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Как измеряют LLM: параметры, бенчмарки и тесты на коленке

В комментариях к моей предыдущей статье о тестировании трех флагманских LLM моделей были примерно такие мысли и вопросы:

  • Я взял простую бесплатную LLM, запустил локально и она тоже справилась.

  • А почему вы в свое сравнение не взяли никого от DeepSeek, они же тоже хороши?

  • А зачем всем один и тот же промпт, они же по-разному их воспринимают?

Это логичные комментарии. Мир LLM вообще нелинейный и тут иногда бывает трудно провести грань между мощной и слабой моделью, понять кому какие задачи ставить и в каком виде подавать промпт на вход. И все же как-то в этом мире модели тестируют, классифицируют и определяют их условное качество.

В этой статье я хочу рассказать про общий подход к определению мощности моделей и пройтись по нюансам. Потом покажу самые популярные бенчмарки. И, наконец, расскажу, на что смотрят в бытовых тестах. Думаю, это будет правильно, прежде чем мы продолжим серию статей про сравнение разных LLM.

Важно! Эта статья написана для всех, кто хочет разобраться в теме, максимально простым языком. Я постараюсь не усложнять без необходимости и иногда буду приводить примеры из жизни. Надеюсь, вы простите мне некоторые упрощения.

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Параметры

Число параметров – это первая характеристика модели, которую мы слышим. Нередко она даже вшита в название. Например, Llama-2 70B, что означает, что у модели 70 миллиардов параметров. Это много или мало? Давайте разберемся.

Параметры бывают трех основных видов:

Эмбеддинги — это таблицы перевода, точнее вектора с координатами в многомерном пространстве. Они превращают каждый токен в набор чисел. До обучения эти числа случайны и ничего не значат. Смысл появляется в процессе обучения: модель видит, как токен ведет себя в миллиардах примеров, и подбирает ему такие числа, чтобы похожие по смыслу токены или группы токенов получили похожие наборы. Например, слова шкаф и стойка не похожи по написанию, но в контексте дата-центров могут быть синонимами.

Веса — это главное. Это числа, которые модель подобрала при обучении и через которые прогоняет каждый токен на всех своих слоях. Если эмбеддинг отвечает на вопрос “что это за токен”, то веса отвечают на вопрос “как понимать его в этом контексте”: на какие соседние слова смотреть и насколько сильно их учитывать. Важные связи модель усиливает, неважные — почти гасит. Весов в модели подавляющее большинство; когда говорят “70 миллиардов параметров”, речь почти целиком про них.

Смещения — помощники весов. На каждом шаге к результату добавляется небольшая поправка-константа, которая чуть сдвигает баланс в ту или иную сторону. Их мало, и на общий счётчик параметров они почти не влияют.

Мне нравится такой пример. В контексте полного имени после слова “Барак” логично ждать “Обама” — на таких продолжениях модель и обучилась, поэтому предсказывает их уверенно. Но срабатывает это не всегда. У бывшего американского президента есть второе имя (как и у большинства граждан США), потому полностью он “Барак Хуссейн Обама”. То есть бывают контексты, где за “Бараком” идёт не “Обама”, а “Хуссейн”. Что выбрать, модель решает сама: взвешивает весь контекст и подбирает самое вероятное продолжение, а в пограничных случаях её слегка подталкивают те самые смещения.

Есть четвертая вещь, которую часто путают с параметрами — гиперпараметры. Это число слоев и размер модели (задаются до обучения), температура при генерации (выставляется уже на инференсе) и подобные настройки. Разница простая: параметры модель подбирает сама во время обучения, а гиперпараметры до обучения выставляет человек. В число параметров они не входят.

Бывают модели с сотнями миллиардов или, наоборот, миллиардами параметров. Но сама по себе эта цифра, вырванная из контекста, нам ни о чем не говорит.

Потому что судить о качестве и мощности модели по этому критерию – примерно как судить о машине по числу лошадиных сил в ее двигателе. Кое-какую информацию нам это число сообщить может, но дальше важна задача, которую мы решаем этой машиной. 180 лошадей в микролитражке и грузовике “Урал” – это прямо очень разные лошади в очень разном контексте использования.

Но что мешает нам напрямую сравнить модели по числу параметров? Ведь LLM общего назначения должны решать примерно одни задачи и работать в одном классе?

Архитектура. Есть модели, которые прогоняют каждый токен запроса через все свои параметры, грубо говоря задействуют себя на полную мощность. Такие модели называются плотными. Однако, практика показала, что это не всегда требуется. Поэтому появилась архитектура MoE (Mixture of Experts, “смесь экспертов”). Она поступает иначе: вместо одной большой сети в каждом слое стоит несколько маленьких подсетей — “экспертов” — а специальный маршрутизатор решает, к каким из них направить конкретный токен.

Получается, что в MoE задействуют не все параметры, а только их часть. Например, Mixtral 8x7B имеет 46,7 миллиарда параметров суммарно, но на каждый токен активирует лишь около 13 миллиардов. Она прекрасно справляется с задачами и обходит многих конкурентов, но 46,7 миллиардов в ней все же не аналогичны 46,7 миллиардам у плотной модели.

