Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 10 September 2026 and assigned it the closest of 31 fixed categories, at 100% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
Growth
31 measurements spanning 42 days, net +508. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 17,064–17,724 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 31
Measured (UTC)
Subscribers
Change
18 Sept 2026, 05:57
17,648
+18
16 Sept 2026, 00:38
17,630
+18
14 Sept 2026, 04:22
17,612
+1
12 Sept 2026, 09:36
17,611
+2
10 Sept 2026, 07:21
17,609
+47
7 Sept 2026, 00:37
17,562
+6
4 Sept 2026, 08:17
17,556
+6
2 Sept 2026, 22:57
17,550
-2
2 Sept 2026, 00:23
17,552
+7
1 Sept 2026, 03:48
17,545
-7
31 Aug 2026, 02:52
17,552
+9
29 Aug 2026, 05:36
17,543
-8
28 Aug 2026, 02:33
17,551
+11
27 Aug 2026, 00:32
17,540
+6
25 Aug 2026, 22:23
17,534
+9
24 Aug 2026, 20:15
17,525
-9
23 Aug 2026, 03:45
17,534
+7
21 Aug 2026, 17:47
17,527
-10
20 Aug 2026, 17:53
17,537
-13
19 Aug 2026, 15:07
17,550
first reading
Engagement
22 posts held, back to 24 July 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 55 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
11.3%
avg views ÷ 17,648 subscribers
Avg views / post
2,000
4 posts measured
Reaction rate
1.50%
reactions ÷ views · ER floor
Posts in window
4
of 22 held
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.
What these figures were computed from
Window
Rolling 30 days · latest post in window 1 September 2026
Posts held
22 (24 July 2026 – 1 September 2026)
Views total
8,010
Reactions total
120
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
3 Sept 2026, 09:13 UTC
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
What this channel posts
Video runtime
3m 50s
Average length
21s
Measured directly from 11 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
Reaction mix
689 reactions across 22 posts, in 14 distinct kinds. The most used accounts for 34.1% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
🔥
235
34.1%
👍
178
25.8%
❤
169
24.5%
custom 5352647290932711418
25
3.63%
⚡
24
3.48%
🤣
19
2.76%
😁
15
2.18%
🥴
8
1.16%
😱
6
0.871%
🤝
4
0.581%
🤔
3
0.435%
🆒
1
0.145%
👌
1
0.145%
🙏
1
0.145%
Custom emoji. One row above is a Telegram custom emoji, which the public preview renders as an element carrying only a numeric id — no character, and no image we can reach. The id is printed as-is rather than substituted with a look-alike glyph, because a stand-in would be our invention showing where a measurement should be. The count beside it is Telegram’s.
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 22 of the 22 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 689 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 22 most recent posts we hold, published 24 July 2026 to 1 September 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
Telegram Stars
Stars received
37
across the posts below
Posts paid on
14
of 22 we hold a reading for · 64%
Most on one post
7
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @gptunnel. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.
Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.
This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.
Counted over the 22 most recent posts we hold for this entry, published 24 July 2026 to 1 September 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.
В GPTunneL появился Claude Fable 5.1. 🚀
Anthropic обновила свою топовую линейку и модель уже доступна у нас.
Что нового по сравнению с Fable 5:
— Новая планка в кодинге, работе со знаниями и долгих многошаговых задачах
— Модель не срезает углы ради быстрого ответа и умеет чинить первопричину проблемы, а не симптом
— На низком и среднем уровне усилий даёт такой же или лучший результат, чем Fable 5, — и заметно деше…
MiniMax H3 Max — модель, которая создаёт видео быстрее, чем оно длится⚡️
⏺480p — 4₽/сек
⏺768p — 6.4₽/сек
Скидка 60% до 1 сентября 👀
Уже в GPTunneL ⬅️
MAX | Буст каналу | YT | TT
Вышла Gemini Omni 1.1 Flash от Google
Новая мультимодальная модель для быстрых генераций и работы с видео.
Что интересного:
🟠Поддерживает качество 4K;
🟠Есть инструмент первого и последнего кадра;
🟠Можно вставлять референс-картинку, референс-видео и исходное видео;
Отличный вариант для тех, кто занимается созданием роликов, рекламных креативов и контента для соцсетей. Модель уже доступна в GPTunneL.
Переходите и т…
🟠Запускаем серию видео по работе с Grom TV
Разбираем всё с нуля: как работать с моделью и как составлять промпты, чтобы получать нужный результат.
