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# How to Choose an AI Model: A Four-Step Method That Won't Go Stale
- URL: https://blog.avis.xyz/how-to-choose-an-ai-model-a-four-step-method-that-wont-go-stale/
- Published: 2026-09-15T10:04:29.000Z
- Updated: 2026-09-15T10:04:29.000Z
- Description: Leaderboards go stale. A four-step method to choose an AI model on your own data, and shrink to the cheapest one that still clears the bar.
- Author: AVIS
- Tags: Blog, English

# How to Choose an AI Model: A Four-Step Method That Won't Go Stale

Any list of the best AI models goes out of date quickly, and the model at the top of a leaderboard can still be the wrong one for your feature. Learning how to choose an AI model is more useful than memorising a ranking because the method still works when next quarter's models arrive. The short version is: define "good enough" with numbers, filter out models that can't do the job, use benchmarks to build a shortlist, and make the final decision on your own examples. Then keep shrinking until you find the cheapest model that still clears the bar.

## Before you choose an AI model, decide what good enough means

Choosing a model starts with your requirements, not with the models themselves. Before you compare anything, write down three numbers: the accuracy you need, the latency you can accept, and what one request is allowed to cost. Without those numbers, every model can look either impressive or disappointing depending on which demo or leaderboard you saw last.

### Set an accuracy target from what mistakes cost

The accuracy you need should come from what mistakes actually cost you. For example, if a correct classification saves $50 and a wrong one costs $300, the model needs to be right about 85.8% of the time just to break even. In practice, you'd probably set the target at 90% or higher to leave some margin.

Your numbers will be different, but the exercise is the same. A support-ticket tagger that's occasionally wrong is relatively cheap to correct; a model that automatically approves refunds is a different story. The more expensive a mistake is, the higher your quality bar should be, and the more you can reasonably spend to clear it.

### Pick the latency number that matches the workload

"Fast" also means different things depending on who's waiting:

| Workload                | The number that matters | Why                                                                                 |
| ----------------------- | ----------------------- | ----------------------------------------------------------------------------------- |
| Chat or assistant UI    | Time to first token     | Users feel the silence before text appears; once it streams, they can start reading |
| Agent step or tool call | End-to-end latency      | Each step waits for the full response before the next one begins                    |
| Batch processing        | Throughput and cost     | Nobody is watching; total time and spend matter                                     |

For chat, there's an interesting ceiling. Adults read non-fiction at about 238 words per minute, which works out to roughly five to six tokens per second. A stream running at 20 tokens per second is therefore already faster than most people can read. Beyond that point, making the stream faster won't necessarily make the experience feel faster. Reducing the wait before the first token will.

### Budget per request, not per token

Price per token isn't a great way to compare models on its own. Two models with similar token prices can have very different costs per request because one may produce longer answers or spend additional tokens reasoning before responding. Set a maximum cost for a single request and measure candidates against that number using real prompts, as you'll do in step four.

## Filter on hard constraints first

Before comparing quality, remove every model that can't actually do the job. This part is quick and objective, and it can turn a catalogue of hundreds of models into a shortlist of just a few.

### Modality and context

Start with what goes in and what comes out. A feature that reads screenshots needs image input; one that generates narration needs audio output. Then check the context window: the maximum amount of text, including instructions, conversation history, and retrieved documents, that the model can process in one request. If your longest realistic input doesn't fit, the model is out, regardless of how well it performs on benchmarks.

With a large catalogue, you can do this as a query instead of reading individual spec sheets. The [AVIS model list endpoint](https://docs.avis.xyz/api-reference/endpoints/model-list.md?ref=blog.avis.xyz) lets you filter by input and output modality, including text, image, file, video, audio, and embedding. If your requirement is "text in, video out", you can use that requirement to filter the catalogue directly.

### Parameters and tools

Next, check whether the model accepts the fields your application actually sends. Tool calling, structured output, reasoning controls, and even basic sampling settings aren't universal. Models in the AVIS catalogue expose their supported parameters and per-parameter bounds under `capabilities`, so you can rule out incompatible models before a `400` error does it for you.

### Image and video models differ more than you'd expect

Media models have even more specific constraints. One video model might accept any clip length from 2 to 15 seconds, another might accept only 6 or 10 seconds, and a third might support only 5, 8, or 10\. Aspect ratios and resolutions can vary just as much, and values outside a model's supported range are rejected.

If your product lets users choose a duration or output format, that requirement alone can eliminate most of the catalogue. Treat each model's documented limits as the source of truth, and validate user input against those limits instead of assuming that the options supported by one model will work with the next.

## Use benchmarks to shortlist, not to decide

Benchmarks are useful for one thing: taking a long list of possible models and narrowing it down to two or three candidates worth testing. They're much less useful for predicting exactly how a model will perform on your own task, for several reasons.

- **Contamination.** When test questions leak into training data, benchmark scores stop being a clean measure of capability. On one widely used coding benchmark, models typically scored around 20 to 25 points lower on a contamination-resistant version of the same test, using identical weights.
- **Selective release.** Public rankings don't necessarily show every model variation a developer tested internally. In one documented case, a model developer tested 27 private variants before releasing one to a public ranking.
- **Task mismatch.** A model's ability to solve competition maths problems tells you very little about how well it will summarise your support tickets. The closer a benchmark's inputs are to your real traffic in length, domain, and format, the more useful the score becomes.