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Качество обучения. В начале 2020-х бытовало упрощенное мнение: чем больше параметров, тем умнее модель. Его подкрепляли первые законы масштабирования OpenAI: из них следовало, что вычислительные ресурсы выгоднее вкладывать в размер модели, а данные вторичны. Но в 2022 году DeepMind работой про Chinchilla показала, что это не так.

Оказалось, что нужно не просто наращивать число параметров, а гармонично увеличивать и количество тренировочных токенов. Модели-гиганты вроде GPT-3 были “недокормлены” данными. Llama-2 70B, обученная на 2 триллионах токенов, обходила куда более жирный GPT-3 175B по большинству бенчмарков просто потому, что ее качество обучения было выше.

Специализация. В целом, если мы говорим про LLM общего назначения, то есть такие, которые одинаково хороши (или плохи) в разных задачах. Однако, почти каждая нейронка имеет “перекос”, какие-то качества у нее развиты лучше, а какие-то сильно страдают.

Здесь приведу пример из практики. Чтобы оценить логику изложения в своих статьях, после написания я всегда прогоняю их через ChatGPT и DeepSeek. Делаю я это для того, чтобы мне указали на возможные фактические ошибки, орфографию или проблемы по стилю/логике изложения.

И при прочих равных DeepSeek – это такой рубаха-парень, который объясняет все более бытовым языком, отлично подмечает места, где я перемудрил, но крайне невнимателен к фактам.

ChatGPT – это интеллигентный и аккуратный нудила. Он докопается до каждой буквы, уточнит каждую формулировку, обратит внимание на каждую нестыковку. Но он жутко душнит и предлагает такие замены для статьи, которые убьют всю ее живость и превратят в учебник. Очень скучный, но точный учебник.

Потому, можно попасть в больное место очень хорошей LLM и получить ерунду. А можно плохенькую нейросеть использовать для того, в чем она сильна и вполне себе радоваться жизни.

Бенчмарки

Итак, число параметров - это информация, которую можно воспринимать только в контексте задачи, условий использования, точности промпта и прочих факторов. Но как же сравнивают нейронки и делают выводы, что условная Fable (которую толком никто не успел пощупать) ужасно мощная?

Для этого используют бенчмарк-тесты. Точнее пытаются использовать, ибо с ними тоже есть проблемы.

Бенчмарки — это стандартизированные наборы задач, на которых гоняют все модели подряд, чтобы получить сравнимые цифры. Их сегодня сотни, и в них легко утонуть. Давайте пройдемся по основным и посмотрим, что они там проверяют.

Чтобы не утонуть, держите в голове четыре главные метрики: бенчмарки на эрудицию (что модель знает), на рассуждение (умеет ли реально думать), на здравый смысл и отдельно — на мультиязычность. Ниже примеры.

MMLU (Massive Multitask Language Understanding) — самый цитируемый бенчмарк последних лет. Это около 16 тысяч вопросов с вариантами ответов по 57 предметам: от элементарной математики и анатомии до права, философии и микроэкономики. По сути, проверка широты эрудиции. Долгое время именно MMLU был эталоном сравнений.

Но у успеха есть обратная сторона. Сегодня MMLU устарел: топовые модели набирают на нем за 90%, и разница в один-два процента уже теряется в шуме. Грубо говоря, это перестало быть экзаменом для сильнейших и стало тестом на грамотность. Почему? Потому что растет общий уровень моделей. Что было круто в 2022 году сегодня уже выглядит слабенько.

Чтобы вновь сделать оценку показательной появился MMLU-Pro — версия с более сложными вопросами и большим числом вариантов ответа.

GPQA Diamond — это вопросы уровня PhD по биологии, химии и физике, причем настолько сложные, что непрофильные специалисты с доступом к интернету ошибаются на большой их части. Если модель уверенно берет такой барьер, ей можно доверить разбор по-настоящему сложного научного, юридического или финансового материала. Именно GPQA многие сегодня считают золотым стандартом: он измеряет не красноречие, а способность реально соображать.

Humanity's Last Exam (HLE) — “последний экзамен человечества”, около трех тысяч экспертных вопросов из десятков дисциплин, специально собранных так, чтобы пробить потолок насыщения (ситуация, когда тесты становятся слишком простыми для большинства).

ARC-AGI — отдельная история: тесты на абстрактное мышление, где надо угадать правило по нескольким примерам. Для человека это головоломки уровня детских, для LLM — на удивление трудные, потому что тут нельзя “вспомнить” ответ из обучающих данных, надо именно вывести закономерность.

HellaSwag — здравый смысл через продолжение фразы. Дается начало бытовой ситуации, надо выбрать самое правдоподобное продолжение из четырех. Для топ-моделей он давно насыщен (за 95%), но остается хорошей лакмусовой бумажкой для маленьких и дообученных моделей.

