What is a good engagement rate on Instagram?

A good Instagram engagement rate is one that compares well with similar accounts using the same formula, format, and measurement window. There is no single percentage that fairly describes every creator and brand. For reference, Rival IQ's 2026 report puts the median brand account at 0.30% of followers per post, while Buffer's reach-based data puts its 2025 Instagram median at about 5.4%.¹² Published studies use different audiences and denominators, so check the method before adopting a target.

What do published Instagram benchmarks actually say?

As of November 10, 2026, useful published references include Rival IQ's follower-based brand benchmark and Buffer's reach-based research. Read them as descriptions of their samples, rather than thresholds Instagram sets for your account.¹²

The table separates the headline percentage from how it was produced:

Rival IQ's report states its follower-based formula, its sampling method and the 0.30% median.¹ Buffer supplies the second row's scope and method; the headline sample covers the whole report, not an Instagram-only sample.²

These figures cannot tell you which study's participants make better content. They describe different populations using different measures.

There are also limits within the sources. Rival IQ's sample is brand accounts, includes paid posts, and added five industries this year, which the report says slightly shifts the medians.¹

Why one benchmark is below another

The denominator can explain a large difference without any change in content quality. A follower-based rate compares actions with the account's audience size. A reach-based rate compares actions with the audience that actually saw the post.

For an example, suppose a post gets 50 interactions, belongs to an account with 10,000 followers, and reaches 1,000 accounts. Its follower-based rate is 0.5%, while its reach-based rate is 5%. Those are two valid descriptions of the same post using the formulas described in the studies.¹²

Other methodological differences matter too:

• Actions counted: likes and comments alone are a different numerator from likes, comments, shares, and saves.
• Mean or median: a mean gives unusually high results more influence; a median identifies the middle result.
• Account mix: businesses, creators, and accounts using a particular publishing service are different samples.
• Paid activity: a mixed paid-and-organic sample is a different comparison from an unpaid-only report.
• Time: a current dashboard number and a study based on an earlier period do not describe the same conditions.

Buffer explicitly limits its study to content published through its service. Its method generally uses 2025 baselines, with minimum account activity requirements, and notes that some format analyses use other windows.² That is useful context for a small team comparing a handful of posts with a report containing many active accounts.

Do not average different studies' headline rates into a new “industry standard.” That would combine incompatible measures and conceal the distinctions you need.

How follower count changes the comparison

Match audience size when choosing peers. An account with 1,000 followers and 40 interactions has a follower-based rate of 4%. An account with 10,000 followers and 200 interactions has a rate of 2%. The larger account gets more actions while showing a lower rate. These are arithmetic examples, not measured size-tier benchmarks.

Ranges such as “1% to 5%” are widely repeated, but many do not state their denominator. Before using one, check whether it divides by followers or by reach. Buffer's size-tier article reports rates generally dipping as follower count climbs, then ticking back up for accounts over 1 million followers.³

Published size-tier data can help, but only when the formula is identifiable. Buffer's September 2025 article analyzes more than 27 million posts from 273,000 accounts and presents different results by follower tier. It links to a general formula explanation rather than spelling out a study-specific denominator in the article, so use that page to understand its segmentation and verify the method before borrowing its percentages.³

For your own peer list, choose accounts that serve a similar audience, publish comparable content, and sit in a similar follower range. Compare a stable group over time. Replacing peers whenever one has a strong month makes your reference point difficult to interpret.

Public metrics also limit what you can compare. If your own rate includes saves and another account's visible rate does not, create a second calculation using only the actions available for both. Keep your more complete internal rate as a separate measure.

Does a good rate differ for Reels and carousels?

Yes, format belongs in the comparison. Buffer's 2026 report gives reach-based format medians of 6.90% for carousels, 4.44% for single images, and 3.31% for Reels. Its format section describes likes, comments, saves, and shares relative to reach. Buffer's methodology says its Instagram format analysis uses posts from January 2022 to October 2024, so these are historical sample results, not November 2026 platform targets.²

Use that evidence to justify separating formats in your report. It does not establish that switching formats will produce those rates for you.

A demonstration video and a reference carousel may have different purposes. Evaluate a video intended to explain a process with viewing measures as well as interactions. Evaluate a reference post with saves as well as its overall rate. Keep the goal visible so the highest percentage does not automatically decide your content mix.

The Instagram Insights guide explains where to find those underlying counts. The engagement formula guide shows how to keep your calculations consistent.

How to benchmark against your own average

Your own recent, comparable posts are a practical starting point. Build a baseline you can reproduce, then use an outside study as context.

The engagement rate calculator works out a post's rate by followers or by reach and shows the published median that uses the same formula.

1. Choose a reporting period and one format. Keep paid and organic content separate.
2. Select one numerator and denominator. Record them beside the result.
3. Capture each post after the same amount of time has passed since publication.
4. Calculate individual post rates, their mean, and their median. Keep totals nearby.
5. Compare the next group with that baseline and review the actual posts behind a change.

Here is an example. Five comparable posts have reach-based rates of 3%, 4%, 4%, 5%, and 14%. Their mean is 6%, but their median is 4%. A new post at 5% exceeds the middle result while falling below the mean. Neither comparison makes it automatically good or bad; the difference shows why you should inspect the unusually high post.

Also ask whether the post served its goal. Did it prompt relevant comments, saves, profile visits, or inquiries? A higher rate accompanied by much lower reach might produce fewer useful actions. A lower rate with more relevant responses might deserve another attempt.

Finish the review with a test you can repeat: adapt the topic of a strong educational post, keep the format consistent, and compare its response with your baseline. Use the broader social media metrics guide to connect that test to the outcome you wanted.