MagicPost · LinkedIn Benchmark
February 2026
Impressions per post by follower count · 26,569 posts analyzed| Followers | Low | Median | High | Top 10% |
|---|---|---|---|---|
| 0-1K | 91+5% | 208+7% | 456+5% | 1,079+7% |
| 1K-5K | 223-6% | 499-5% | 1,168-2% | 2,865-3% |
| 5K-10K | 382-14% | 848-7% | 2,082-0% | 5,650-1% |
| 10K-25K | 659-4% | 1,540-6% | 3,760-0% | 11.2K+6% |
| 25K-50K | 1,234+4% | 3,276+10% | 8,928+21% | 24.6K+17% |
| 50K-100K | 2,635+0% | 7,067+19% | 18.3K+11% | 50.6K+19% |
| 100K+ | 7,468+37% | 16.9K+29% | 41.9K+17% | 106.9K+7% |
Low = bottom 25% · Median = middle post · High = top 25% · Top 10% = best decile
Six-month trend
Median impressions for a LinkedIn postimpressions on a image post
+NaN% vs the same author's other posts
+NaN% vs the same author's other weekdays
This month, no content type, format, or writing style stands out reliably enough to explain variations in reach. The differences observed stay within the usual range of background noise, with no clear signal emerging. On the question of AI-assisted writing, the numbers do not support a penalty. When comparing the most AI-like and most personal posts from the same creator, the difference in reach is too small to rule out chance. Posts written in a highly automated style reached around 90% of their author's usual impressions, while posts with a strongly personal voice reached around 98%, but this gap of a few points does not hold up as a consistent pattern. Put simply, writing style does not appear to be the driving factor. What the data points to instead is content that is generic and interchangeable, posts that could just as easily have been written by anyone, losing reach regardless of how they are written. The actionable takeaway remains the same: before posting, ask whether this is something only you could have written.