OpenAI releases gpt-oss, its first language models since GPT-2

OpenAI has released gpt-oss-120b and gpt-oss-20b, two open-weight language models that can be downloaded, modified and run outside the company’s servers. It marks OpenAI’s return to this format for language models since GPT-2, which launched in 2019.

The decision matters because it contrasts with the strategy that made OpenAI one of the industry’s central companies: GPT-3, GPT-4, the o family and ChatGPT models have been distributed as closed services. With gpt-oss, developers, companies and government agencies can run the model on their own infrastructure, tune its behavior with specialized data and avoid having to send every query to an external API.

Two sizes for local deployment

The models are distributed under the Apache 2.0 license, a permissive license that allows commercial use, modification and redistribution. That does not mean they are completely transparent software—OpenAI has not published the training data or the full development process—but it does provide the trained parameters, the essential component needed to run and adapt the model.

The larger model, gpt-oss-120b, has 117 billion parameters in total and is designed to run on a single GPU with 80 GB of memory. The smaller gpt-oss-20b has 21 billion parameters and can run on devices with 16 GB of memory, bringing it within reach of powerful personal computers and more affordable local deployments.

The key is their mixture-of-experts architecture, or MoE. Rather than activating every parameter for each word or text fragment, the model selects only some of its internal components. The larger model activates 5.1 billion parameters per token, while the smaller one activates 3.6 billion. This design lowers inference costs—the work required to generate a response—without sacrificing much greater total capacity.

Both models support context windows of up to 128,000 tokens, enough to process lengthy documents, code repositories or long conversations.

Reasoning, tools and comparative results

OpenAI places gpt-oss-120b close to o4-mini in its reasoning tests and says gpt-oss-20b achieves results similar to o3-mini on standard evaluations. The company highlights tests covering mathematics, competitive programming, general knowledge, health and tool use.

Both models can adjust reasoning effort across low, medium and high levels. In practice, developers can request a quick answer for a simple task or let the system spend more computing time on a complex problem. They are also designed to call external functions and tools, such as web searches or code execution—a relevant capability for agents that do more than simply generate text.

The comparisons should be read with caution. The results come from evaluations published by OpenAI, and benchmarks measure narrowly defined tasks rather than every enterprise use case. A model that performs well on AIME, Codeforces or HealthBench is not automatically validated for diagnosing patients, making financial decisions or automating processes without supervision.

OpenAI explicitly warns that the models do not replace health care professionals and are not intended to diagnose or treat diseases.

Open weights do not mean zero risk

Openness makes audits and adaptations easier, but it also reduces the publisher’s control over how the model is used later. Once the weights have been downloaded, an organization can run them without relying on OpenAI’s infrastructure and create versions tailored to specific fields.

The company says it subjected gpt-oss-120b to safety evaluations, including an adversarially fine-tuned version tested under its Preparedness Framework, the framework it uses to assess risks from advanced capabilities. It has also published a model card and a technical paper on those tests.

Another unusual feature is access to the model’s chain of thought. OpenAI says it has not applied direct supervision to that internal chain, with the aim of allowing researchers to study signs of deception, misuse or failures during reasoning. The company nevertheless recommends not showing it directly to end users: it may contain errors, harmful content or instructions that should not appear in the visible response.

A new option for companies and developers

The announcement does not replace OpenAI’s closed offering. The company itself presents gpt-oss as an alternative for those who prioritize cost, latency, customization or data control, while its proprietary models remain available through the API.

For a company with strict privacy requirements, hosting a model within its own network may matter more than accessing the most powerful system available. For independent developers, the 20-billion-parameter model opens the door to experimentation without a variable bill for every query. And for the open-model ecosystem, OpenAI’s entry increases competition with offerings such as Llama, Qwen, Mistral and DeepSeek.

The decisive test will come outside the benchmarks: real deployment costs, quality in languages other than English, ease of fine-tuning and behavior in specialized applications. But the release breaks six years of a clear divide between the lab’s open research and its commercial language models.