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Choose a hosted OpenAI model if you want a managed service and do not want to operate inference infrastructure. Consider an open-weight model if deployment control, customization, or running on infrastructure you control matters enough to take on compute, maintenance, and safety responsibilities. Neither approach is a universal winner: compare specific models on your actual tasks, costs, and constraints.
“Open-source” is common shorthand in this comparison, but OpenAI describes its gpt-oss models as open-weight. Their trained weights are publicly available under Apache 2.0 and an accompanying usage policy; that does not mean every tool or part of the surrounding infrastructure is open.
How hosted and open-weight models differ
The key difference is not simply which model produces better answers. It is who operates the system and who takes responsibility for deployment. With a hosted model, a provider runs the inference service. With an open-weight model, you can download the weights and choose how to deploy them, but you or your hosting provider must supply and manage the environment.
| Decision area | Hosted OpenAI model | Open-weight model |
|---|---|---|
| Hosting and control | The provider operates the service; you use it through the available product or interface. | You can deploy the model on infrastructure you control or use a managed hosting partner. Self-hosting means you manage the deployment. |
| Cost | Account for the applicable service or API charges. The exact amount depends on the product and usage. | OpenAI says gpt-oss weights are free to download, but compute, storage, and any third-party hosting charges remain the user’s responsibility. |
| Privacy and data handling | Check the specific product’s data handling terms, including where processing occurs and what may be retained. | For self-hosted gpt-oss on infrastructure you control, OpenAI says it does not receive or process submitted data unless you share it with OpenAI or use a managed hosting partner. That does not establish how a separate hosting provider handles data. |
| Hardware and operations | You do not need to provide the inference hardware for the hosted service. | You need an appropriate runtime and enough compute and memory for the particular model, plus people or processes to maintain the service. |
| Customization and license | Customization depends on the hosted product’s available features and terms. | Check the model’s license and usage policy, commercial-use terms, fine-tuning options, and whether the rest of the software stack is open. |
| Safety and support | The provider operates its service and its safeguards, subject to that product’s terms. | You take on more responsibility for safeguards and deployment. OpenAI says its support does not cover implementation or debugging of self-hosted or third-party-hosted gpt-oss setups. |
These are deployment trade-offs, not fixed properties of every model. A hosted service can have varying terms and capabilities, and open-weight models differ in license, hardware needs, and tooling.
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What OpenAI’s gpt-oss example shows
OpenAI’s 2025 launch material describes gpt-oss-120b and gpt-oss-20b as text-only reasoning models with instruction-following and tool-use capabilities, including use of web search and Python execution. OpenAI says the models are released under Apache 2.0 with its usage policy. This is a specific OpenAI example, not a description of every open-weight model.
Hardware claims are model-specific
OpenAI says gpt-oss-20b can run on edge devices with 16 GB of memory and that gpt-oss-120b can run efficiently in an 80 GB GPU configuration. These are the publisher’s examples for those models. They do not establish a general hardware minimum, guarantee a particular speed, or show that any device meeting the memory figure will provide a good experience. Check the exact model, runtime, workload, and expected concurrency before choosing hardware.
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OpenAI’s published benchmark results
The table reproduces figures that OpenAI published in 2025 for the named models and evaluations. They are vendor-reported results, not an independent ranking. Do not assume scores are directly comparable unless benchmark setup, prompting, scoring, and model versions align.
| Evaluation | gpt-oss-120b | gpt-oss-20b | OpenAI o3 | OpenAI o4-mini |
|---|---|---|---|---|
| MMLU | 90.0 | 85.3 | 93.4 | 93.0 |
| GPQA Diamond | 80.1 | 71.5 | 83.3 | 81.4 |
| Humanity’s Last Exam | 19.0 | 17.3 | 24.9 | 17.7 |
| AIME 2024 | 96.6 | 96.0 | 95.2 | 98.7 |
| AIME 2025 | 97.9 | 98.7 | 98.4 | 99.5 |
The figures vary by evaluation: for example, the listed gpt-oss scores exceed o3 on AIME 2024 but are lower on MMLU and GPQA Diamond. That variation is a reason to test the work you actually need, not evidence that one model is best overall. The published comparison alone does not determine which model will work best for an individual workload.
