How Long Should a Free Trial Be for a Mobile App? Longer Converts 70% Better

The short answer: default to a 14-day trial on the annual plan

How long should a free trial be? Start at 14 days on the annual plan, then lengthen only if your funnel is stable and you're optimizing for conversion and LTV. The RevenueCat State of Subscription Apps 2026 numbers point one way: longer trials convert better. Trials of 17-32 days convert to paid at 42.5%, against 25.5% for trials of 4 days or fewer. That's about a 70% lift from the window alone, no new copy, no new price.

The catch is that longer isn't free, and 'as long as possible' isn't the answer. Headspace A/B tested 7-, 14-, and 30-day trials and the 14-day trial on the annual plan won, long enough to feel low-risk without letting intent decay. Shorter trials also buy things a growing app wants: faster experiments, faster cash, lower serving cost. So the honest answer is a default of 14 days on annual, plus a deciding variable. Both are below.

Free trial length and conversion rate, by the numbers

The trial-to-paid ladder from RevenueCat's 2026 report, across 115,000 apps, runs one direction. Trials of 4 days or fewer convert at 25.5%. Trials of 5-9 days convert at 37.4%. Trials of 17-32 days convert at 42.5%. The jump from short to long is the ~70% relative lift, and most of it lands early: moving from 4 days to the 5-9 day band alone carries conversion from 25.5% to 37.4%.

The 5-9 day band is the practical sweet spot for most apps, near the top of the curve without a month-long window's cost. That lines up with what Headspace found testing 7, 14, and 30 days: the 14-day trial on the annual plan won. The curve flattens past two weeks, so the gain from 14 to 30 days rarely pays for the extra cash-cycle drag.

Why longer trials convert: more time to survive Day 0 and reach the aha

Cancellations are front-loaded, and that's the whole mechanism. In the same dataset, 55.4% of 3-day trials cancel on Day 0. A 7-day trial drops that to 39.8%, a 14-day to 35.7%, a 30-day to 31.1%. The first session decides, and a short trial gives the user almost no room to move past that first-day 'not now' before the charge is already looming.

A longer window buys time to reach the aha moment, the point where the user first feels the product work. A user who's felt the value is a different person at the renewal screen than one who bailed on Day 0. That's also why the day-0 spike matters more than the headline conversion number. If you want to understand why users cancel free trials on Day 0, the fix is usually getting the value to land in the first session, not stretching the trial toward infinity.

The trade-off: shorter trials buy velocity, cash, and cost control

Longer converts better per trial. That doesn't make it the right call for every app, and the tasu brain keeps this tension open on purpose. Duolingo cut its free trial from 14 to 7 days and doubled its experimentation velocity. A shorter trial front-loads the cancel decision, so the team learns which paywall test won in days instead of weeks, and ships more experiments per quarter.

Two other costs push the same way. A shorter trial shortens the cash cycle and CAC payback, which matters when you're spending on ads. It also caps per-trial serving cost, the reason AI apps with real inference bills go short. Handing a 30-day free trial to a user who might churn fast anyway is expensive when every session burns compute.

The deciding variable is what you're optimizing and your cost profile. Stable funnel, maximizing conversion and LTV, no marginal cost per trial? Lengthen it toward the 14-day-on-annual sweet spot. Iterating fast, cash-constrained, or carrying a real serving cost per trial? Shorten it, and accept that you're trading some per-trial conversion for learning speed, cash velocity, and unit-cost control.

The market is shortening trials anyway, and that's the leak

Here's the strange part. The data says longer converts better, and the market is moving the other way. Year over year, trials of 4 days or fewer rose from 42.1% to 46.5% of apps. The 5-9 day band fell from 43.5% to 39.9%. The 17-32 day band fell from 6.1% to 5.0%. Nearly half of all apps now run a trial of 4 days or fewer, the worst-converting band in the report.

Some of that shortening is rational, from AI apps and fast-iterating teams that genuinely need velocity or cost control. Most of it isn't. It's apps trimming out of habit or copying a competitor, and leaving the ~70% conversion lift on the table. If you have a stable funnel, real value inside the window, and no per-trial serving cost, the herd is walking away from the easiest lever you own. This is a different call from whether to run a hard paywall or freemium in the first place, but it's the same species of mistake: copying a pattern instead of testing your own funnel.

How to choose your free trial length

• Default to 14 days on the annual plan. It's the length Headspace's A/B test landed on, and the long end of the curve beats the short end by a wide margin (42.5% vs 25.5%). Start here unless you have a specific reason not to.
• Shorten to 7 days if you're iterating fast, spending on ads, or carrying a real serving cost per trial. Duolingo's 14-to-7 cut doubled experiment velocity. You trade some conversion for speed and cash, on purpose.
• Don't run a 3-day trial by default. 55.4% cancel on Day 0 and it's the worst-converting band. A 3-day window only makes sense on a high-impulse, weekly-priced app where the buy decision is instant.
• Fix Day 0 before you extend the window. If your aha doesn't land in the first session, a longer trial just delays the same cancel. Get the value to land, then lengthen.
• Test trial length as a single variable. Change it on its own, not alongside a new price or a redesigned paywall, or you won't know what moved conversion.
• Match the trial to the plan. Headspace's win was specifically the 14-day trial on the annual plan: a trial that steers users to annual collects more cash per convert while still reading as low-risk.

Pick the length, then test it on your funnel

Trial length is one lever, and the only test that settles it runs on your own funnel, not a benchmark table. If you want this kind of conversion research on tap while you build, tasu's MCP is the layer your AI coding agent calls for onboarding, paywall, pricing, and retention answers, each one a sourced claim with its benchmark. It's retrieval-only: it hands your agent the evidence, and your agent writes the code.