THE KEY ANSWER
Test models on the same representative tasks and with established criteria. In addition to accuracy, consider the cost of a completed case, latency, data handling, and the ability to switch providers.
First, describe the task and constraints
Classifying short tickets may require a different model than analyzing a complex document. Document the languages, input format, expected output, and maximum wait time. Also define the data handling approach: what can be shared with the provider, where it can be processed, and what configuration the organization requires.
Do not start by asking for the single best model for the entire company. The process may include tasks of varying difficulty. Separating them facilitates comparison. At the same time, avoid a complex network of models if a simpler solution meets the requirements. Each additional variant means testing and maintenance.
Context and references: NIST: Generative AI Profile
Build a comparable test set
Collect typical, rare, incomplete, and difficult materials. Establish the expected result without looking at a specific model's response. If evaluation requires an expert, prepare clear criteria and check for consistency in ratings. Keep some tasks outside the instruction refinement process.
Compare settings appropriate for the application, not just the default demo. Record the model version, data preparation method, and test conditions. For tasks involving randomness, repeat trials to observe stability. A single excellent response does not indicate how the system will perform on the next hundreds of cases.
Case cost is more important than invocation cost
Add repetitions, tools, retrieval, and user effort to the calculation. A model generating cheaper text may require more corrections. A more expensive variant may be justified only for a subset of difficult cases. Consider such a split only when you can reliably identify these cases and control the handoff between variants.
Measure response time from the user's perspective: from starting the task to a useful result. Quickly displaying the first words does not mean the work is finished quickly. For a task performed in the background, meeting a deadline may be more important than an immediate reaction. Interface requirements should influence the technology choice.
The choice should be reversible
Separate business rules and data formats from model provider specifics. This is not about abstraction for every possible future, but about a clear point of exchange. Maintain your own test set and history of results. This allows a new model to be tested without rediscovering what quality means.
Before switching, compare the new variant with the current one on the same dataset and on fresh tasks. Plan a limited rollout and a rollback to the previous configuration. If the old model becomes unavailable, such a procedure reduces time pressure. Do not depend on the assumption that the provider's current offer will remain unchanged.
WHERE TO START
Bring this into your project.
- Define the language, data, output format, and time.
- Keep independent acceptance examples.
- Calculate the full cost of a correctly completed task.
- Prepare a test and a provider change procedure.
Choose one thing your process is missing today. It's a useful topic for your first conversation with the team.
QUESTIONS AND ANSWERS
Frequently asked questions.
Is an open-source model always cheaper?
No. Self-hosting requires infrastructure, operations, and expertise. Compare full costs under real load and quality requirements, not just the absence of an API fee.
How often should the comparison be repeated?
After a significant change in the task, quality, costs, or model availability. A constant control set helps check a new option without interrupting product work. Frequency should be driven by needs, not by the number of releases.
Sources and context
- NIST: Generative AI Profile ↗
NIST describes risk assessment in the system lifecycle. The model selection criteria in this guide are a practical proposal for comparison, without pointing to a single provider.
Prepared by the ALGOV team. Current as of September 8, 2026. Examples describe possible scenarios, not results from client projects. How we create our guides.