Неважно, есть у вас опыт или нет: всё равно найдёте что-то полезное для себя, без лишней воды и сложных терминов.
Новое видео уже на канале:
«Grom TV с нуля: первый шаг к идеальной генерации» 👈
Во втором канале делимся крутыми креативами и идеями по работе с ИИ
MAX | …
🟠Теперь в Воркфлоу доступны все модели
Раньше можно было выбрать только часть моделей, сейчас доступна любая.
Стараемся делать сервис удобнее для вас. Заходите и собирайте воркфлоу без ограничений 👈
Примеры работ и готовые воркфлоу ищите во втором канале
MAX | Буст каналу | YT | TT
📷 Убираем лишние элементы и водяные знаки с фото за пару кликов
Нужен чистый визуал, а на изображении водяной знак или ненужный объект в кадре? Не обязательно лезть в фоторедактор.
Показали на примерах, как в Воркфлоу убрать лишние элементы. На выходе чистое изображение, аккуратная текстура на месте удалённого объекта и высокое разрешение.
Забирайте Воркфлоу 👈
🧿Больше об инструментах и создании контента в нашем в…
🎥 У нас появились видеоуроки на YouTube!
Рассказываем, как эффективнее пользоваться GPTunneL: повышение качества фото, замена лица, работа с Grom Art и Grom Pixel и другие полезные функции.
Дальше будет ещё больше интересного: разберём, как озвучивать текст голосом и создавать свои песни.
Переходите, подписывайтесь и следите за обновлениями 👈
Во втором канале показываем готовые работы и даем промпты к ним.
Gemini 3.7 Flash в GPTunneL: вдвое дешевле и заметно умнее в коде
Завезли новую Gemini 3.7 Flash из семейства Gemini 3.
На тесте FrontierCode, который проверяет качество продакшн-кода, модель показывает 43,6%. Это выше, чем у Claude Sonnet 5 и GPT-5.6 Terra. При этом сама модель работает быстрее предыдущей версии, а стоит дешевле.
Gemini 3.6 Flash - 300₽ / 1500₽
Gemini 3.7 Flash - 150₽ / 750₽
Контекст: 1 миллион …
Больше визуальных экспериментов с нейросетями во втором канале
👉 Публикуем работы наших креаторов и делимся промптами.
👉 Уже протестировали Flux 3, Seedance 2.5 и MiniMax H3. Сравниваем новые модели и показываем результаты.
Пока вы читаете, мы уже генерим 👀
Обновили инструмент Speech-to-Text
Под капотом новые модели, качество транскрибации стало заметно лучше. Плюс теперь результат можно сразу обработать в LLM: сделать саммари, заметки встречи или написать свой запрос по файлу.
Заходите и пробуйте 💬
А примеры креативов с новых моделек смотрите в нашем втором канале
MAX | Буст каналу | YT | TT
Seedance 2.5 уже в GPTunneL
ByteDance улучшили стабильность персонажей, управляемость сцены и точность работы с промптом. Видео стало длиннее и связнее, а контроль над деталями заметно вырос.
Переходите и пробуйте одними из первых 👈
А примеры креативов с новых моделек смотрите в нашем втором канале
💡 Обновили посадочную страницу на сайте GPTunneL
Поработали над дизайном и структурой: сделали её удобнее, симпатичнее и понятнее. Всё для того, чтобы пользоваться сервисом было ещё комфортнее.
Загляните на главную и поделитесь впечатлениями в комментариях 💬
А примеры креативов с новых моделек смотрите в нашем втором канале
🔥21❤10custom 53526472909327114189
Showing the 12 most recent of 22 posts we hold for @gptunnel. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Stars beside a post are paid reactions — Telegram Stars, bought with money and spent on that post. They are a different unit from reactions and are never added to them, here or anywhere else on this page.
Posts edited after publishing
@gptunnel edited 3 posts after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.
An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.
First edit seen
10 August 2026
Most recent edit
10 August 2026
Mentions
Named by 2 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
Appears in Telegram’s recommendations for other channels
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
Промты для нейросетей @iidlyabi · 22,600 Telegram ranks this channel #40 of 87 here — alongside 86 others — read 20 September 2026
This channel appears in 1 seed channel's Telegram-generated recommendation list in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
Cite this entry
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 18 September 2026 — this
entry's latest reading, not the date you are reading this.
“GPTunneL” (@gptunnel), 17,648 subscribers as measured 18 September 2026. Telegram Register, tgregister.com/channel/gptunnel.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.