BIG-Bench Hard (BBH) — набор из 23 заданий, специально подобранных так, чтобы их нельзя было решить “срезав угол”: логические выводы, причинно-следственные цепочки, алгоритмические задачки. В отличие от MMLU, BBH без пошагового рассуждения не взять, и потому он лучше предсказывает реальное мышление у моделей среднего эшелона.

Отдельная боль и отдельная категория – мультиязычность. Большинство классических бенчмарков — англоязычные, и высокий балл на них ничего не говорит о том, как модель справится с русским, хинди или суахили. Поэтому появились Global-MMLU (тот же MMLU, переведенный и культурно адаптированный на четыре десятка языков) и MMLU-ProX (многоязычная версия MMLU-Pro на тринадцать типологически разных языков). Для нас с вами это важно: модель, блестяще работающая на английском, на русском может оказаться заметно слабее. И проверять это нужно отдельно.

Продолжать можно долго. Но слепо доверять бенчмаркам нельзя, ибо у них есть системные болезни:

  • Многие бенчмарки публичны или данные с них уже утекли в публичное пространство. А потому можно натренировать модель на их прохождение и слегка сжульничать. Ведь таким образом можно добавить своей модели маркетингового веса. Это называется законом Гудхарта: “когда метрика становится целью, она перестает быть хорошей метрикой”.

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  • Общий уровень моделей растет постоянно. Бенчмарки быстро устаревают, ибо возникает тот самый потолок насыщения. Это решается новыми бенчмарками, потому процесс оценки меняется и развивается вместе с технологиями.

  • Зависимость от обвязки. Как вы знаете, у моделей есть много помощников (или костылей), которые помогают ей эффективнее справляться с определенными типами задач. Самый яркий пример мы видели в нашем тесте на работу агрегата. Ни одна из флагманских моделей не смогла посчитать то, что Excel считает одной формулой и за секунды. Иногда, правильно настроенные инструменты могут помочь со многими тестами. И вот как такое считать?

Человеческие бенчмарки

Раз автоматические тесты можно обмануть и они насыщаются, родилась альтернатива — спросить живых людей. Так появилась Chatbot Arena (ныне LMArena), запущенная в 2023 году исследователями из Беркли.

Механика гениально проста. Вы пишете запрос, получаете два ответа от анонимных моделей — “Модель A” и “Модель B”. Голосуете за ту, что лучше. И только после голосования вам открывают, кто есть кто. Голоса (а их собрано уже миллионы) скармливаются рейтинговой системе.

Арена ловит то, что не видят автотесты: тон, естественность, умение следовать сложной инструкции, ощущение “этот ответ просто приятнее”. Но и у нее свои грехи.

Главный — смещение к многословию: люди склонны выбирать ответы подлиннее и покрасивее оформленные, даже когда короткий ответ точнее. Из-за этого модели в среднем “разнесло” в сторону болтливости. Поэтому ввели отдельную колонку Style Control, которая делает поправку на длину и форматирование.

Второй грех — арену можно накручивать: исследования показали, что даже несколько сотен скоординированных голосов способны заметно сдвинуть рейтинг.

Живые голоса собирать дорого и медленно, поэтому появился гибрид — LLM-as-a-judge, когда судьей выступает сильная модель, оценивающая ответы других. На этой идее построен Arena-Hard: автоматический набор сложных запросов, который пытается воспроизвести вердикты живой арены. Но у судьи-машины свои перекосы: та же любовь к длине и предвзятость к позиции ответа, так что и здесь нет серебряной пули.

Наконец, есть совсем приземленный рейтинг: OpenRouter и подобные сервисы, через которые разработчики гоняют запросы к десяткам моделей. Здесь люди “голосуют кошельком”, выбирая лучшее сочетание качества и цены под боевые задачи. Подделать реальные деньги и реальную нагрузку куда труднее, чем клики на сайте.

В общем, не существует одного числа, которым измеряется “ум” модели. Бенчмарк на знания, бенчмарк на рассуждение, бенчмарк на код и арена с людьми измеряют разные вещи. Сильная на одном модель может проседать на другом. Грамотный подход – это смотреть не на одну цифру, а на профиль по нескольким разнотипным тестам. Помнить про потолок насыщения, жульничество на тестах, и — обязательно — проверять модель на своих собственных задачах.

Что может мощная LLM и чего не может простенькая

Прежде чем мы перейдем к категориям тестов, которые можем делать сами и объясним их, важно разделить в голове лиги, в которых играют модели. Снова вспомним, как меня спрашивали в комментариях, почему я не включил в обзор DeepSeek. Давайте разберемся с этим вопросом. Итак…

  1. Граница номер один – это глубина рассуждения. Слабая модель отлично справляется с одним логическим шагом, но на цепочке из пяти-шести взаимосвязанных шагов теряет нить: забывает раннее условие, противоречит сама себе, выдает правдоподобный, но неверный итог. Мощная держит длинную цепочку рассуждений связной — каждый шаг опирается на предыдущий. Именно поэтому топовые модели берут GPQA и олимпиадную математику, а простенькие — нет. Если ваша задача требует разложить проблему на части и пройти по ним последовательно, разница будет драматической.

