What Are Frontier Models? Capabilities, Uses & Limitations
Frontier Models are advanced general purpose AI models that can think, write, code and understand images and documents on the frontier of AI. As technology improves, what we considered frontier models today will be normal tomorrow.
Existing definitions tend to be on models that can do many things at, or close to, their maximum capability. Understanding this difference can be useful in determining whether to consider using a frontier AI model in a specific application within a business, where it may have some drawbacks, or to use a frontier model at all.
What Makes a Model “Frontier”?
There is no formal test for a model to become a frontier one, and it is decided on a case-by-case basis. It’s usually judged by capability, scale, training compute and performance against other leading models released at the same time.
In general, frontier models are constructed to perform various tasks, not just one or two specific ones. These activities may include understanding and producing text, plus:
- Programming
- Examining pictures
- Using logic in complex problems
- Using tools
- Greater autonomy
This breadth is why frontier models are so widely adopted. Rather than developing a model for each application, a general application model can be the basis for multiple applications.
Foundation Models vs. Frontier Models
It is important to distinguish between these two types of models to understand the difference between foundation models and frontier models.
A large amount of data is used to train a foundation model, and it can be adapted to numerous downstream tasks.
The categories can overlap, but not every foundation model is a frontier model.
For businesses, the distinction matters because:
- Smaller models may be cheaper to operate.
- Specialized models may perform better on specific tasks.
- Frontier models may be more suitable for complex tasks requiring broader reasoning.
- The right choice depends on accuracy, cost, speed, and deployment requirements.
What Can Frontier Models Do?
The main benefit of the frontier model capabilities is that they can handle complex, multi-step work.
For example, a frontier model can process a large set of documents, find relationships between information, summarize information, answer subsequent questions, and write a response. Coding models can understand, construct, explain and assist in the construction of software.
- Document analysis: Look through extensive amounts of documents, see how they relate to each other, and summarise information.
- Programming: Create code, document, review code changes, and assist in debugging code.
- Multimodal processing: Depending on the model, working with text, images, audio, and documents.
- Complex reasoning: Help solve problems involving multiple steps or sources of information.
- Tool use: Engage with the external tool and system, when appropriate.
Frontier models can power agentic systems and AI automation that plans actions, uses tools and completes multi-step tasks.
What Are Frontier AI Models Used For?
Typical applications of AI for the frontier include tasks that require a wide-ranging understanding or reasoning, such as handling large volumes of unstructured data.
Frontier AI models can help with coding, code review, code explanation, and code debugging in software development.
- They can add up documents
- Compare information between documents
- Organize information
- Help users learn about complex sets of information in the context of data-driven tasks.
One other is AI in cybersecurity, where clever algorithms are investigated and utilized to discover vulnerabilities and support security efforts. Meanwhile, their increasing cyber competencies raise security issues, as the same could be used in an improper way. The right use depends less on whether a model has the “frontier” label and more on whether its capabilities match the problem you need to solve.
What Are the Limitations of Frontier Models?
While frontier models have more capability than previous models in many aspects, they are not necessarily reliable.
One of the major restrictions is the accuracy of facts. Even the most sophisticated language models can provide incorrect and believable information.
This matters most in high-stakes areas such as finance, legal, healthcare and compliance, where a wrong answer has real consequences.
Long, multi-step workflows add risk: a model can be right at one step and wrong at the next. Context also matters. Your organization may not have any pre-established policies, data, customer information, or workflow processes that a general-purpose model is familiar with. There are several challenges to considering trusted business data in a model: access, privacy, permissions, and security.
What Are the Risks of Frontier AI?
When it comes to frontier AI risks, there are two sides to the discussion: the limitations of the models themselves and the implications of deploying increasingly powerful systems.
The more powerful models can give rise to more useful applications, and the more powerful the model, the larger the consequences of errors or misuse.
- Model evaluation
- Security testing
- Security measures
- Model monitoring
and human oversight are all crucial components of deploying frontier AI.
When installing a model for businesses, the process doesn't stop at choosing a model and attaching it to an app. There are concepts to consider: What information can be used as inputs to the model, what actions the model can take, where outputs can be checked by humans, and how errors will be detected.
How Can Businesses Use Frontier AI?
While today’s frontier models are impressive, their power should not dictate how they're used. Start with the problem you want to solve. Then consider what sort of reasoning you need, what information you must provide the model, how long you have to wait for the model to respond, how expensive the model is, what privacy issues you may have, the reliability of the model, and what the ramifications of a mistake are.
Smaller systems will have limitations on certain applications compared to what will be found on the frontier models. A smaller, specialised model may be cheaper and simpler to run. The real question isn’t which model is most powerful, but what level of AI this use case actually needs.
Most AI risks come from how teams use the models, not the models themselves. At Velocity, our AI enablement and training helps your staff use frontier tools safely and get results from them.
