Choosing an AI model can feel overwhelming. Providers release new versions every few months, each with claims of better performance. Benchmarks rarely match business tasks. The good news is that a simple, practical process works: define the task, test real examples and compare a few candidates on what matters to you.
Our AI model directory summarizes current options in plain English. This guide explains how to use that information to make a decision.
Step 1: Define the task precisely
Start with what the model must do, not which model is popular. "Read supplier invoices and extract supplier, date, total and tax into JSON" is a task. "Use AI for finance" is not. Write down:
- The input: emails, scanned invoices, questions, recordings?
- The output: a label, extracted fields, a reply, a summary, an action?
- What a good result looks like, with examples.
- What a bad result would cost you.
Step 2: List your requirements
- Inputs: text only, or also images, PDFs, audio or video?
- Volume: ten requests a day or ten thousand?
- Speed: is a person waiting, as in live chat, or can it run in the background?
- Output format: free text, or structured output that software reads?
- Tools: does the model need to call your systems?
- Data rules: can data go to a hosted provider, and in which regions?
- Budget: what can you spend per month at expected volume?
Step 3: Make a shortlist
Pick two or three candidates that meet your requirements, ideally from different providers. A typical shortlist mixes a fast, low-cost model with a stronger mid-tier model. If data must stay in-house, include an open-weights model you can host. Our general purpose, reasoning and vision categories are useful starting points.
Step 4: Build a test set
Collect real examples: fifty is a good start, a few hundred is better for high-volume tasks. Include the hard cases: poor scans, unusual phrasing, missing information and examples where the right answer is "I do not know". Write down the correct output for each. This test set is the most valuable asset in the whole process, and you will reuse it every time you consider a new model.
Step 5: Run the comparison
Run every candidate on the same test set with the same instructions. Measure:
- Quality: how many outputs are correct, and how bad are the wrong ones?
- Format reliability: how often does the output break the required format?
- Speed: average and slowest response times.
- Cost: cost per request and estimated monthly cost at your volume.
Look closely at the failures. A model that is slightly less accurate but fails safely, for example by saying it is unsure, may be better than one that is confidently wrong.
Step 6: Check what is beyond quality
- Data terms: does the provider train on your data under your plan? Where is data processed?
- Availability: is the model stable or a preview? What are the rate limits?
- Lifecycle: are older versions being retired soon?
- Support: is there business support if something goes wrong?
Our model pages note a reviewed date and link to provider documentation for these details.
Understanding context windows and tokens
Models differ in how much text they can read at once, called the context window, and they charge by tokens, which are pieces of words. A large window lets a model read a long contract in one go, but sending more text costs more and takes longer. For large document collections, retrieval is usually better than sending everything. See what is RAG and when do you need it.
Step 7: Consider combining models
Many production systems use more than one model. A fast, cheap model handles the simple majority of requests, and a stronger model handles the hard or uncertain ones. This keeps average cost low while protecting quality where it matters. Routing can be based on confidence, request type or a simple rule.
Step 8: Design for change
The best model today may not be the best in six months, and providers retire older versions. Keep the model name and settings in configuration, not scattered through code, and keep your test set so you can evaluate new models quickly. With that setup, switching models becomes a test and a setting change, not a project.
If data must stay private
If your rules prevent sending data to hosted providers, look at open-weights models you can run on your own servers or a trusted host. Compare their quality honestly with hosted models on your test set, and include the cost of hardware and maintenance. Sometimes a hosted model with strong business terms and a regional endpoint meets your rules more simply. See how to keep customer data safe when using AI.
An example decision
A company wants to classify incoming support emails into twelve categories, at about two thousand a day. They build a test set of three hundred labeled emails and test three models: a small, fast model, a mid-tier model and a large reasoning model. The small model is correct on most emails and cheapest by far. The mid-tier model is slightly more accurate. The large model adds little and is slow. They choose the small model, sending emails it is unsure about to the mid-tier model. This is illustrative, but it reflects a common result.
Common mistakes
- Choosing by benchmark headlines instead of your own tests.
- Testing only easy examples.
- Ignoring cost at real volume.
- Hard-coding a model so switching is painful.
- Forgetting data terms until a customer or auditor asks.
Next steps
Use our model picker to get a starting shortlist, then run a small test on your own data. If you would like help, we run these evaluations as a short, focused project. For context on what AI can do, read what can AI do for a small business.
Who should be involved
Model choice is not only a technical decision. Involve the people who know what a good result looks like, such as the bookkeeper for invoice extraction or the support lead for customer replies. They should help build the test set and judge the outputs, because they will spot errors a developer might miss. Involve whoever is responsible for data protection early, so data terms and regions are checked before a favorite emerges. And agree who will review the choice when providers release new models, so the decision does not quietly go stale.
Finally, write down the decision and the reasons: which models were tested, the results, the costs and the data terms. That short record makes future reviews quick and helps explain the choice to customers, auditors or new team members.
A careful choice today saves many rushed decisions later.