  2. Контекст. Простая модель помнит начало разговора и теряет середину длинного документа. Мощная удерживает связность на десятках и сотнях тысяч токенов: помнит, что вы просили в начале, не противоречит сказанному пять страниц назад, находит нужную деталь в большом тексте. Для работы с документами, кодовыми базами, длинными перепиской это решающее различие. Подробнее о том, как работает контекстное окно я писал здесь.

  3. Работа с кодом и особенно с правками. Слабая модель — это продвинутое автодополнение: допишет функцию, подскажет синтаксис, объяснит ошибку. Полезно, но локально. Мощная модель в связке с инструментами работает как агент: читает большую незнакомую кодовую базу, находит причину реального бага, пишет патч, прогоняет тесты, итеративно правит. Именно это меряет SWE-bench, и именно здесь бюджетные модели резко отстают.

  4. Точность следования инструкциям. Мощная модель надежно выполняет составные требования: формат, ограничения, структуру вывода. Слабая — выполняет частично, особенно когда требований несколько. На уровне болтовни это незаметно, но как только вы строите на модели продукт, где важен предсказуемый формат ответа (JSON, строгая структура, заданная длина), пропасть становится очевидной.

  5. Честность и калибровка. Топовые модели лучше осознают границы своих знаний и чаще честно говорят “не уверена” вместо того, чтобы сочинять. Слабые галлюцинируют охотнее и увереннее. Это не значит, что флагманы не врут вовсе — врут, но реже и аккуратнее, а главное — лучше отличают “знаю” от “придумываю”.

  6. Языки и мультимодальность. Мощные модели прилично работают с десятками языков и понимают не только текст, но и изображения, графики, документы, иногда звук и видео. Бюджетные обычно сильны лишь в английском и нескольких популярных языках и часто вообще не умеют в картинки. Если вам нужен редкий язык или анализ изображения — это сразу отсекает простые модели.

  7. Агентность и инструменты. Самый свежий рубеж. Сильная модель умеет планировать многошаговые действия, вызывать внешние инструменты, ходить в интернет, работать с файлами, выстраивать и корректировать стратегию по ходу дела. Слабая теряется уже на втором-третьем шаге автономного сценария. Поскольку именно агентные применения сейчас на острие, этот разрыв в 2026 году — один из самых практически значимых.

Но! Далеко не везде нужны флагманы. Для огромного числа задач — классификация, извлечение данных, короткие ответы, черновики, простые переписывания, рутинная сортировка — маленькая модель работает прекрасно, в разы быстрее и в десятки, а то и в сотни раз дешевле. Запускать флагман на 671 миллиард параметров, чтобы рассортировать письма по папкам — все равно что ездить за хлебом на том самом “Урале” в 180 лошадей. Технически можно, но на редкость расточительно.

Флагман отвечает медленнее и стоит в разы, а то и в сотни раз дороже за тот же объем текста.

Именно поэтому в серьезных системах применяют маршрутизацию: простые запросы уходят к дешевой быстрой модели, сложные — к мощной. Это экономит огромные деньги без особой потери качества там, где оно реально нужно. Вспомните, как в нашем тестировании Gemini 3.1 Pro буквально уделал всех конкурентов по цене.

Есть и еще одна причина, по которой маленькие модели в последние годы так похорошели — дистилляция. Грубо говоря, большую умную модель-учителя заставляют обучать маленькую модель-ученика, передавая ей не сырые данные, а свои собственные ответы и распределения вероятностей. Ученик получается на порядок компактнее, но впитывает изрядную долю поведения учителя. Поэтому современная “малышка” на несколько миллиардов параметров нередко ведет себя умнее, чем гигант трехлетней давности. Все дело в том, что она основана на большой модели, которая ее натаскала. Это еще один гвоздь в гроб тезиса, что число параметров = ум/мощность/качество.

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И еще одна большая новость последних лет: разрыв между открытыми и закрытыми моделями стремительно сокращается. То, что вчера умели только дорогие проприетарные флагманы, сегодня все чаще доступно в открытых моделях, которые можно запускать у себя, — особенно в задачах вроде программирования, где открытые модели уже почти достали лидеров. И энтузиасты в комментариях так же это подтверждали: локальные модели справились с некоторыми задачами вполне себе на уровне. А по цене взяли только на электричество и амортизацию видеокарт.

Тесты на коленке

ОК, есть число параметров, бенчмарки и понимание, что у каждого теста есть слабая сторона. Можно ли как-то для себя устроить мини-соревнование между моделями, чтобы понять, что лучше подходит именно нам?

Можно. Но для этого нужно знать вашу задачу. Крайне маловероятно, что она будет уникальна. Существует множество тестов для разных типов задач, которые делаются легко и малыми силами. Вот некоторые из них.

Сложные паттерны

Возьмите книгу, которую вы хорошо знаете и загрузите на вход (но следите, чтобы влезть в контекстное окно). Попросите дать нестыковки.

