Best AI Mock Interview Platforms in 2026: An Honest Comparison

If you are searching for an AI mock interview platform, you have probably noticed the category is crowded and the marketing is nearly identical. Everyone promises realistic practice and instant feedback. This guide compares the main options on the things that actually change your interview outcome - whether you speak out loud, whether your code runs, and whether the feedback tells you something you did not already know.

We build one of the tools on this list, so treat the recommendation with the scepticism it deserves. What we have tried to do instead is describe each platform accurately enough that you could disagree with our conclusion and still find the comparison useful.

What actually separates these tools

Almost every technical interview has the same four parts: you hear a problem, you talk through an approach, you write code while someone watches, and you answer follow-up questions about trade-offs. A practice tool is useful in proportion to how many of those four it reproduces.

That is the whole framework. A problem archive covers part three brilliantly and the other three not at all - which is why strong problem-solvers still fail interviews. Peer platforms cover all four but depend on a stranger showing up and being good at interviewing. Scheduled sessions with real engineers cover all four properly and cost accordingly.

The comparison

Capabilities as of 2026. We have deliberately left pricing out - it changes often enough that any figure here would be wrong within a quarter, and every platform below publishes current prices on its own site.

Platform	Speak aloud	Runs your code	Available instantly	Best for
LeetCode	No	Yes	Yes	Building raw problem-solving volume
HackerRank	No	Yes	Yes	Practising the assessment format employers send you
Pramp	Yes, with a peer	Shared editor	Scheduled	Free spoken practice, if you can match schedules
interviewing.io	Yes, with an engineer	Shared editor	Scheduled	The closest thing to the real round, at a price
Devana	Yes, with an AI interviewer	Yes, executed	Yes	Repeatable spoken practice on demand

The pattern in that table is the point. The two columns that matter most - speaking aloud and having your code actually executed - are usually a trade-off. Archives run your code but let you work in silence. Live platforms make you talk but typically grade by eye, because a human interviewer is reading your screen rather than running your submission against a test suite. Combining both is the gap we built into.

Where each tool spends your time

A different way to see the same thing. Each bar is how much of a real interview loop that tool rehearses - not a quality score, just coverage.

"Programs must be written for people to read, and only incidentally for machines to execute."
- Harold Abelson, Structure and Interpretation of Computer Programs

Abelson was writing about source code, but the line describes a technical interview exactly. Your solution is read by a person before it is ever run. Practice that never involves a person reading your reasoning is practice for the wrong audience.

Honest picks, by situation

If you are…	Start with	Because
New to data structures	LeetCode	You need volume and pattern recognition before anything else is worth doing
Solving problems but failing interviews	Devana	The gap is spoken reasoning under observation, which archives cannot rehearse
Two weeks from an onsite	interviewing.io	A working engineer's read on your performance is worth paying for at that point
On no budget at all	Pramp	Free spoken practice beats paid silent practice, scheduling friction included

Where Devana is different

Three things, stated plainly so you can check them.

You talk, and the interviewer answers. Sessions are spoken. You explain your approach before you write anything, the interviewer pushes back on the parts you skipped, and the transcript is scored alongside your code. This is the part archives cannot do and the part interviews are mostly made of.

Your code is executed, not eyeballed. Submissions run against real test cases in Python, JavaScript, TypeScript, Java or C++. Every test case in the question bank has been verified against a working reference solution, so a correct answer is not marked wrong by a bad expectation - a failure mode more common in this category than anyone admits.

The bank is deep enough not to repeat. Filtering to coding rounds for a backend role still leaves several hundred distinct questions, which is more than a daily practice habit will exhaust in a year. Questions are grouped by company to reflect the style each is known for - they are written by us, not taken from anyone's hiring process.

The verdict

Use an archive to build the raw ability. Use spoken mock interviews to convert that ability into an offer. Most candidates over-invest in the first and skip the second entirely, then conclude they need more problems - when what they needed was to have said any of it out loud beforehand.

If you take one thing from this comparison, make it that. The tool matters less than whether your practice resembles the thing you are practising for.