The Tool Desk
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What “open-weight” and “closed” actually mean
These terms describe access and control, not a quality score. AI model access falls along a spectrum: you might use a hosted product, call a provider’s API, receive fine-tuning access, download model weights, or get a release that also makes training data and code available. The International AI Safety Report describes this spectrum and notes that there is no universal agreement about which artifacts must be public for a model to count as “open source.” Its categories reflect the report’s publication period, not permanent labels for every product. International AI Safety Report, 2025
Open-weight means the trained weights are available to download. That does not necessarily mean the training data, full code, or every part of the surrounding system is available. Check the license and release contents before assuming you can use, modify, or redistribute a model in a particular way. For example, OpenAI describes gpt-oss as open-weight because its weights are publicly available under Apache 2.0, while noting that some surrounding infrastructure or tools may remain proprietary. That description applies to this release, not to every open-weight model. OpenAI gpt-oss documentation
| Access arrangement | What it generally gives you | What to verify |
|---|---|---|
| Hosted product | Access to a model through a provider’s application. | Which model and features are included, how your data is handled, and what controls apply to your account. |
| API access | A way to send requests to a provider’s model from your own application. | Endpoint-specific retention, training-use settings, region, pricing, and contract terms. |
| Downloadable weights | Weights that can be run on infrastructure you or a hosting partner control, subject to the license and technical requirements. | Whether code or training data is also available, license restrictions, compute needs, and which party operates the deployment. |
| Fully open release | A release that may make weights, data, and code available. | Which artifacts are actually included and how the release license applies. Definitions of “open source” for AI differ. |
These categories can overlap in practice. A model with downloadable weights may also be available through a hosted API, and managed hosting can run weights on infrastructure that you do not operate yourself.
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Privacy depends on the data path and controls
Self-hosting can give an organization direct control over where inference runs and can keep prompts and outputs within its infrastructure. But that is a deployment choice, not a privacy guarantee: data can still flow through application logs, telemetry, backups, identity systems, network routes, or a hosting partner. The operator needs to understand and secure those paths.
OpenAI says its self-hosted gpt-oss models are designed to run on infrastructure controlled by the operator, and that OpenAI does not receive or process data sent to those models unless the operator shares it or uses a managed hosting partner. This is a statement about that model and deployment arrangement; it does not establish how other open-weight products or hosting setups handle data. OpenAI gpt-oss documentation
A hosted API does not automatically mean that prompts are retained indefinitely or used to train models. Providers may offer controls, but their coverage can depend on the product, endpoint, account, and contract. For example, Mistral’s documentation says its zero data retention (ZDR) option is available to eligible organizations on paid plans for supported stateless API calls. It does not cover certain stateful services, and ZDR is separate from opting out of model training. Confirm that the control is approved and active for your account and applies to the endpoint you plan to use. Mistral zero data retention documentation
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Before sending sensitive information to any model, establish the actual data path and confirm these points for the specific product and deployment:
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- Where prompts and outputs are processed, including the relevant region.
- Whether data is retained, for how long, and whether retention differs by endpoint or feature.
- Whether data may be used for model improvement, and which setting or agreement controls that use.
- Which subprocessors handle data, and how logs and backups are controlled.
- Who can access the data and whether access controls, audit trails, and incident procedures meet your requirements.
Compare the full cost of running your workload
Downloading weights may have no purchase price, but inference still consumes resources. OpenAI says gpt-oss weights are free to download and use under Apache 2.0; the operator pays for compute, storage, or third-party hosting. Self-hosting can also require engineering, security work, maintenance, capacity planning, and model-upgrade effort. With a hosted API, the provider operates much of the inference infrastructure, while you still need to check usage charges and service terms. OpenAI gpt-oss documentation
A useful comparison includes more than the price per token or the cost of a GPU. Estimate the resources and work needed for the workload you actually expect:
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- Expected request and token volume, including peak capacity.
- Compute, storage, power, or cloud rental, adjusted for how much the hardware will be used.
- Engineering, security, maintenance, and on-call time.
- Evaluation, fine-tuning, monitoring, and model-upgrade work.
- API charges, where applicable, and the cost of errors, fallbacks, or service interruptions.
Low or irregular utilization can make dedicated hardware uneconomical; high, stable use can change the calculation. There is no universal break-even volume: it depends on the model, hardware, hosting, usage pattern, staffing, and service requirements.
