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OpenAI engagement report

@OpenAI - 5.1M followers on X

Measured over 13 original posts from a 30-day window, last computed on August 31, 2026.

Engagement

Top 10% for its size
Per follower
0.178%
of 5.1M followers
Per impression
0.507%
1.8M views on a typical post
Reach
35.2%
of its followers see a post
Typical post
9.2K
interactions (median)
Saved
0.067%
1.2K bookmarks on a typical post
Posting rate
1.4/day
active 50% of days
Peak time
17:00 UTC
Tuesday

A typical post picks up 9.2K interactions against 5.1M followers, an engagement rate of 0.178%. Measured over 13 original posts, its engagement rate beats 91% of 3,716 tracked accounts of a similar size. Comparing inside a size band matters here: engagement rate falls as accounts grow, so a raw rate would mostly just re-measure the follower count. Posts are seen about 1.8M times each, and 0.507% of those impressions turn into an interaction. That is about 35.1% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.4 post a day over the last 30 days, with activity on roughly 50% of days. Most posts go out around 17:00 UTC, and Tuesday is the busiest day of the week. Of the 13 posts sampled, 77% carry an image or video, 54% are part of a thread and 38% link out. The account's strongest tracked post pulled 38K interactions, about 7.0x its own typical post.

Measured over 13 original posts from a 30-day window, last computed on August 31, 2026.

Compared with accounts its own size

OpenAI's engagement rate beats 91% of the tracked X accounts closest to it in follower count (3,716 accounts, accounts of similar size (decile 10 of 10)). A percentile is spread evenly by construction, so 50 really is the middle of that group and 90 really is its top tenth.

On engagement per impression rather than per follower it beats 39% of the same group. When those two numbers disagree, the gap is about how far its posts travel rather than how people react to them.

Where this sits in the catalog

At 0.178%, OpenAI sits above the 50th percentile of the 36,134 accounts in this comparison. That places it in the above the median band, which runs 0.08% to 0.431%.

p100.002%
p250.012%
p50 (median)0.08%
p750.431%
p902.11%
p99161.6%
Engagement rate as a share of followers, across the 36,134 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 107,713 times apart and a linear axis would flatten everything below the median into a single point.
Show the percentile table
Engagement rate percentiles
PercentileEngagement rate
10th percentile0.002%
25th percentile0.012%
50th percentile0.08%
75th percentile0.431%
90th percentile2.11%
99th percentile161.6%

This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built

Posting timing

This account posts most often around 17:00 UTC, and Tuesday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.

Engagement by hour posted, UTCTwenty-four bars, one per UTC hour. Each bar shows how posts published in that hour compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest hour: 17:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 6%Busiest hour: 17:00 UTC
Show engagement by hour posted, utc as a table
Engagement by hour posted, UTC
Hour (UTC)Vs author medianPosts
00:00 UTC-1%50K
01:00 UTC-2%51K
02:00 UTC-4%49K
03:00 UTC-4%53K
04:00 UTC-6%42K
05:00 UTC-4%41K
06:00 UTC-4%47K
07:00 UTC-5%51K
08:00 UTC-4%60K
09:00 UTC-3%68K
10:00 UTC-2%71K
11:00 UTC-3%77K
12:00 UTC-2%85K
13:00 UTC-2%93K
14:00 UTC-3%96K
15:00 UTC-2%99K
16:00 UTC-3%96K
17:00 UTC-2%89K
18:00 UTC-2%83K
19:00 UTC-2%79K
20:00 UTC-1%73K
21:00 UTC-1%65K
22:00 UTC-1%57K
23:00 UTC-2%51K
Engagement by day of weekSeven bars, one per weekday, Sunday first. Each bar shows how posts published on that day compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest day: Tuesday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 5%Busiest day: Tuesday
Show engagement by day of week as a table
Engagement by day of week
DayVs author medianPosts
Sunday+4%228K
Monday0%281K
Tuesday-2%270K
Wednesday-1%249K
Thursday-2%242K
Friday-3%250K
Saturday+3%225K
See what moves engagement across the whole catalogWhat counts as a good engagement rate at this size

Best tweets

  • Jul 15, 20267.0x their median

    Or… what if we gave you $100 in Codex credits if you tell us what you love about GPT-5.6 Sol or why you switched? Tweet it, claim your gift, enjoy more usage. First 10k get the free tokens! https://t.co/8mU93eA13i

    16K3.7K13K5.7K6.4M viewsView on X
  • Jul 8, 20263.3x their median

    Introducing GPT-Live, a new generation of voice models for natural human-AI interaction. Rolling out in ChatGPT starting today. You’ll want to turn the sound on for this one. https://t.co/WzoQFvA5ir

    24K2.2K1.3K1.9K8.4M viewsView on X
  • Jul 30, 20262.7x their median

    We are committed to pushing the model frontier across cost efficiency, capability, and speed. Starting today, we are reducing prices for GPT-5.6 Luna by 80% and GPT-5.6 Terra by 20% , and offering a faster option for GPT-5.6 Sol in the API. Luna and Terra’s lower prices are reflected in how usage is counted in Codex and ChatGPT Work, so your usage goes further.

