Every new artificial intelligence model arrives with an almost inevitable question: is it better than the previous one? Reasoning, coding, multimodal understanding, context and benchmark performance are compared. For a few days or weeks, one model occupies the top position until the next one appears.
For those building enterprise systems, however, there is a much more useful question: is this the right model for what we need to do?
Because the most capable model does not always deliver the best solution. Cost, speed, privacy, data location, availability, specialisation, integration capabilities and level of risk can be just as important as the intelligence of the model itself.
Choosing correctly means moving away from thinking in terms of the best model and towards the right model.
There is no single definition of ‘best’
Comparing models is necessary.Evaluations help us understand their strengths and limitations and provide useful information for decision-making. The problem arises when we turn those comparisons into a universal ranking.
A model may perform exceptionally well on complex reasoning tasks while being unnecessarily expensive for classifying thousands of simple documents.Another may respond extremely quickly but fail to provide the accuracy required for a particular process.
An open model deployed within an organisation’s own infrastructure may not match the overall performance of the most advanced models and yet still be the right choice when particular privacy, control or data sovereignty requirements apply.
The question ‘which is the best model?’ is therefore incomplete. We need to add ‘Best for what?’.
The task should determine the technology
Not every task requires the same level of intelligence. Extracting fields from an invoice, classifying a document, summarising a conversation, generating code, analysing a contract and solving a complex problem are different activities.
Using the most powerful model available for all of them may work technically.
That does not make it good architecture. More capable models often involve trade-offs in other dimensions, including cost, latency and resource consumption.
At scale, those differences can multiply quickly. A process performing millions of inferences should not be optimised according to the same criteria as an application used a few times a day to solve complex problems.
Efficiency begins by matching capability to need.
Cost and latency are part of system intelligence too
When evaluating an AI solution, we tend to focus on the quality of the answer. Production introduces other variables.
- How long does it take to respond?
- How much does each operation cost?
- What happens when volume increases?
- What availability does the provider offer?
An apparently small difference can become decisive when multiplied across thousands or millions of executions. This introduces an important idea: optimisation should not happen solely at model level, but across the system as a whole.
It may make more sense to use a lightweight model for most cases and call a more advanced model only when complexity requires it. Even the same task may follow different routes depending on its characteristics.
The architecture can decide.
Data also determines the choice
There is another variable that does not appear in many benchmarks: where the data is allowed to be.
Not all information has the same sensitivity or is subject to the same requirements. An organisation may accept certain information being processed through a public cloud service while requiring other data to remain within specific infrastructure.
In some cases, it may need to run a model in a private cloud or even on-premises. In others, the geographical location of processing or the provider’s contractual conditions may be decisive.
Selecting a model is therefore not solely a decision about capabilities. It is also a decision about data architecture, security and governance.
Specialisation versus general capability
Large general-purpose models are extraordinarily versatile. But versatility and specialisation are different qualities.
Certain tasks may be better addressed by models specifically trained or adapted for a particular function. In other cases, smaller models may deliver sufficient performance with far greater efficiency.
The right solution may not even involve changing the model. Providing better context, improving information retrieval, structuring instructions correctly or breaking a problem into several stages can produce greater improvements than replacing the model with a more powerful one.
This requires us to look at the system as a whole. When a solution does not deliver the expected result, the automatic response should not be ‘we need a better model’.
We may need better data, a different architecture or a more precise definition of the problem.
Dependency has a cost too
Choosing a model also has long-term consequences. If an application is built directly around the particular characteristics of one provider, changing it later may become difficult.
Dependencies emerge around APIs, formats, tools, instructions and specific behaviours. This does not mean that all technological dependency is negative. Every architecture involves decisions and trade-offs.
But those dependencies should be deliberate. In a market as dynamic as AI, preserving some ability to replace models can be extremely valuable. And not only for economic reasons.
A provider may change its terms, retire a model, alter its limits, change regional availability or cease to meet the requirements that led to its original selection. Adaptability needs to be part of the design.
One system may need several models
If different tasks have different requirements, the logical consequence is straightforward: the same organisation may need different models. Even the same application may need several.
A fast, economical model might classify a request. Another might retrieve or process information. A third could handle complex reasoning. A specialist model might perform a very specific function.
The application should not need to understand all of that complexity. An abstraction layer can determine which model to use according to policies and criteria established by the organisation.
This is one of the principles behind KLIA, developed by LAUDE: decoupling applications from models and providing a layer from which their access and selection can be governed.
The objective is not to use more models. It is to be able to use the right model when it creates value, without pushing all of that complexity into every application.
From model routing to intelligent routing
Selecting models according to predefined rules is a first step.
But the idea can go further. Imagine a request arriving at a system that can automatically assess its complexity, sensitivity, latency requirements or cost before deciding which model should handle it.
Simple requests could be routed to efficient models. Complex ones to more capable models. Sensitive data to specifically authorised environments.
Selection is no longer static. It becomes intelligent routing.
This approach makes it possible to optimise several dimensions of the system simultaneously and change the criteria as the available models evolve. Intelligence no longer resides solely in the model producing the answer.
It also resides in the architecture deciding how to use it.
Evaluate using your own use cases
Public benchmarks are useful, but they have an obvious limitation: they do not necessarily represent our problems. The genuinely relevant test is to evaluate models using tasks, data and conditions similar to those they will encounter in production.
- What quality do we obtain with our documents?
- What mistakes does the model make with our queries?
- What latency do we experience at our volumes?
- How much does it cost to achieve the quality level we require?
- How does it behave with our edge cases?
These evaluations enable decisions to be based on our own evidence rather than solely on results published by third parties.
And they should be repeated. The model selected today does not necessarily remain the best option a year from now.
Design for choice
The speed at which artificial intelligence is evolving makes it difficult to predict which models will dominate in a few years’ time.
We probably do not need to. A robust architecture should not depend on correctly guessing today which provider will win tomorrow.
It should allow us to choose. Change models when a better alternative emerges. Combine different providers. Run particular models within private infrastructure. Optimise costs. Adapt to new regulatory or security requirements.
In other words, retain the ability to make decisions. The strategic question is not ‘Which model should we choose?’, but rather, ‘How do we build systems that allow us to choose the right model at any given time?’
Because in AI, the advantage will not necessarily lie in always having the most powerful model. It will lie in knowing when to use each one.