How long does a proper model evaluation take?

For one feature, a small team can usually collect examples, run three or four models and score the results in one to two weeks. Most of the time goes into gathering good examples and agreeing what a good answer looks like. Once that test set exists, re-running it on a new model takes hours.

Should every feature use the same model?

No. Different features have different difficulty, speed and cost needs. A product might use a budget model for tagging and search, a mid-tier model for chat, and a top-tier model for a complex report. Routing requests to the right model is often the biggest single cost saving available.

Do we need an in-house machine learning team to choose a model?

Not to choose and use one through an API. What you need is someone who knows what good output looks like for your users, and an engineer who can run the comparison and build the integration. A machine learning team becomes relevant if you plan to fine-tune or host models yourself.

How to Choose an AI Model in 2026: A Buyer's Checklist

The question behind "which model is best?"

Sound familiar?

The board wants AI in the product this quarter. Engineering favours one model, a consultant recommends another, and a new release last week claims to beat both. Nobody can explain why one is right for your product.

There is no best AI model, only the best model for a particular job, budget and set of rules. The leaders change every few months: September 2026 alone brought new flagships from OpenAI, Anthropic and Google, compared in our AI model comparison. A choice made by following the news goes stale fast. A choice made with a repeatable process can be re-checked in a day.

The five-step process

Define the job precisely
Write down the exact task, the input it receives and what a good answer looks like. "Answer support questions" is too vague; "answer billing questions from our help centre in under 5 seconds, in English and Spanish" is testable.
Set your constraints
Decide the limits before you look at models: data rules, response time, cost per task, languages and context size. Constraints rule out options faster than benchmarks do.
Shortlist across tiers
Pick three or four candidates: at least one budget model, one mid-tier model and one open-weight option, adding a top-tier model only if the task is genuinely hard.
Test on your own examples
Run every candidate on 50 to 100 real cases, including the awkward ones, and score quality, speed and cost per task.
Decide, then plan to re-test
Choose the cheapest model that passes, keep the test set, and re-run it whenever a provider ships a new version.

Constraints that decide for you

Questions to answer before comparing models
Constraint	Question to answer	What it usually rules in or out
Data and privacy	Where may our data be processed, and may it be kept or used for training?	May require a specific cloud region, enterprise terms, or a self-hosted open-weight model
Response time	How long will a user wait?	Live chat favours faster mid-tier and budget models; overnight jobs can use slower, cheaper batch processing
Cost per task	What is one completed task worth to us?	Sets the highest tier you can afford at your expected volume
Volume	How many requests a day, and how steady?	High, steady volume makes caching, batching and open-weight hosting worth the effort
Context size	How much text must one request read?	Whole contracts or codebases need a long context window; most features do not
Languages	Which languages must it handle well?	Test every language you serve; a model that is strong in English can be noticeably weaker in others
Vendor risk	What happens if this provider changes prices or retires the model?	Favours a design where switching models is a configuration change

For current per-token prices and worked monthly cost examples, see our AI API pricing guide. For the data-control side, see open-weight vs proprietary AI models.

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Where to start your shortlist

IfThe task is simple and high-volume: tagging, routing, extraction

ThenStart with budget models and open-weight APIs.

IfThe task is customer-facing chat or drafting

ThenStart with mid-tier models and step down wherever a budget model passes your tests.

IfThe task is long, multi-step and costly to get wrong

ThenInclude a top-tier model, and reserve it for those steps only.

IfYour data cannot leave your control

ThenShortlist open-weight models you can run in your own cloud account.

IfYour traffic mixes easy and hard requests

ThenPlan for more than one model. See model routing.

Red flags in a model decision

Choosing from a public leaderboard without testing on your own examples
Testing only the easy, typical cases, not the ones that go wrong today
Comparing input prices and ignoring output prices, which are several times higher
Building directly against one provider with no layer to switch models
No fallback when the provider has an outage
Nobody owning the re-test when a new model version ships

Make the decision repeatable

The most valuable output of a model evaluation is not the choice itself. It is the test set, the scoring method and the integration layer that let you make the next choice quickly. Teams that keep those three things adopt better, cheaper models within days of release. Teams that do not are stuck re-running the whole debate.

If you want help defining the test set, running the comparison or building the integration, see our AI integration services and our guide to integrating LLMs into web applications.

Key takeaway

Define the job, set constraints first, shortlist across tiers, test on your own examples and keep the test set. The right model is the cheapest one that passes, and the right process lets you switch when that changes.

Frequently Asked Questions

AI & MLArticleTricolens

Written by

Bhumin Patel

AI & Engineering

A New AI Model Every Week: How to Choose the Right One for Your Product