Before trusting a benchmark score, check whether the test set is public or documented, whether contamination was controlled, whether the full set was run, and whether the settings are published well enough for someone else to reproduce the result. Then use the benchmarks closest to your workload to pick two or three models that also pass your hard constraints. That's the shortlist. The real decision starts after that.

## Choose the AI model on your own examples, then shrink

The final decision should happen on your data. Test the shortlist against real examples, find the models that clear your accuracy target, and then look for cheaper and faster options until you find the point where quality drops below your bar.

### Where to start: big or small

Official model-selection guidance generally points to two reasonable starting strategies:

| Start with...          | Best when                                                                            | Then                                                    |
| ---------------------- | ------------------------------------------------------------------------------------ | ------------------------------------------------------- |
| The most capable model | The task is complex, mistakes are expensive, or you don't yet know what's achievable | Hit your accuracy target first, then try smaller models |
| A fast, low-cost model | The task is well defined, volume is high, or you're prototyping                      | Upgrade only when you find a real capability gap        |

Starting with a powerful model tells you what good performance looks like before you start trading quality for cost or speed. Starting with a smaller model keeps early costs down and can be surprisingly effective for simple, high-volume tasks.

### Build a small evaluation set

A good evaluation set matters more than the size of the benchmark you started with. It doesn't need to be huge. Take a few dozen real inputs from your logs or expected traffic, and write down what an acceptable output looks like for each. Include straightforward cases, awkward edge cases, and anything that should be refused.

Run every shortlisted model on exactly the same examples, with the same prompt and settings, and score the results against your criteria. Testing multiple providers doesn't necessarily mean building multiple integrations. Through the [AVIS Gateway](https://www.avis.xyz/gateway?ref=blog.avis.xyz), one API key gives you access to 300+ models across text, image, video, and audio, while an [OpenAI-compatible endpoint](https://docs.avis.xyz/api-reference/introduction/openai-compatibility.md?ref=blog.avis.xyz) lets you keep using the SDK you're already familiar with.

### Shrink until it breaks

Once a model clears your quality bar, start looking for a cheaper way to get the same result. The difference can be substantial. In one published experiment on 1,000 news articles, a large model scored 84.5% with no examples. Adding five examples raised that to 91.5%, but the run cost about seven times as much. A smaller model fine-tuned for the task also reached 91.5%, at less than 2% of the cost of the five-example run.

There are other levers to try before switching models. Several models now let you control how much reasoning effort they use, and lowering that setting can reduce latency and cost without changing the rest of your setup. For mixed workloads, you can also route routine requests to a cheaper model and send harder cases to a more capable one.

Whatever approach you take, measure it on real runs rather than relying on price sheets. The [usage endpoint](https://docs.avis.xyz/api-reference/introduction/use.md?ref=blog.avis.xyz) records the final cost and duration of every generation, giving you a direct way to compare what you actually paid. For image, video, and audio models, estimate endpoints can preview the cost before you submit a request. Treat those as estimates rather than guarantees: video in particular is billed according to the length the model actually produces, so the final charge can differ.

## FAQ

**What is the best AI model for my app?**

There isn't one best model. The right choice is usually the cheapest and fastest model that meets your accuracy target while staying within your latency and cost limits. A leaderboard leader may be much slower or more expensive than your feature needs, so test two or three candidates on your own inputs before deciding.

**Should I start with a large model or a small one?**

Start with the most capable model when the task is complex or mistakes are expensive, because it gives you a clear idea of what's achievable. Start with a fast, inexpensive model when the task is well defined and high volume, then upgrade only when you find a real capability gap. Either way, let your evaluation results make the final decision.

**Are LLM benchmarks reliable for choosing a model?**

They're useful for building a shortlist, but they're not a substitute for testing your own workload. Scores can be inflated when test data leaks into training, and benchmark tasks rarely match real production traffic. Use benchmarks that are close to your task to identify two or three candidates, then evaluate those models on your own examples.

**How many examples do I need to evaluate a model?**

A few dozen real examples are usually enough to separate strong candidates from weak ones. Include typical requests, edge cases, and inputs that should be refused, and define what an acceptable output looks like for each. As you discover failures in production, add those examples to the evaluation set so it becomes more representative over time.

**Which latency metric matters most?**

It depends on the workload. Chat interfaces are most sensitive to time to first token because users notice the wait before text appears. Agents and multi-step workflows care more about end-to-end latency because each step waits for the previous response. Batch workloads generally care most about throughput and total cost.

**How do I compare the cost of two AI models?**

Compare the cost per request using the same real prompts rather than comparing token prices alone. Output length and reasoning tokens can vary significantly between models, so a lower token price doesn't necessarily mean a cheaper request. Run the same evaluation set through each candidate and compare the actual recorded cost.

## Wrapping up

Choosing an AI model doesn't need to depend on whichever leaderboard happens to be popular this month. Set your quality, latency, and cost targets first. Remove models that can't meet the basic requirements, use benchmarks to narrow the list, and make the final decision on your own examples. Once you've found a model that clears the bar, keep testing cheaper and faster options until you find the simplest model that still does the job.

Keep the model name in configuration so changing it is a normal deployment rather than a rewrite. If you want to compare models across providers without building a separate integration for each one, [AVIS](https://www.avis.xyz/gateway?ref=blog.avis.xyz) puts 300+ models behind one API key and passes each model's own price through with a 10% platform fee on API usage, as described in its [pricing guide](https://docs.avis.xyz/guide/get-started/api-pricing.md?ref=blog.avis.xyz).

*Last updated: September 2026*