    20K2.0K1.4K1.9K21M viewsView on X
  • Jul 10, 20261.9x their median

    Hello beautiful people! We have reset usage limits across Codex and ChatGPT Work. And another one will come later in the day. Rejoice. Now that I have your attention, a quick update on ChatGPT Work, Codex and all the updates we shared yesterday. We’ve spent the last 24 hours reading feedback, looking at usage patterns, and talking with many of you. The short version is that there is a *lot* of excitement for GPT 5.6 Sol, ChatGPT Work on mobile & web, but also that we didn't get everything quite right. - We made it too easy to use the highest-compute settings without making the impact on usage limits sufficiently clear. - We reorganized the desktop app in one bold move, making familiar things like chats and projects harder to find. - Our launch framing was focused on ChatGPT Work and to some of our Codex fans it made it feel like Codex was going away over time. Absolutely not our intention, we love Codex and it is here to stay. - And we introduced regressions for some existing multi-agent workflows, alongside a collection of rough edges in plugins and other parts of the experience. We’re landing a first set of improvements today. We’re resetting usage twice so people can keep experimenting, changing defaults and the model picker so they don’t push people toward unnecessarily expensive settings, fixing several plugin submission issues, improving how we represent Codex in the product, and cleaning up some of the most immediate desktop problems. A larger set of improvements will land next week. We’re bringing chats and projects back into the sidebar in a more familiar and customizable way, making usage and reset timing much more visible, clarifying when to use ChatGPT Work and when to use Codex, and addressing the many other smaller pieces of great feedback we've had. The ambition behind this launch hasn’t changed. We think bringing ChatGPT and Codex together into a workspace where people and agents can collaborate is a very important step forward. But an ambitious direction doesn’t excuse avoidable confusion or regressions in the first version. Please keep the feedback coming. We’re moving quickly, and you should see the experience already get better with a few updates today; and substantially better again next week.

    14K8232.0K4492.3M viewsView on X
  • Aug 25, 20261.8x their median

    Since announcing Jalapeño, our first custom inference chip, we’ve been testing it and the system around it. The results show a major advance: more intelligence from every watt and faster responses, delivering both higher throughput and lower latency in one architecture without sacrificing efficiency.

    14K1.1K6625722.6M viewsView on X
  • Aug 21, 20261.8x their median

    As we continue to push the frontier of capabilities while improving efficiency, we're dropping API and credit pricing of GPT-5.6 Sol by over 20% for the next 3 months. https://t.co/UoTb3hcB2t

    14K8557236083.8M viewsView on X
  • Aug 3, 20261.7x their median

    An internal version of our next major model produced 10 new results on long-standing open problems in mathematics and theoretical computer science, using roughly $2,000 worth of tokens at GPT-5.6 Sol API rates. https://t.co/4cgowmPOpY

    14K1.0K5843731.8M viewsView on X
  • Jul 29, 20261.7x their median

    We quietly released the open-source Codex Security CLI, but Hacker News found it before we had a chance to share it here... You can now use it to scan repositories, track findings across runs, verify fixes, and add security checks to CI/CD. This is an early release, and we're listening to your feedback as we continue improving it.

    14K1.2K4612401.4M viewsView on X
  • Aug 13, 20261.6x their median

    ChatGPT can now remember your activity across the apps and websites on your computer. With Computer History in the desktop app, future interactions feel more personalized and require less explanation. https://t.co/WHZPxPp31R

    12K9788268374.3M viewsView on X
  • Aug 11, 20261.5x their median

    Now in preview: The ChatGPT desktop app for Linux. Use ChatGPT, ChatGPT Work, and Codex where you already work and build, with your projects and browser workflows on supported Linux systems. https://t.co/OtsPt5N5QC

    12K9907996012.4M viewsView on X

Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.

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Reading these numbers

A typical post picks up 9.2K interactions against 5.1M followers, an engagement rate of 0.178%. Measured over 13 original posts, its engagement rate beats 91% of 3,716 tracked accounts of a similar size. Comparing inside a size band matters here: engagement rate falls as accounts grow, so a raw rate would mostly just re-measure the follower count. Posts are seen about 1.8M times each, and 0.507% of those impressions turn into an interaction. That is about 35.1% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.4 post a day over the last 30 days, with activity on roughly 50% of days. Most posts go out around 17:00 UTC, and Tuesday is the busiest day of the week. Of the 13 posts sampled, 77% carry an image or video, 54% are part of a thread and 38% link out. The account's strongest tracked post pulled 38K interactions, about 7.0x its own typical post.

What is OpenAI's engagement rate on X?
OpenAI (@OpenAI) has an engagement rate of 0.178%, based on the median interactions across 13 original posts from the last 30 days against 5,142,120 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.178%, OpenAI sits above the 50th percentile of the 36,134 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
Does @OpenAI have real engagement?
Its engagement rate beats 91% of the tracked X accounts closest to it in follower count (3,716 accounts), which puts it in the top 10% for its size group. Ranking inside a size band matters because engagement rate falls as accounts grow, so a raw rate would mostly re-measure the follower count. It is a starting point for a look at follower quality, not a verdict on it.
When does @OpenAI post?
Most posts go out around 17:00 UTC, and Tuesday is its busiest day, at roughly 1.4 posts per day across the measured window.

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