A foundation model is defined by how it is trained and reused: it learns from broad data at scale and can be adapted to many tasks. A frontier model is described by its position near the leading edge of capabilities—or, in some safety-policy writing, by its potential to exhibit dangerous capabilities. The labels answer different questions: a model can be both, and not every foundation model is frontier.
What is a foundation model?
Stanford’s Center for Research on Foundation Models (CRFM) describes foundation models as models trained on broad data at scale that can be adapted to a wide range of downstream tasks. They are often an intermediary starting point, not a finished, task-specific system: developers may need to adapt one for a particular use.
In short, “foundation” describes a model’s broad training and potential for reuse. It does not say that the model is the most capable available or that it meets a particular safety threshold. See Stanford CRFM’s 2021 report, On the Opportunities and Risks of Foundation Models.
What does “frontier model” mean?
“Frontier model” has more than one usage in the sources discussed here, and there is no single universal cutoff established for the term. Two distinctions matter:
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Frontier as a capability position
In a capability-relative usage, a frontier model is close to or beyond the average capabilities of the most capable models available at a given time. The label can also signal differences in scale, design, or the mix of capabilities and behaviors. This is a moving reference point: as the field advances, a model’s relative position can change. Shevlane and coauthors discuss this framing in Model evaluation for extreme risks.
Frontier as a safety-policy category
In safety-policy writing, “frontier AI model” can instead mean a highly capable foundation model that could exhibit dangerous capabilities. That definition adds a risk criterion; it is not simply another way of saying “one of the strongest models.” Anderljung and coauthors define the term in their 2023 paper as follows: “For the purposes of this paper, we define ‘frontier AI models’ as highly capable foundation models that could exhibit sufficiently dangerous capabilities.” The phrase “for the purposes of this paper” matters: it signals a scoped definition, not a universal standard. See Frontier AI Regulation: Managing Emerging Risks to Public Safety.
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How the labels compare
| Question | Foundation model | Frontier model |
|---|---|---|
| What does the label describe? | Broad training and adaptability across downstream tasks. | Either relative position at the capability edge or, in a specified safety-policy usage, potential dangerous capabilities. |
| How is it identified? | By broad-data, large-scale training and transfer or adaptation to different tasks. | For capability-relative use, compare it with the strongest existing models and consider its scale, design, and capability mix. For policy use, assess dangerous capabilities and possible severity. |
| Is there a fixed boundary? | The concept is broad, although usage can vary. | No universal threshold is established by the sources cited here; the meaning depends on context. |
| Can one model have both labels? | Yes. A broadly trained, adaptable model may also be frontier. | Yes. Under the cited safety-policy definition, frontier AI models are highly capable foundation models. |
Are frontier models the same as foundation models?
No. They are not competing architectures or mutually exclusive product types. “Foundation” concerns how a model is built and reused; “frontier” concerns where it sits relative to capabilities or whether it meets a defined risk criterion. Under the safety-policy definition above, frontier models are a subset of foundation models. In capability comparisons, the term highlights leading-edge position and distinctiveness.
That distinction also prevents a common overreach: state-of-the-art capability alone does not establish that a model has dangerous capabilities or poses a particular level of risk. A safety-policy assessment asks a separate question about capabilities and potential severity.
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Why the definition matters when you read about AI
- Check the author’s meaning. A capability ranking and a risk-policy category are not interchangeable. Look for the stated comparison or risk criterion.
- Don’t treat “frontier” as a permanent rank. In capability-relative use, the label depends on the state of the field and can shift as models improve.
- Don’t infer danger from the label alone. The safety-policy usage adds a dangerous-capability criterion; broad training or high benchmark performance by itself is not that assessment.
- Read numbers as reports of views, not forecasts. Shevlane and coauthors’ 2023 paper reports that 36% of AI researchers surveyed in 2022 thought AI systems could plausibly cause a catastrophe this century at least as bad as an all-out nuclear war. That figure records respondents’ opinions; it is not a 36% probability that such a catastrophe will happen. The paper attributes the survey to Michael and coauthors (2022).
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