- OpenAI’s GPT-OSS models – gpt-oss-20b and gpt-oss-120b – are now accessible through Amazon SageMaker AI, offering a powerful way to tailor these advanced language models to your specific needs. Released on August 5, 2025, this integration simplifies the process of fine-tuning, allowing you to leverage their capabilities for coding, scientific analysis, and mathematical reasoning. These pre-trained, text-only Transformer models are built on a Mixture-of-Experts (MoE) architecture, activating only a subset of parameters per token – a key factor in delivering high reasoning performance while minimizing compute costs. The models support a 128,000 context length, adjustable reasoning levels (low/medium/high), chain-of-thought (CoT) reasoning with audit-friendly traces, structured outputs, and tool use to support agentic-AI workflows. GPT-OSS Models represent a significant step forward in accessible AI research and development. The ability to fine-tune these models on AWS SageMaker provides unparalleled flexibility and control. Furthermore, the documentation provides comprehensive details on these processes.
Model Specifications
The following table summarizes the key specifications for both GPT-OSS models: | Model | Layers | Total Parameters | Active Parameters Per Token | Total Experts | Active Experts Per Token | Context Length |
| —————- | —— | —————- | ————————— | ————- | ————————- | ————– |
| openai/gpt-oss-120b | 36 | 117 billion | 5.1 billion | 128 | 4 | 128,000 |
| openai/gpt-oss-20b | 24 | 21 billion | 3.6 billion | 32 | 4 | 128,000 |
The different parameter counts highlight the trade-offs between performance and computational cost. The 120B model offers superior reasoning capabilities but requires significantly more resources than the 20B version. Both models leverage the MoE architecture to optimize for efficiency. This is a critical element in making these powerful language models accessible to a wider range of users.
Getting Started
You can deploy these models using Amazon SageMaker JumpStart and also leverage Amazon Bedrock for easy access. The integration with SageMaker simplifies the deployment process, allowing you to focus on fine-tuning and experimentation. The seamless workflow provided by Bedrock further streamlines the process. This makes it easier than ever to harness the power of GPT-OSS Models for your specific applications. Consider starting with the 20B model if resource constraints are a concern – its performance is still impressive, particularly for many common tasks.
Key Features and Capabilities
The GPT-OSS models boast several key features that set them apart: 128,000 context length, adjustable reasoning levels (low/medium/high), chain-of-thought (CoT) reasoning with audit-friendly traces, structured outputs, and tool use to support agentic-AI workflows. These capabilities provide a significant advantage in complex problem-solving scenarios.
Safety and Robustness
The GPT-OSS models have undergone safety-focused training and adversarial fine-tuning evaluations to assess and strengthen robustness against misuse. Both models have undergone rigorous testing for safety and robustness, ensuring responsible AI deployment. The documentation outlines the specific methodologies used – a testament to OpenAI’s commitment to ethical AI development. The ongoing evaluation of these models is crucial for maintaining trust and reliability. Investing time in understanding how they were trained provides critical context when interpreting their outputs.
Summary: OpenAI’s GPT-OSS models are now available on AWS through Amazon SageMaker AI and Amazon Bedrock. These pre-trained, text-only Transformer models are built on a Mixture-of-Experts (MoE) architecture that delivers high reasoning performance while reducing compute costs. This makes them a highly desirable option for those looking to develop advanced AI applications. Further exploration of these GPT-OSS Models is highly recommended.
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