1,000 or 5,000 Monte Carlo Runs? Why More Is Not More Accurate
Some planners advertise 5,000 simulations. Beagle runs 1,000. We measured what the extra runs buy, and what moves the answer far more.
A Monte Carlo retirement simulation plays your plan forward through many randomly generated markets and counts how often the money lasts. Empower, for example, describes its planner as running 5,000 scenarios. Beagle runs 1,000. If more is better, why not run more?
The short answer: more runs make the number steadier, not truer. Past about 1,000, the gain is a fraction of a percentage point, while a small change in what you assume about the future moves the result by 10 to 20 points.
What we measured
We used Beagle's sample plan: a 58-year-old with $1 million saved, a $75,000 salary until retiring at 65, $70,000 a year of spending rising 3% a year, $48,000 of Social Security from 67, and a 7% expected return, planned through age 92. Then we asked the Monte Carlo engine for the odds 20 times at each run count. Every request draws fresh random markets, so the spread between the 20 answers shows how much of the number is noise.
Each bar spans the lowest and highest result from 20 requests. At 100 runs the same plan scored anywhere from 65% to 77%; at 1,000 runs, 69% to 73.5%; at 5,000 runs, 71.1% to 73.1%.
The pattern follows a basic rule of sampling: noise shrinks with the square root of the number of runs. Going from 1,000 to 5,000 runs is five times the work but cuts the typical wobble only by a bit more than half, from about 1.1 points to about 0.5.
| Runs | Typical wobble | Time per request |
|---|---|---|
| 100 | about 4.0 points | about 1.0 seconds |
| 500 | about 1.5 points | about 2.2 seconds |
| 1,000 | about 1.1 points | about 2.8 seconds |
| 5,000 | about 0.5 points | about 7.1 seconds |
The time matters because every Beagle run is a full year-by-year projection, with taxes, required distributions, and withdrawals from each account, not a shortcut formula. The planner recalculates the odds each time you change a what-if field, so 1,000 runs keeps that loop to a few seconds.
What moves the answer much more
Now compare that half-point of noise with changing the inputs. Same plan, 5,000 runs each:
| Change | Chance the money lasts to 92 |
|---|---|
| As entered (7% return) | 72% |
| Expected return 6% | 56% |
| Expected return 8% | 84% |
| Spending 10% higher | 51% |
One percentage point of expected return moved the odds by about 14 points. Four thousand extra runs moved them by less than one.
That is the real limit of any Monte Carlo number. The simulation can only be as good as its assumptions: the average return, how much returns swing from year to year, inflation, and the model used to generate random markets. Beagle draws each year's return from a normal distribution, with volatility of 12% for stocks and 6% for bonds blended by your allocation. Real markets have fatter tails and runs of bad years, which this model does not capture. Running it 5,000 times gives you the same model with less noise.
How to read your odds
We may still raise the run count where waiting once is fine, such as a downloaded report. For the live planner, we would rather give you a fast answer you can test against different assumptions.
Stress-test your own plan
Open the sample plan, change the expected return or spending, and watch the odds move.
Measured October 10, 2026 on Beagle's sample plan using the planner's Monte Carlo engine. Timings are from a development computer and will differ on other hardware. Empower's run count is from its own description of its Retirement Planner. Results are educational estimates, not guarantees or personal financial advice.