We’d like to introduce OpenAI’s latest development in open source AI models: the gpt-oss series. These two open-weight language models, gpt-oss-120b and gpt-oss-20b, have been tested by OpenAI to deliver impressive performance across logic tasks, agent capabilities, and developer usage. Available under the flexible Apache 2.0 license, the gpt-oss models are OpenAI’s first open-weight language models since GPT-2, and are designed to make AI more widely accessible and drive innovation.

Here’s a summary of why you should check out these models:

- Two versions, for different purposes

- gpt-oss-120b: This larger model has 117 billion parameters and is designed to run on a single 80GB GPU (such as the NVIDIA H100 or AMD MI300X). It is well suited for production environments, general-purpose and high-thinking tasks.

- gpt-oss-20b: This smaller model has 21 billion parameters and requires only 16 GB of memory, making it ideal for low-latency, local or specialized applications, even on consumer hardware.

- Open-source and permissive license:

- The gpt-oss models are released as open-source models. The Apache 2.0 license allows for free experimentation, customization and commercial use, without copyleft restrictions or patent risks.

- Advanced thinking capabilities:

- The models support adjustable thinking effort (low, medium, high), which can be optimized according to the task requirements and latency expectations.

- Full Chain-of-Thought (CoT) access is provided. This allows detailed insight into the thinking process of the model, which helps in debugging and increases confidence in the outputs. It is important to note that the content of the CoT is not guaranteed to be security compliant and should not be shown directly to end users.

- The gpt-oss-120b model outperforms OpenAI o3-mini on most benchmarks and approaches the capabilities of OpenAI o4-mini in areas such as competitive mathematics or health queries.

- Agent capabilities and device usage:

- Models are natively capable of calling functions, browsing the web, executing Python code, and generating structured output.

- They are able to use built-in browser and Python tools to perform their tasks more efficiently.

- Efficiency and hardware support:

- The use of MXFP4 quantization significantly reduces the memory footprint of the models. This allows gpt-oss-120b to run on a single 80 GB GPU and gpt-oss-20b to run on just 16 GB of memory.

- Wide range of runtime environments supported, including Transformers, vLLM, Ollama, LM Studio, PyTorch, Triton, and Apple Metal.

- Fine-tuning:

- Both gpt-oss models can be fully fine-tuned for specific use cases. gpt-oss-20b can even be fine-tuned on consumer hardware.

- Harmony response format:

- The models are trained exclusively using and work properly with the OpenAI Harmony response format. This format defines the structure of conversations, reasoning outputs, and function calls.

- Security focus:

- OpenAI considers security to be of fundamental importance. The models have undergone extensive security training and evaluation, including filtering out harmful data during pre-training, and are resistant to jailbreak attacks.

- Wide availability:

- Weights are freely available for download from Hugging Face.

Links

Introducing gpt-oss: https://openai.com/index/introducing-gpt-oss/Technikai dokumentáció: https://cdn.openai.com/pdf/419b6906-9da6-406c-a19d-1bb078ac7637/oai_gpt-oss_model_card.pdfGitHub: https://github.com/openai/gpt-ossHugging Face: https://huggingface.co/collections/openai/gpt-oss-68911959590a1634ba11c7a4LM Studio: https://lmstudio.ai/blog/gpt-ossOllama: https://ollama.com/library/gpt-ossgpt-oss playground: https://gpt-oss.com/OpenAI Harmony Response Format: https://cookbook.openai.com/articles/openai-harmony