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How to compare models for your workload
Use the same representative tasks to evaluate each candidate. If you rely on published results, record who ran the evaluation and which model version and date it covers; do not treat vendor benchmark figures as a substitute for your own use case.
- Define the job. List the tasks you need the model to perform, such as drafting, coding, reasoning, information extraction, or tool use. Include the input types, expected answer format, and how often the work occurs.
- Build a representative test set. Collect realistic prompts and examples, including difficult cases and examples where a wrong answer would matter. Use the same set for every candidate.
- Set scoring criteria before testing. Decide what counts as correct, useful, sufficiently fast, or acceptable in format. Where practical, hide model identities while scoring to reduce expectation bias.
- Measure the full cost and operating burden. Include hosted service or API charges where applicable; for self-hosting, include compute, storage, hosting, setup, maintenance, and engineering time. A free download removes neither infrastructure expense nor the work of running it.
- Check deployment requirements. Verify memory, throughput, context length, concurrency, energy use, and runtime compatibility for the exact model and intended workload. A hardware example for one model is not a general requirement.
- Review terms, privacy, safety, and support. Confirm the license and usage policy, where data is processed, who operates the host, what is retained, which safeguards you need, and what help is available if deployment fails.
Choose based on the combined result: task quality, total cost, operating capacity, data requirements, and acceptable risk. If candidates are close, operational simplicity or control may be the deciding factor.
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Privacy, safety, and support need separate checks
Local deployment changes the data path, not every privacy question
OpenAI says self-hosted gpt-oss is designed for infrastructure controlled by the user, and that OpenAI does not receive or process submitted data unless the user explicitly shares it with OpenAI or uses a managed hosting partner. If another company hosts the model, assess that company’s data practices separately; the model’s open weights do not settle the host’s retention or access policies.
Released weights shift some safety responsibilities
OpenAI’s gpt-oss model card describes a risk specific to releasing weights: third parties can fine-tune them, and OpenAI cannot later add mitigations to those copies or revoke access to them. It says developers may need additional safeguards to reproduce protections available in managed products. This is OpenAI’s account of its release and safety assessment, not a finding about every open and hosted model.
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Support may not include your deployment
OpenAI’s Help Center documentation on gpt-oss open-weight deployments states: “OpenAI does not provide assistance, hands-on implementation, or debugging support for any self-hosted or third-party-hosted open-weight setups, configurations, environments, or applications.” Make sure you have an operations plan or support arrangement for the environment you choose.
Which option fits different users?
Individuals
A hosted service is the simpler starting point if you want to use a model without setting up inference hardware or maintaining a deployment. Experiment with local inference when control or customization is important and you are willing to check the exact model’s requirements and handle setup yourself.
Developers
Choose a hosted model when a managed inference endpoint fits your application and you prefer not to run the model stack. Choose open weights when you need to customize or control deployment and can budget for integration, compute, maintenance, and safeguards.
Organizations
Start with policy and operational requirements: data handling, contractual terms, security controls, availability, and who will maintain the system. A self-hosted model can provide infrastructure control, but it also makes the organization accountable for operating that environment. A hosted option can reduce infrastructure work, but its terms and data handling still need to meet the organization’s requirements.
Adoption figures do not settle the choice
NIST CAISI’s 2025 adoption analysis compared open-weight models including gpt-oss and Qwen3 with DeepSeek. The report notes that closed-weight models such as GPT-5 and Opus 4 could not be assessed using some measures, including model downloads and derivative uploads. It describes its view as partial because usage data are scattered across platforms and some early data may be proprietary. Those limits mean platform-specific measures should not be read as a comprehensive market-share ranking or a recommendation for a particular user.
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