Keep training cost separate from inference cost
The International AI Safety Report, published in 2025, gives an estimated $191 million in compute costs to train Google’s Gemini model and says compute costs for the most expensive single general-purpose AI model were projected to exceed $1 billion by 2027. These are training-compute figures reported by the report, not the cost of running inference or a price quote for using a closed model. International AI Safety Report, 2025
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Why one cost study cannot settle the question
A peer-reviewed 2024 study, Laboratory-Scale AI, tested selected models on three public-interest tasks. In its climate fact-checking test, it reported an inference cost of $0.31 for fine-tuned Mistral-7B-Instruct, compared with $2.65 for zero-shot GPT-4-Turbo. The same study reported that closed models were faster under its evaluated runtime conditions. These are results from specific models, tasks, and experimental conditions—not current market prices or proof that open models always cost less. Wolfe et al., ACM FAccT 2024
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Measure performance on your own task
A model’s benchmark score does not tell you how well it will perform in your application. Quality, latency, reliability, and tool use can all vary by task, model version, prompt, context limit, fine-tuning, and evaluation setup. A result from one benchmark should not be treated as a universal ranking.
In Laboratory-Scale AI, GPT-4-Turbo outperformed the tested open models in the study’s few-shot comparisons. Fine-tuning improved results for selected open models and, on individual tasks, sometimes matched or exceeded the particular hosted baseline. The tested closed models were faster in the reported runtime setting. Because the study’s model versions and test design are historical and narrow, its value is in showing that adaptation and task choice can change the comparison—not in establishing which category wins today. Wolfe et al., ACM FAccT 2024
Benchmark figures need the same caution. OpenAI’s gpt-oss model card reports AIME 2025 scores with tools at high reasoning effort of 97.9% for gpt-oss-120b and 98.7% for gpt-oss-20b. Those figures refer to a named benchmark and documented evaluation setup; they are not directly comparable with another provider’s result unless tool access, prompting, sampling, and scoring conditions are matched. OpenAI gpt-oss model card
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For a useful pilot, hold the evaluation conditions steady and measure the outcomes your application needs:
- Choose representative requests, including difficult cases and likely failure modes.
- Fix model versions, prompts, tools, context limits, and scoring rules for each candidate.
- Measure task quality and failure rates alongside end-to-end latency, throughput, availability, and cost.
- Have qualified reviewers assess outputs where mistakes could cause harm or material loss.
Decide who owns safeguards and operations
With self-hosted weights, the deploying organization has more direct control over the runtime and can modify the model, but it also takes on more of the work to secure, monitor, maintain, and safeguard that deployment. OpenAI’s gpt-oss model card describes a distinct risk profile for downloadable models: downstream users can modify them, potentially bypass refusals or increase harmful capabilities, and the provider cannot revoke every released copy. The card says some developers may need to add safeguards to replicate protections present in the provider’s API and products. These are OpenAI’s assessments of its gpt-oss release, not a universal risk comparison for all models. OpenAI gpt-oss model card
In an August 2025 assessment, OpenAI said its adversarially fine-tuned gpt-oss variants underperformed o3 in the frontier-risk evaluations described in that report. This is a result under one provider’s testing and threat model; it does not independently establish the relative risks of every open-weight and closed model. OpenAI, “Estimating worst case frontier risks of open weight LLMs,” 2025
Hosted deployment shifts some system operation to the provider, but your organization still needs to select appropriate data controls and evaluate outputs. For either deployment, assign clear ownership for policy, access control, evaluation, monitoring, updates, incident response, and human escalation.
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Use these decision checks to narrow the options before running a pilot:
- Data requirements: If policy requires inference to stay in infrastructure you control, assess self-hosting or an appropriate privately managed deployment. If a provider is acceptable, verify its exact data controls and contract for your endpoint.
- Operational capacity: Choose self-hosting only if you can provide the infrastructure, security, maintenance, monitoring, and safeguards it requires. A hosted service reduces some of that operating burden but does not replace your evaluation and governance work.
- Economics: Compare API spend with the full cost of operating the alternative at your expected volume and utilization. Include staff time and peak capacity, not just weights or hardware purchase price.
- Task performance: Test the candidate versions on representative inputs, using matched conditions and a scoring method appropriate to the consequences of errors.
- License and risk: Review the model’s actual license and release artifacts, and assess whether you can manage its safety and misuse risks in the intended application.
The practical choice is the model and deployment that meet your requirements at an acceptable total cost and risk—not a category label. Because versions, licenses, hosting terms, and performance can change, base the decision on the specific candidates you intend to deploy.
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