Which AI model should I use right now? A living, refreshed comparison

Learn in your browser · Chapter 01 of 05

Any list that names a single 'best' AI model is out of date almost as soon as it is published. What does not go stale is knowing what to weigh, and knowing how to test the first and most important factor, reasoning ability, on your own work.

New models and updates arrive most months, so a ranked top five written today can read strangely within weeks. A short list of factors, and a habit of testing them yourself, keeps working long after any single article stops being current.

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Start with reasoning ability, because it decides whether the other five factors are even worth weighing. A fast, cheap, private answer that is wrong is still wrong. A simple way to judge it: ask a question from your own field that contains a small false assumption, and see whether the assistant notices and corrects it, or simply goes along with it.

Learn in your browser · Chapter 02 of 05

How assistants are packaged and offered changes often, so a specific comparison written here would be out of date fast. What lasts is a way of thinking about effort that does not depend on today's set-up.

Different assistants offer different ways to work: some through a simple chat window, others through a developer interface for building your own tools. What is available, and how it is offered to you, changes often and without much notice, so always check what is currently on offer on the day you decide, rather than trusting what you remember from a few months back.

The thing that actually matters is effort per useful result, not how polished the first reply looks. An option that reads well but needs three attempts, heavy editing, or a fact check every time, can end up taking more of your time than an option that is a little slower to set up but gets it right on the first try.

A slicker-looking assistant is not automatically the one that saves you the most time, and it is not automatically the most private one either, which the next chapters cover. For now, treat effort as one weight among six, not the deciding factor on its own.

Learn in your browser · Chapter 03 of 05

These are two of the most oversold specifications in AI marketing. A long context window looks impressive on a page. Whether it is actually used well, and whether your files are even accepted, are separate questions worth testing yourself.

The context window is how much text an assistant can consider at once, including your messages, its replies and any documents you add. A longer window helps with big documents, but a detail buried in the middle of a long file can still be missed even when the window is technically large enough to hold it. A big number on a spec page is not the same as reliable recall.

Multimodal support means an assistant can work with more than plain text: images, PDFs, audio or spreadsheets. Claims here vary a great deal between assistants and between different versions of the same assistant, so check the exact file types you actually use rather than trusting the word 'multimodal' on its own.

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Learn in your browser · Chapter 04 of 05

These two are easy to forget about until something goes wrong: how long you actually wait for a reply, and what happens to what you typed once you send it.

Latency is how long you wait for a reply. It is not fixed: a request that needs more reasoning usually takes longer by design, and speed can vary by time of day if a service is under heavy load. Test it at the times you actually work, not once, first thing, when everything is quiet.

Data handling is where your text goes and what is done with it. Do not assume a service is private just because it feels convenient or well designed. Before you paste anything, check whether your conversations may be used to improve the service, and whether you can switch that off in your settings.

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If a task is genuinely confidential, that is usually a sign to slow down rather than to test quickly: read the actual privacy terms, ask your employer's policy if one exists, and consider whether a model you run yourself is more appropriate. That deeper comparison, including self-run open-weight models, is covered fully in this library's original model-comparison workbook.

Learn in your browser · Chapter 05 of 05

One factor rarely decides alone. This closing chapter turns the six into a single weighing exercise, then shows you the habit that keeps any choice from going stale: a short model log.

Different tasks put weight on different factors. A confidential document puts data handling first. A quick brainstorm puts latency and effort first. A long report puts context length and reasoning ability first. Decide your top two or three factors for a task before you compare anything, or the most impressive-sounding feature will decide for you.

A model log is a short, honest record of what you tested, when, and what you decided. It stops you repeating work you have already done, and it shows you clearly when an old decision has gone stale.

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What you can do now. You have six factors to weigh instead of one brand to chase, a small test for each one, and a log that keeps your decision current without you needing to track every new release yourself.

Keep this workbook. It is yours to keep, print and write in. Come back to it whenever a new model launches, your work changes, or your current choice starts to feel wrong.

https://trust-agent.ai/exam/which-ai-model

This is free educational content published by Mickai LTD, not professional, legal, financial or compliance advice, and Mickai accepts no liability for how it's used.

Trust Agent publishes workbooks like this one for free because a framework you can apply yourself, kept current by your own testing, is worth more than any single ranked list ever could be.