How to choose an AI model for your application
A practical guide to comparing AI models by task quality, supported inputs, latency, and cost using your own prompts in Xava Inference.
Choose an AI model by testing it on the task your application actually performs. First check the required input and output capabilities, then compare answer quality, latency, and cost on the same set of representative requests. Use the model catalog for current capabilities and prices, and the Playground for initial trials.
The right model is the one that completes your task reliably within your quality, time, and budget requirements. A general benchmark can help you form a shortlist, but it cannot tell you how a model will handle your customers' questions or your application's inputs.
Xava Inference lets you explore models through one catalog and account. Start with a small evaluation rather than committing to a model based on its name alone.
Define the task before choosing the model
Write down what a successful result looks like. A support assistant needs accurate answers grounded in your product information. An extraction workflow needs correctly structured fields. An image workflow needs suitable visual results and the right input controls.
Your evaluation should include the inputs you expect to receive, the output you need, and the mistakes you cannot accept. Be specific about requirements such as tool calling, image inputs, structured output, or prompt length.
Use the model catalog to find models for your task. The API documentation explains how requests differ across text, images, video, audio, and embeddings.
Build a small, representative evaluation set
Collect examples from your own workflow. Include straightforward cases, ambiguous questions, incomplete inputs, and requests near the limits of your application's intended use.
For each example, define a useful result before running the models. This reduces the chance that a fluent answer will receive a high score despite missing a key fact or instruction.
- Write the input and any instructions the application supplies.
- Define the expected facts, format, or behavior.
- Run the same request against each shortlisted model.
- Record the result and evaluate it using the same criteria.
- Repeat important cases to see whether results are consistent.
The Playground is a convenient place for initial trials. For a repeatable evaluation inside your application, follow the quickstart.
Compare quality, latency, and cost together
Treat these as separate measurements. A model might generate a polished answer quickly while failing a required instruction. Another might cost less per token but produce unnecessarily long answers or require more retries.
| Dimension | Useful measurement |
|---|---|
| Quality | How often the output meets your defined requirements. |
| Latency | Time to a usable result, including retries or tool calls. |
| Cost | Total billed usage to complete the task. |
| Reliability | Consistency across repeated requests and difficult inputs. |
Read the catalog's billing units carefully. Text generation, image creation, video, and audio do not necessarily share the same pricing meter. The models documentation and billing guide describe how to interpret usage.
A sensible choice may be a different model for each step of a workflow. A short classification task and a detailed explanation do not have to use the same model. Test each step independently, then test the full experience.
Validate the integration before launch
Once a model passes your initial evaluation, run it through your actual application. Check request parameters, streaming if you use it, tool results, output parsing, and error handling.
Our OpenAI compatibility guide explains the integration starting point. The errors reference covers failures your application should handle.
Keep your evaluation set so you can rerun it when a model changes or you want to compare another option. Record the model ID and evaluation date with your results. Avoid treating a one-time trial as a permanent guarantee.
Frequently asked questions
Is the largest model always the best choice?
No. A smaller model may satisfy a focused task while meeting your cost and latency requirements. Compare models on representative inputs and choose based on the results your application needs.
Can I compare models without building an integration first?
Yes. Start with the Playground and the model catalog. Use the same prompts for each trial, then validate the shortlisted model in your application before launch.
Where can I find current prices?
The model catalog is the source for current model prices and billing units. Use it when evaluating a model rather than relying on a price quoted in an older article.
