OpenAI’s open-weight models are the gpt-oss family of general-purpose reasoning models and the gpt-oss-safeguard family of policy-based safety classifiers. You can download and run their weights on infrastructure you control or through a hosting provider, but they are not available in ChatGPT or the OpenAI API. The key choices are which family fits your task, what hardware or hosting route you can support, and what safeguards your application needs.
What are OpenAI’s open-weight models?
OpenAI’s current catalog lists two families: gpt-oss, for general reasoning and agentic workflows, and gpt-oss-safeguard, for safety classification. The core gpt-oss models are text-only language models designed to reason and use tools such as web search and Python execution. OpenAI describes adjustable low, medium, and high reasoning effort, fine-tuning, function calling, and structured outputs as supported capabilities. OpenAI’s open-model catalog lists the 120B and 20B sizes in both families.
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“Open-weight” is the more precise description than “fully open source”: the trained weights are downloadable, but that does not mean all training data, tools, or hosting infrastructure are open. OpenAI says the weights are free to download under Apache 2.0, subject to its gpt-oss usage policy. The license permits broad use, modification, redistribution, and commercial use subject to that policy. OpenAI’s Help Center explains the access and license terms.
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OpenAI introduced gpt-oss-120b and gpt-oss-20b on August 5, 2025. The 120b model has 117 billion total parameters, with 5.1 billion active per token; the 20b model has 21 billion total parameters, with 3.6 billion active per token. Both use a mixture-of-experts transformer architecture and have a stated 128,000-token context length. OpenAI’s launch announcement describes the model architecture and intended uses.
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gpt-oss-safeguard: policy-based safety classification
OpenAI describes gpt-oss-safeguard as a research-preview pair for trust-and-safety classification. Rather than applying one fixed classification policy, the models interpret a policy supplied by the developer at inference time and classify messages, completions, or conversations against it. OpenAI recommends the core gpt-oss models—not safeguard—for general chat and agent applications. See the gpt-oss-safeguard announcement and gpt-oss launch announcement.
Can I run gpt-oss locally?
Yes. OpenAI’s weights can be run on hardware you control or through a hosting provider. OpenAI names vLLM, Ollama, and llama.cpp as common inference options, and points developers to the weights on Hugging Face, a GitHub repository, and setup guides in the OpenAI Cookbook. The exact setup depends on your chosen runtime and hardware; consult the Help Center and launch page for the available resources.
What GPU do I need to run gpt-oss?
OpenAI’s published deployment guidance gives different memory expectations for the two sizes. Its August 2025 launch page says gpt-oss-20b requires 16 GB of memory, while gpt-oss-120b can run within 80 GB. The developer model page says gpt-oss-120b fits on one H100 GPU. These are OpenAI specifications, not independent hardware tests; actual memory use and performance can vary with runtime, quantization, workload, and configuration. A listed memory requirement is not a guarantee of a particular response speed or throughput. See the launch specifications and the gpt-oss-120b developer page.
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At a glance
| Model | Purpose | OpenAI-stated memory guidance | OpenAI-stated context |
|---|---|---|---|
| gpt-oss-20b | General reasoning and agent workflows | 16 GB; OpenAI launch guidance, August 2025 | 128,000 tokens; OpenAI launch page, August 2025 |
| gpt-oss-120b | General reasoning and agent workflows | Within 80 GB; OpenAI launch guidance, August 2025. Developer page says it fits on one H100. | 128,000 tokens; OpenAI launch page, August 2025 |
| gpt-oss-safeguard-20b | Policy-based safety classification | Not stated in the cited OpenAI material | Not stated in the cited OpenAI material |
| gpt-oss-safeguard-120b | Policy-based safety classification | Not stated in the cited OpenAI material | Not stated in the cited OpenAI material |
OpenAI lists the safeguard family in corresponding 20B and 120B sizes, but the cited safeguard material does not establish separate hardware or context specifications for those models. Do not assume the core gpt-oss figures apply to safeguard without checking the model’s current documentation.
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Can I use gpt-oss through ChatGPT or the OpenAI API?
No. OpenAI states: “These models are not served in the OpenAI API and do not appear in ChatGPT.” You must run the weights yourself or use a hosting provider that offers them; downloading the weights does not add them to an OpenAI account or API endpoint. OpenAI Help Center.
Is self-hosting free, private, or cheaper?
The weight download has no price according to OpenAI, but operating the model can still incur compute, storage, and hosting costs, as well as maintenance and upgrade work. OpenAI says whether self-hosting costs less than API use depends on the workload and those operational costs; there is no universal cost advantage established by the published material.
Self-hosting gives you control over the infrastructure, but privacy depends on how you deploy and manage it. OpenAI says it does not receive data sent to self-hosted models unless a user shares it or uses a managed hosting partner. A third-party hosting provider may have its own data handling terms, so review those terms and your own deployment design before sending sensitive information. OpenAI Help Center.
How should I compare the models and deployment routes?
Choose based on the actual task and the system you can operate, rather than model size alone.
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- Match the purpose: choose core gpt-oss for general reasoning, chat, or tool-using agents; consider safeguard when you need policy-driven safety classification and can provide and evaluate the policy.
- Check memory and runtime fit: compare the model’s published memory guidance with the hardware and inference stack you intend to use. Test the actual quantization, context length, and workload rather than assuming a specification guarantees production throughput.
- Choose the control model: self-managed infrastructure gives your team responsibility for deployment and operations; managed hosting shifts some infrastructure work to a provider but adds a third party to the data path. Confirm current model support and provider terms directly.
- Estimate total workload cost: include compute, storage, hosting, maintenance, and upgrades, then compare against the relevant alternative for your usage pattern.
- Evaluate on your own tasks: OpenAI publishes benchmark figures and comparisons, but those do not establish how a model will perform on your prompts, tools, latency targets, or domain-specific requirements.
OpenAI’s catalog reports MMLU scores of 90.0 for gpt-oss-120b and 85.3 for gpt-oss-20b, GPQA Diamond scores of 80.1 and 71.5, and AIME 2024 scores of 96.6 and 96.0, respectively. These are figures published by OpenAI in its catalog as accessed October 4, 2026—not independent evaluations. Use them as vendor-reported results, not as a substitute for testing your own workload. OpenAI open-model catalog.
What safety responsibilities come with open weights?
Open weights make downstream modification possible, but they also mean OpenAI cannot centrally update or revoke copies after release. OpenAI’s model card says downstream developers and enterprises may need additional safeguards to reproduce protections found in OpenAI-hosted products. The same model card reports that the default gpt-oss-120b did not meet OpenAI’s indicative High-capability thresholds in tracked biological and chemical, cyber, and AI self-improvement categories; it also says the Safety Advisory Group reviewed adversarial fine-tuning work and concluded that the fine-tuned model did not reach High capability in biological and chemical or cyber risk. These are bounded OpenAI evaluations of particular model versions and conditions, not proof that every downstream deployment or modification is safe. OpenAI’s gpt-oss model card.
OpenAI also warns against showing chain-of-thought directly to end users: it may contain hallucinated or harmful content, or language inconsistent with OpenAI’s standard safety policies. Treat model outputs as untrusted until they have been reviewed and shaped for the application, and implement safeguards appropriate to your users and use case. OpenAI launch announcement.
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