MagicPost · LinkedIn Benchmark
May 2026
Impressions per post by follower count · 18,784 posts analyzed| Followers | Low | Median | High | Top 10% |
|---|---|---|---|---|
| 0-1K | 102+17% | 207+8% | 467+11% | 1,069+7% |
| 1K-5K | 174-10% | 413-6% | 1,034-2% | 3,033+7% |
| 5K-10K | 278-19% | 689-10% | 1,880-5% | 5,544+6% |
| 10K-25K | 592-15% | 1,492-11% | 4,052-14% | 12.7K-10% |
| 25K-50K | 748-43% | 2,242-30% | 6,932-25% | 22.7K-23% |
| 50K-100K | 2,742-18% | 6,991-13% | 19.3K-13% | 49.8K-16% |
| 100K+ | 5,312-15% | 11.8K-16% | 27.6K-14% | 71.8K-18% |
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
+35%vs text posts
+11%vs the median
Generic advice posts, the ones built around "5 lessons I learned" or one-size-fits-all playbooks, reached 14% fewer impressions than the same author's usual baseline. That is the clearest pattern in this period: content that could have been written by anyone in the field loses ground in a measurable way. The idea that LinkedIn penalises AI-sounding writing does not hold up in the data. When comparing the most AI-like and the most personal posts from the same creator, the difference in reach is negligible and points in no consistent direction. The style of writing is not what drives the loss. The substance is. The practical takeaway follows from that: before posting, ask whether the content could just as easily be attributed to any other voice in your space. That is where the exposure risk lies, not in how the sentences are phrased.