Даже у такой мастерицы сюжета, как Агата Кристи, нестыковки в книгах есть. Но отследить все сюжетные линии в большом романе и найти в них неточности под силу далеко не каждой модели. Если результат получается достойный, высока вероятность, что и с задачей поиска ошибок в документации модель справится.

Языки и перевод

Попросите перевести что-нибудь с английского на русский, сделать это литературно или подражая кому-то. Здесь особенно заметна разница в объеме и качестве обучающих данных: топовые модели держат десятки языков и писательских паттернов, бюджетные плывут на всем, что сложнее английского и пары других популярных языков.

Ответ с жесткими ограничениями

Отличный тест на гибкость — попросить написать текст с искусственным ограничением, при том, что это ограничение может затеряться на фоне другой, более серьезной задачи. Мы делали суммаризацию по LoRaWAN и ограничивали ее 2000 символов. Сильные модели удерживают и смысл, и форму одновременно — а это совсем другой уровень контроля над собственным выводом.

Внимание посередине

Дайте на вход большой текст и спрячьте в середине конкретную деталь, а потом спросите о ней. Слабые модели теряют середину длинного документа, сильные — находят иголку в стоге сена. Мы с вами делали эту проверку на документации LoRaWAN и про этот тест часто пишут. Я его делаю, но, честно говоря, среди серьезных моделей такой проблемы не встречал. Хотя, может еще встречу.

Использование инструментов

Дайте задачу, с которой LLM в силу своей архитектуры справиться не может и оцените адекватность и возможности использования “подпорок”. К примеру, анализ огромного числа данных со сложным паттерном. Мы для этого использовали показатели ампеража агрегата.

Как вы помните, работали мы через API и ни одна из моделей не смогла подтянуть инструмент для расчета. Но Opus хотя бы дал совет по формуле в Excel.

Понимание рисунков

Если в модели заявлено распознавание визуальных образов, имеет смысл взять схему и внести в нее небольшую ошибку. После чего загрузить изображение на вход и посмотреть, найдет ли модель нашу пасхалку. Как вы помните, мы грузили схему-классификацию ИИ-моделей.

Есть задача посложнее: загрузить фото и предложить модели обработать его в стиле какого-то известного фотографа. Я все жду, когда это станет возможно, но именно LLM пока это делают не то что плохо, а прямо ужасно. При этом они хороши в анализе обработки: подсказывают, как именно конкретный фотограф добился того или иного эффекта.

Тест на логику

Задайте задачу, в которой необходимо рассуждать и посмотрите, как отработает его модель. В нашем тест-драйве эту роль выполняла загадка про страну и цветок и число концов у палок. Как вы помните с цветком вышла заминка: там разные ответы дали все три модели. Более того, модели комментаторов тоже давали новые версии.

У этого теста тут есть проблема. Модель может знать ответ. Чтобы этого избежать используют следующий тест.

Тест на шаблонность

Возьмите классическую загадку и слегка ее измените, чтобы шаблонный ответ стал неверным. Слабая модель узнает знакомый узор и выдаст заученный ответ; сильная — заметит подвох.

Пример. Все знают загадку про хирурга, который отказывается оперировать мальчика со словами “это мой сын” (ответ: хирург — мать). Теперь задайте ее в варианте, где явно сказано, что отец и есть хирург, и спросите, как это возможно. Слабая модель на автомате выдаст “это мать!” — потому что узнала шаблон, не вчитавшись. Сильная заметит, что в вашей формулировке никакого парадокса нет.

Сюда же — задачи с лишними или противоречивыми данными. Цель одна: проверить, думает ли модель над конкретной формулировкой или вспоминает похожий шаблон. Это, пожалуй, лучший бытовой индикатор реального рассуждения.

Скрытый текст

В таких тестах важно не перехитрить самих себя. Одно время по Интернету активно гулял тест рецепта для “свиных крылышек”. Этим тестом многие “крутые ИИшники” показывали как галлюцинирует нейросеть. Потому что не существует рецептов свиных крылышек, нет у свиньи крыльев.

А потом возьми и выяснись, что очень даже существуют. Может это и не частый термин, но все же им обозначают ребра или часть голени свиньи. Получается, что нейронка качественно отрабатывала запрос. А несведущие в кулинарии тестировщики хихикали.

Проверка на галлюцинации

Спросите модель о чем-то несуществующем — выдуманной книге, вымышленном законе, придуманном вами ученом. Хорошая модель честно скажет “не знаю” или “не нахожу такого”. Слабая (а иногда и не очень слабая) с удовольствием сочинит правдоподобную биографию и список трудов. Это тест на калибровку — понимает ли модель границы своего знания. Один из самых важных в быту: модель, которая уверенно врет, опаснее модели, которая честно сомневается. Как вы помните, только GPT 5.5 предупредил нас о том, что ученого Андрея Слонова мы придумали.

Культурный код

Культурный код важен для формирования стиля и понимания контекста. Ради интереса можете поспрашивать модель что-то такое, что зашито в наши головы с самого детства, но о чем понятия не имеют люди в соседней стране. В тесте я взял историю с дверным глазком, но тут можно хорошенько подумать и нагенерить много разных идей.

