeguard-120B and GPT-OSS-Safeguard-20B. These models let you bring your own safety policies and enterprise guardrails, enabling auditable safety behavior.

Our Deeper View

It's a little puzzling that the GPT-OSS models aren't better known. Even more recent open models, like Nvidia Nemotron and Google Gemma models, have been getting much more attention. And of course, the Chinese model makers, such as DeepSeek, Qwen, Kimi and Z.ai, are perhaps the best known in the open model space. But with AI costs soaring as agents gobble up so many tokens, and with a shortage of data center compute to meet AI's growing demands, I expect we'll hear a lot more about running AI workloads locally, since it can save on both costs and compute. Open models play a key role in the world, and the GPT-OSS models could get their moment in the sun.