MagicPost · LinkedIn Benchmark
June 2026
Impressions per post by follower count · 22,724 posts analyzed| Followers | Low | Median | High | Top 10% |
|---|---|---|---|---|
| 0-1K | 93-9% | 207-3% | 454-4% | 1,143+3% |
| 1K-5K | 170-9% | 388-9% | 981-9% | 2,800-7% |
| 5K-10K | 288-7% | 771-7% | 2,136-4% | 6,454-9% |
| 10K-25K | 420-25% | 1,168-18% | 3,593-9% | 11.9K-4% |
| 25K-50K | 986+35% | 2,666+13% | 7,703+9% | 22.7K-3% |
| 50K-100K | 2,126-6% | 4,916-4% | 14.8K-1% | 37.5K-12% |
| 100K+ | 4,622-8% | 11.6K-6% | 30.6K+3% | 79.8K-11% |
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 pattern stands out in a reliable way: neither format, nor topic, nor writing style shows a clear link to impressions landing above or below each creator's usual baseline. The hypothesis that LinkedIn penalises AI-sounding posts does not hold up. When comparing the same creator's most AI-like posts against their most human-sounding ones, the difference in reach is not established. The gap observed between the two groups, just over 13 points of relative reach, is not strong enough to draw a rule from. The practical takeaway stays the same: what tends to cost impressions is content that is hollow and interchangeable across creators, not the writing style itself.