Choosing the Right Model for Optimal Results

Welo Data builds evaluations around the tasks your model will actually perform. Candidate models are tested on representative use cases and assessed against criteria such as task performance, cost, latency, safety, domain requirements, and multilingual performance where relevant. Domain-matched human evaluators can assess outputs where automated metrics do not adequately capture quality.

Public benchmarks are useful for comparing general model capabilities, but they may not reflect how a model performs on your specific tasks, domain, languages, or quality requirements. Models with similar benchmark scores can perform very differently in a real application. Testing candidate models on representative tasks and evaluation criteria provides a more direct measure of how well they are likely to perform for your use case.

The right criteria depend on the application. Common factors include task performance, reliability, cost, latency, safety, multilingual performance, and performance on domain-specific tasks. Deployment requirements such as privacy, context length, tool use, throughput, or infrastructure constraints may also matter. The goal is to define the requirements that matter for the use case and evaluate candidate models against them.

No. Larger or more expensive models may perform better on some tasks, but the improvement may not justify the additional cost or latency for a particular application. A smaller model that meets the required quality threshold may be the better choice for a high-volume or latency-sensitive workflow, while more demanding tasks may justify a more capable model. Model selection should therefore evaluate performance and operational tradeoffs together.

Human evaluation is especially useful when quality cannot be captured reliably by automated metrics alone. This can include specialized domain accuracy, nuanced instruction following, linguistic and cultural appropriateness, safety, or other criteria that require contextual judgment. Welo Data can use evaluators with relevant domain or language expertise to assess model outputs against defined evaluation criteria.

Model performance should be evaluated in the languages and locales where the model will actually be used. Aggregate or English-language benchmark results can hide substantial differences across languages and tasks. Welo Data can build multilingual evaluations using native-language data and language experts to assess whether model quality remains consistent across target markets.

Model selection should be revisited when requirements change, new models become available, providers update existing models, or changes in cost and performance could affect the original decision. Reevaluation can also be useful after deployment to confirm that model performance continues to meet the application’s requirements using representative production tasks.

A custom evaluation starts by defining the tasks the model must perform and the criteria that determine a successful output. Representative test cases are then developed or selected, along with evaluation rubrics and appropriate automated or human evaluation methods. Candidate models can be run against the same evaluation set so that differences in quality and other requirements can be compared consistently.

LLM model selection is the process of evaluating candidate large language models against defined requirements and representative tasks to determine which model provides the appropriate balance of quality, cost, latency, safety, and other deployment requirements for a specific application.