Every comparison of AI assistants has the same problem: it is a snapshot of a leaderboard that reorders every few months. Somebody ships a new model, the rankings shuffle, and the article you read is quietly wrong without changing a word.
So this is not a scorecard. It is the three questions that actually decide it, which have stayed the same through several rounds of that shuffle.
Question 1: Where does your work already live?
This decides it more often than quality does, and people underweight it enormously.
If your documents, mail and calendar are in Google, an assistant built into Google reaches them without you pasting anything. If your team is in Microsoft, the same logic points the other way. If your work is mostly code in a repository, the assistant that runs in your editor beats a better model in a browser tab, because the better model does not have the file open.
The friction of moving material to the model is a real, recurring tax. A tool that is ten percent worse and already has your context usually wins on the actual job.
Question 2: What does a bad answer cost you?
Ask what happens when the assistant is confidently wrong.
Low cost. Drafting, brainstorming, renaming things, rewriting a paragraph you will read anyway. You will notice the failure immediately and the cost is ten seconds. Use whatever is fastest and cheapest. This is most work.
High cost. Anything you will forward, publish, sign, or act on without re-reading. Anything where you would not personally spot the error, which is the important case and the one people miss. Here you are not choosing a model, you are choosing a workflow: one that makes the claims checkable. That is a separate skill, and no assistant solves it for you.
The mistake is using the high-cost workflow for everything, which is exhausting, or the low-cost one for everything, which is how people end up in the news.
Question 3: Is your bottleneck the model, or you?
Be honest about this one.
If you are getting mediocre output from a frontier model, switching to a different frontier model will change almost nothing. They are close enough that the prompt and the context dominate the difference between them.
The bottleneck is genuinely the model when you have hit a structural limit: the thing will not fit in the window, the task needs a capability the tool does not have, the reasoning is failing on a problem you have specified precisely. Those are real and you will recognize them, because the failure is consistent rather than occasional.
What the landscape looks like, with a shelf life
As of writing, three general assistants dominate for ordinary work, and the broad shapes have been stable even as the version numbers have not:
- One is strongest on long-form writing and code.
- One has the widest ecosystem, the most integrations, and the most developed memory across conversations.
- One is deepest inside a productivity suite, with the best live web access.
I have deliberately not attached names to those, because which is which has swapped more than once and will again. Spend twenty minutes putting your own real task into two of them and you will have better information than any comparison article, including this one.
Paid consumer tiers cluster around the same price, which is a useful fact: the decision is rarely about money at the individual level.
Where free tiers stop being enough
The pattern across all of them is the same, whatever the current numbers say.
Free is genuinely fine for occasional questions, drafting, and learning what these tools do. You will hit the wall in three places:
- Length. Long documents and long conversations consume the window fastest, and the free tier is usually where limits bite first.
- The good model. Free tiers typically route you to a smaller or faster model, sometimes silently. If your results got worse and you did not change anything, check which model you are actually talking to.
- Continuity. Mid-task rate limits are the expensive kind, because you lose the thread rather than the answer.
The honest test: if you are hitting limits more than once a week on work you are paid for, the subscription is cheaper than the interruption. If you are not, it is not.
Why switching mid-task is usually a mistake
This is the practical habit worth taking away.
When an answer disappoints, the reflex is to paste the same prompt into a different assistant. It rarely helps, for a reason that follows from how these things work: you have thrown away the entire conversation. Every clarification, correction and piece of context you built up is gone, and you have handed a cold model the same prompt that already underperformed once.
You have also lost the ability to tell what happened. If the second answer is better, you do not know whether it was the model or the fresh sampling roll, because you changed two things at once.
Better, in order:
- Ask again in the same place. These are not deterministic. A second roll is free and often enough.
- Fix the prompt, not the vendor. Nine times in ten the missing piece is context you know and the model does not.
- Then switch, carrying your refined prompt with you rather than the original one.
Switching between tasks is different, and completely reasonable. Draft in one, check in another. Two independent attempts at a claim are genuinely more informative than one, precisely because they do not share a conversation.
The short version
Pick the one that is already where your work is. Match the care to what a wrong answer costs. Assume your prompt is the bottleneck until it consistently is not. Pay when interruptions start costing more than the subscription. And when an answer is bad, roll again and fix the prompt before you switch, or you will never know which change helped.