OpenAI Returns to Open Source with gpt-oss Release

OpenAI has released gpt-oss-120b and gpt-oss-20b—two state-of-the-art open-weight language models that deliver strong real-world performance at low cost. Available under the flexible Apache 2.0 license, these models outperform similarly sized open models on reasoning tasks, demonstrate strong tool use capabilities, and are optimized for efficient deployment on consumer hardware.

The Models

The gpt-oss series comprises two distinct models designed for different use cases:

gpt-oss-120b: A 117-billion parameter model with 5.1 billion active parameters, designed for production, general-purpose, high reasoning use cases that fits into a single H100 GPU. This model achieves near-parity with OpenAI o4-mini on core reasoning benchmarks while running efficiently on a single 80 GB GPU.

gpt-oss-20b: A 21-billion parameter model with 3.6 billion active parameters, optimized for lower latency and local or specialized use cases. This smaller model delivers similar results to OpenAI o3‑mini on common benchmarks and can run on edge devices with just 16 GB of memory, making it ideal for on-device use cases, local inference, or rapid iteration without costly infrastructure.

Key Features

Both models share several important characteristics that set them apart in the open-source AI landscape:

Apache 2.0 License: Build freely without copyleft restrictions or patent risk—ideal for experimentation, customization, and commercial deployment. This represents a more permissive licensing approach compared to other major open-source models like Meta's Llama series.

Configurable Reasoning: Users can easily adjust the reasoning effort (low, medium, high) based on specific use cases and latency needs. The reasoning level can be set in system prompts, allowing for optimized performance across different applications.

Harmony Response Format: Both models were trained on OpenAI's harmony response format and should only be used with this format as they will not work correctly otherwise.

Technical Implementation

The models were trained using a mix of reinforcement learning and techniques informed by OpenAI's most advanced internal models, including o3 and other frontier systems. They feature native quantization support using MXFP4 format specifically applied to MoE (Mixture of Experts) weights, enabling efficient deployment on standard hardware.

The models are available through multiple platforms including Hugging Face, GitHub, and various inference providers, with support for popular frameworks like Transformers, vLLM, Ollama, and llama.cpp.

Safety and Security

Safety is foundational to OpenAI's approach to releasing all models, and is of particular importance for open models. In addition to running the models through comprehensive safety training and evaluations, OpenAI also introduced an additional layer of evaluation by testing an adversarially fine-tuned version of gpt-oss-120b under their Preparedness Framework.

OpenAI's Safety Advisory Group reviewed testing that showed even with robust fine-tuning that leveraged OpenAI's field-leading training stack, gpt-oss-120b did not reach High capability in Biological and Chemical Risk or Cyber risk.

Strategic Context

This represents OpenAI's first fully open-source language model release since GPT-2 in 2019, more than six years ago, and three years before the release of ChatGPT. The timing appears strategic, as open source competitors, beginning with the release of the impressively efficient DeepSeek R1 by the Chinese AI division of the same name in January 2025, are offering near parity on performance benchmarks to paid proprietary models, for free, with fewer implementation restrictions for enterprises and end users.

Availability and Deployment

These models are not served through the OpenAI API and are not available in ChatGPT. Instead, they run on infrastructure users control or through hosting providers, giving organizations complete control over their AI deployments while maintaining data residency and privacy.

The release marks a significant shift in OpenAI's strategy, balancing their successful commercial API business with renewed commitment to the open-source community that helped establish their reputation in artificial intelligence research.