Тест “клубника”: считаем буквы

Самый знаменитый народный тест. Спросите: “Сколько букв "r" в слове strawberry?” (правильный ответ — три). Долгое время даже мощные модели уверенно отвечали “две” и становились героями мемов.

Тут важно понять, почему так происходит, потому что тест глубже, чем кажется. Дело в том, что модель мыслит токенами, и слово strawberry распадается, например, на “st”, “raw”, “berry”. Модель оперирует этими кусками-числами, а не отдельными символами. Спросить ее, сколько “r” в слове, — это примерно как спросить человека, сколько пикселей в букве, которую он читает: информация просто не на том уровне, на котором он работает.

Это делает тест слегка нечестным — он бьет в фундаментальную особенность архитектуры, а не в качество модели. Но как лакмусовая бумажка он по-прежнему полезен: сильные современные модели научились такие вопросы обходить (часто внутренне “проговаривая” слово по буквам или дописывая в уме), а слабые продолжают спотыкаться. Кстати, надежный способ получить верный ответ от любой модели — попросить ее не считать в уме, а написать код, который посчитает. Тогда она разложит слово по символам и не ошибется.

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Вариации той же идеи: попросить написать слово задом наперед, посчитать слова в собственном ответе, найти слова на заданную букву. Все это бьет в одну и ту же символьную слепоту.

Тест на работу с кодом и проектами

Один из главных и самых сложновоспроизводимых тестов. Тут как нигде важна задача. Если вы хотите, чтобы модель писала за вас код, находила ошибки в проекте или вообще вела сразу несколько параллельных задач по разработке и отладке, то тест придется придумывать самому. Чужой опыт тут будет хоть и показателен, но не даст стопроцентной гарантии, что модель справится именно с вашей задачей. Увы. Хотя, именно по вашему направлению можете поискать специализированные бенчмарки - они точно есть и вот здесь будут уместны.

Совет. Не полагайтесь на один тест — соберите свой личный мини-набор из 10–20 вопросов разных типов (логика, счет, творчество с ограничением, проверка на вранье, ваша профессиональная задача) и прогоняйте через него каждую новую модель. Через пару итераций у вас выработается чутье лучше любого агрегатора бенчмарок, потому что вы будете мерить именно то, что нужно вам. По сути, вы построите собственный маленький бенчмарк.

Каждому свое

Еще один важный вопрос, который нельзя не поднять при тестировании. Что подавать на вход моделей? У каждой компании есть свои рекомендации, как лучше формировать промпты для их продуктов. Если мы будем тестить сложной и той же формулировкой, то неизбежно столкнемся с проблемой: для кого-то формулировка попадет в точку и модель отработает хорошо, а для кого-то окажется мимо и модель отработает на редкость ужасно.

Тут мы упираемся в принцип, который кажется банальным, но нарушается на каждом шагу: сравнивать модели можно только в одинаковых условиях. Любая мелочь во вводных способна перевернуть результат. Один и тот же бенчмарк в разных условиях дает разные цифры. Температура генерации, системный промпт, число попыток, формулировка задачи, подключенные инструменты — все это рычаги, которыми можно невзначай (или нарочно) подкрутить итог.

Потому, если мы тестируем модели, то необходимо ставить их в равные условия и подавать на вход одинаковые входные данные.

Есть еще вариант для сравнения двух моделей. Можно составить две группы промптов (наиболее оптимально для каждой) и прогонять тест как минимум два раза. Подавая на вход каждой оптимальный и неоптимальный и усредняя результат.

Тест же с разными вводными для каждой в отдельности – не тест, а ерунда.

Как действуем дальше

После этих вводных я продолжу тестирование различных моделей. Мы уже проверили поведение флагманов. Теперь перейдем к тем, кто поскромнее. Ниже таблица с очень условным разбиением моделей на категории “флагман”, “сильные”, “средние” и так далее. GPT 5.5 Pro, Fable и Mythos, я отношу к суперфлагманам. И раз две из трех нам недоступны, то тестировать прошку одну мы пока не будем (может, отложим на попозже).

Осознаю, как неоднозначен этот список и что всегда найдутся аргументы его оспорить и перенести ту или иную модель в другой класс. Но, как я уже говорил, мир LLM вообще нелинеен, а отталкиваться от чего-то надо. Логику разбиения я постарался отразить в той же таблице в полях с бенчмарками. Отдельно приложу таблицу с комментариями, т.к. на Хабре тяжело разместить таблицу с большим числом столбцов.

Важно! Числа SWE-bench Verified приведены как ориентир: на середину 2026 это в основном самоотчеты вендоров, метрика частично загрязнена утечкой данных, и OpenAI уже сместилась на SWE-bench Pro, где те же модели проседают почти вдвое. Воспринимать как порядок величины, а не точную линейку.

