MagicPost · LinkedIn Benchmark
January 2026
Impressions per post by follower count · 37,646 posts analyzed| Followers | Low | Median | High | Top 10% |
|---|---|---|---|---|
| 0-1K | 87-2% | 195-6% | 436-8% | 1,006-0% |
| 1K-5K | 238+5% | 523+5% | 1,192+4% | 2,946+9% |
| 5K-10K | 445+7% | 917+5% | 2,092+6% | 5,686+19% |
| 10K-25K | 690+12% | 1,632+14% | 3,762+9% | 10.5K+13% |
| 25K-50K | 1,192+31% | 2,992+22% | 7,352+22% | 21K+29% |
| 50K-100K | 2,627+4% | 5,925+13% | 16.6K+23% | 42.6K+26% |
| 100K+ | 5,468+38% | 13.1K+29% | 35.7K+55% | 99.5K+53% |
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
Posts built on cascades of very short sentences, stacked one after another, reach about 5 % fewer people than the same author's typical posts. The gap is not dramatic, but it holds consistently and does not reverse. On the question of whether LinkedIn penalises content that feels heavily automated: the data gives a mixed answer. Across all posts, the link between an AI-like writing style and lower reach is almost undetectable. However, when comparing the most automated posts to the most personal ones from the same creator, a real penalty shows up: the automated posts reach roughly 10 % fewer people than the personal ones, within the same accounts. So it is not automated style in the abstract that costs reach, it is content that feels generic and could have come from anyone. The practical takeaway is straightforward: before reworking your writing style, check whether what you are posting brings something distinctly yours. That is where the difference is made.