The short answer is that it depends on which of three quite different problems you have, and that for one of them a general assistant like Claude or ChatGPT is the best tool available at any price. Anyone telling you a single product wins all three is selling you something.
We build interview software for employers, which makes us an interested party. It also means we have watched a great many first-round interviews go wrong, and the failure modes are consistent enough to be worth writing down. So: what the three problems are, and what actually solves each.
The three things people mean by "interview prep"
Understanding the role. You have a job description full of phrases you half recognise and you want to know what the job really is, what they will probably probe, and which of your experience maps onto it.
Rehearsing answers. You know roughly what you want to say and you need to say it out loud enough times that it comes out in order, at length, under mild pressure.
Finding out what is weak. You think your answers are fine. You want someone to tell you which ones are not, and why, before an interviewer does it for you.
These need different tools. Most disappointment with AI interview prep comes from using a tool built for one of them on a different one.
Where general assistants win outright
For the first problem — understanding the role — a general assistant is the best option, and it is not close.
Paste the job description in, tell it your background, and ask it what the role probably involves day to day, which requirements are load-bearing and which are boilerplate, and what a sceptical interviewer would push on. Then argue with the answer. That back-and-forth is the whole value, and it is exactly what a conversational model is for. A structured mock interview tool cannot do it, because a structured tool's entire premise is that it does not go off-script.
They are also very good at the middle problem in one specific way: generating variations. "Ask me that again but harder." "Now assume I gave a weak answer and follow up." A general assistant will happily do this for an hour, for free, at whatever level of difficulty you ask for.
If you only ever use one thing, use one of these. Genuinely.
Where they are weak
The third problem is the one general assistants handle badly, and the reason is structural rather than a matter of model quality.
They are agreeable. Ask a chatbot whether your answer was good and it will usually find something encouraging to say, because that is what the conversation rewards. You can push it — "be harsh", "score this out of ten and justify it" — and it will comply for a message or two, then drift back. Over a session, the average feedback is warmer than your performance deserves.
They also have no fixed bar. Each answer is judged in the context of the conversation so far rather than against a standard, so you cannot tell whether your fourth answer was better than your first or whether the model simply got more familiar with you.
And they do not make you commit. In a real screening round you answer once, without a retry and without watching the question rephrase itself into something easier. Practising in a medium where you can always ask for a hint rehearses a situation you will not be in.
Where a dedicated tool earns its place
A purpose-built mock interview is worth using when you want the shape of the real thing: a fixed set of questions written for the role, answered once each, scored against those questions rather than discussed.
That constraint is the product. Because the questions are fixed before you start, the score means something across attempts — do it twice a day apart and the difference is signal. Because you cannot negotiate with it, you find out what you sound like when you cannot negotiate.
The feedback is also narrower in a useful way. What these tools are reliably good at is noticing mechanical failures: an answer that never got specific, a question that was not actually addressed, a claim that arrived without evidence. That is not deep insight, but it is the thing that most often loses a first round, and it is the thing people cannot see in their own answers.
Ours is free and takes about ten minutes, with no account and no email wall. There are others; the ones worth your time are the ones that generate questions from the actual job description rather than serving a generic list, because generic questions produce generic practice.
What none of them can do
No tool of either kind can tell you whether you will get the job. It cannot see the other applicants, the budget, the internal candidate the manager already wanted, or the referral that arrived by email last week. Treat any number you are given as a check on whether your answers work, not as a prediction.
They are also poor judges of domain correctness in specialist fields. If the substance of your answer is wrong in a way that only another practitioner would catch, expect to be marked as fluent. Fluent and wrong is a distinct failure mode and no current tool is reliable at spotting it.
What we would actually do
If you have a week:
- Once, with a general assistant. Paste the job description, interrogate the role, work out which of your experience maps onto it. Come out with five or six stories you intend to tell.
- Once, with a structured mock. Answer under something like real conditions and read the feedback for the mechanical failures — vagueness, unanswered questions, unevidenced claims.
- Out loud, to yourself, twice. No tool. This is the step people skip, and it is the one that fixes the problem the others only identified. An answer you have said aloud twice comes out in order; one you have only typed does not.
- Once more with the structured mock, a day later. Same questions. If the score has not moved, the problem is the content of your stories rather than your delivery, and you should go back to step one.
The tools are worth using. The step that changes the outcome is still saying it out loud, and no AI does that part for you.