Единого точного значения по колонке Arena Elo на самом деле не существует. Во-первых, рейтинг динамический и пересчитывается ежедневно. Во-вторых, на одну и ту же дату рейтинг может быть в формате без поправок или с поправкой на стиль (Style Control), общий зачет или сложные промпты. А после смены методологии в начале 2026 года абсолютные числа вообще сдвинулись на десятки пунктов. Поэтому я привожу не точные значения, а порядок: модели сгруппированы по уровням, и важна не цифра, а то, в какой группе модель и кто рядом с ней. Если вам нужно точное число — берите его из первоисточника (arena.ai/leaderboard) на конкретную дату и с конкретным срезом.

Модель

Доступ

Контекст

На BotHub

GPQA-D, %

SWE-bench Verified, %

HLE, %

Arena Elo (ориентир)

Уровень

Claude Fable 5

Закрытая (приост.)

н/д

94,6

95

≈64,5

1510

1·Запредельный

Claude Mythos 5

Закрытая (приост.)

н/д

94,6

≈64,7

1·Запредельный

gpt-5.5-pro

Закрытая

1M

≈94

≈1500

1·Запредельный

claude-opus-4.8

Закрытая

1M

93,6

88,6

45,7

≈1500

2·Флагман

gpt-5.5

Закрытая

1M

≈94

82,6

≈1495

2·Флагман

gemini-3.1-pro-preview

Закрытая

1M

94,3

80,6

44,7

≈1490

2·Флагман

claude-opus-4.7

Закрытая

200K

94,2

82

≈1490

3·Сильная

deepseek-v4-pro

Открытая

1M

≈90

80,6

≈1455

3·Сильная

MiniMax M3

Открытая

≈1M

92,7 *

80,5 *

3·Сильная

Qwen 3.7 Max

Открытая*

≈256K

92,4 *

80,4 *

≈1450

3·Сильная

Kimi K2.6

Открытая

≈256K

в топ-10

80,2 *

3·Сильная

claude-sonnet-4.6

Закрытая

200K (1M β)

89,9

79,6

≈1430

3·Сильная

grok-4.3

Закрытая

1M

≈90

N/P

≈50 ‡

≈1450

3·Сильная

claude-opus-4.6

Закрытая

200K

91,3

80,8

3·Сильная

gemini-3.5-flash

Закрытая

1M

92,2

78,8

4·Крепкая

claude-opus-4.5

Закрытая

200K

87,0

80,9

4·Крепкая

gpt-5.4

Закрытая

1M

90

41,6

4·Крепкая

gpt-5.4-pro

Закрытая

1M

58,7

4·Крепкая

o3

Закрытая

128K

83,3

20,3

4·Крепкая

grok-4.20

Закрытая

≈2M

≈87

≈1493

4·Крепкая

grok-4.20-multi-agent

Закрытая

≈2M

4·Крепкая

claude-opus-4.6-fast

Закрытая

200K

4·Крепкая

qwen3.6-plus

Открытая

≈256K

4·Крепкая

GLM-5.x

Открытая

≈128K (5.2: 1M)

91,2 (5.2)

77,8 (5)

4·Крепкая

gpt-4.1

Закрытая

1M

66

≈1400

5·Средняя

gemini-2.5-pro

Закрытая

1M

86,4

≈1400

5·Средняя

claude-haiku-4.5

Закрытая

200K

55,2

≈1350

5·Средняя

deepseek-v3.2

Открытая

≈128K

85

5·Средняя

Llama 4 Maverick / Scout

Открытая

1M (Scout 10M)

≈80

≈1300

5·Средняя

Mistral Large 3 / Medium 3.5

Откр./Закр.

≈128K

≈1300

5·Средняя

claude-sonnet-4.5

Закрытая

200K (1M β)

83,4

5·Средняя

gemini-3-flash-preview

Закрытая

1M

5·Средняя

sonar-pro

Закрытая

200K

н/c

н/c

5·Средняя

sonar-reasoning-pro

Закрытая

≈128K

н/c

н/c

5·Средняя

sonar-deep-research

Закрытая

≈128K

н/c

н/c

5·Средняя

GigaChat-2-Max

Закрытая

≈128K

5·Средняя

YandexGPT Pro 5.1

Закрытая

≈32K

5·Средняя

YandexGPT Pro

Закрытая

≈32K

5·Средняя

Gemma 3 / 4

Открытая

≈128K

≈1200

6·Базовая

Phi-4

Открытая

≈16K

56

≈1200

6·Базовая

gpt-4o-mini

Закрытая

128K

40

≈1100

7·Минимальная

deepseek-v4-flash

Открытая

≈128K

≈88

73,4

≈1100

7·Минимальная

gpt-5.4-nano

Закрытая

≈128K

7·Минимальная

Qwen3.5 0.8B

Открытая

≈32K

7·Минимальная

Llama 3.1 8B

Открытая

≈128K

7·Минимальная

Gemma 3n / Nova Micro

Откр./Закр.

≈32–128K

7·Минимальная

Модель

Примечание

Claude Fable 5

Та же база, что у Mythos 5, но в с ограничениями: навесили защиты по био/кибер/ИИ-разработке. По цифрам сильнейший из всех, только более не в публичном доступе

Claude Mythos 5

Закрытая модель, проверить что либо по ней невозможно

gpt-5.5-pro

Прокачанный режим 5.5: думает дольше и глубже. Отдельных чисел почти нет — OpenAI многое не раскрывает

claude-opus-4.8

Главная рабочая лошадь верхнего эшелона. По коду первый среди тех, кого реально можно запустить, и держит миллион токенов.

gpt-5.5

Универсал без явных слабых мест. OpenAI заявляет SWE 88,7% — независимо ближе к 82,6. Кому верить — вопрос доверия к замеру.

gemini-3.1-pro-preview

Лидер науки и картинок: лучший GPQA и мультимодальность, миллион контекста, минимальная (среди флагманов) цена

claude-opus-4.7

По цифрам все еще флагман, просто поколением старше.

deepseek-v4-pro

Гордость открытых моделей: 1,6 трлн параметров, активны 49 млрд. На тяжелых задачах закрытым уступает, но так он и не флагман

MiniMax M3

Свежая открытая модель: миллион контекста, картинки-видео из коробки, копеечная цена. Цифры пока в основном вендорские.

Qwen 3.7 Max

Самый дешевый в топ-10 по GPQA. Открытость со звездочкой — лицензия с оговорками.

Kimi K2.6

Китайский бюджетный, но крепкий: входит в десятку по GPQA за смешные деньги.

claude-sonnet-4.6

Та самая рабочая лошадка на каждый день. Не самые высокие метрики, но пишет и слушается инструкций лучше многих.

grok-4.3

Хорошо выигрывает за счет цены. SWE Verified xAI принципиально не показывает (сам по себе намек). HLE — уровень линейки Grok-4.

claude-opus-4.6

Предыдущий Opus. Глубокая логика и автономные агенты — для своего поколения был хорош.

gemini-3.5-flash

Быстрый и дешевый. По коду внезапно обходит старший Pro — вот она, магия дистилляции.

claude-opus-4.5

Еще на поколение назад. Инженерку и сложный код тянул уверенно.

gpt-5.4

Прошлый флагман GPT: рассуждения, инструменты, до миллиона контекста.

gpt-5.4-pro

Топовый режим 5.4 — на HLE до сих пор держится бодро. Прошлое поколение, но не списанное.

o3

Ветеран рассуждений (апрель 2025). Логика и переваривание больших объемов — да, по нынешним меркам уже скромно.

grok-4.20

Старший Grok с гигантским 2М контекстом. Глубокая логика, код, мультимодальность.

grok-4.20-multi-agent

Тот же Grok, но разруливает несколько агентов разом — под исследовательские задачи.

claude-opus-4.6-fast

Тот же Opus 4.6, только отвечает быстрее. Размен глубины на скорость.

qwen3.6-plus

Прошлый флагман Qwen. Логика, код, длинный контекст — добротный середнячок-плюс.

GLM-5.x

Открытая темная лошадка: GLM-5.1 первой из открытых обошла всех на SWE-bench Pro (58,4%). По обычному Verified ≈77,8%.

gpt-4.1

Народная рабочая лошадка прошлого года. Миллион контекста, надежна для кода — до сих пор живее всех живых.

gemini-2.5-pro

Прошлое поколение Pro. Миллион контекста, для многих задач до сих пор за глаза.

claude-haiku-4.5

Малыш по задумке: чаты, мелкие правки кода, парсинг. Берет скоростью и ценой.

deepseek-v3.2

Прошлый DeepSeek. Универсальные диалоги и код — крепко, но новое поколение уже обогнало.

Llama 4 Maverick / Scout

Берет не умом, а скоростью и контекстом — у Scout аж 10М токенов (правда, эффективно рабоатет только с половиной).

Mistral Large 3 / Medium 3.5

Главная надежда Европы. До лидеров США и Китая не дотягивает, но знамя ЕС держит.

claude-sonnet-4.5

Прошлый Sonnet. Диалоги, кодинг, логика — добротно для своего поколения.

gemini-3-flash-preview

Превью Flash прошлого поколения: скорость плюс мультимодальность.

sonar-pro

Поиск: веб + RAG. Сравнивать с обычными LLM по науке/коду бессмысленно — тут другие задачи.

sonar-reasoning-pro

Рассуждающая поисковая: RAG со встроенной логикой. Прямого сравнения по бенчмаркам не ждите.

sonar-deep-research

Для глубоких веб-раскопок. Меряется не GPQA, а тем, насколько хорошо собрала отчет из сети.

GigaChat-2-Max

Топ от Сбера под русский. Глобальных бенчмарков нет — меряют в основном на своих, русскоязычных.

YandexGPT Pro 5.1

Русскоязычная от Яндекса (RC). Контекст скромный, глобальных цифр нет, но русский и документы знает.

YandexGPT Pro

Та же история попроще: диалоги и документы на русском.

Gemma 3 / 4

Открытая малая модель от Google. Для своего размера вполне хороша.

Phi-4

Узкий профиль, но для своего размера - очень достойно.

gpt-4o-mini

Бюджетный мультимодальный ветеран. Для дешевых массовых задач до сих пор в строю.

deepseek-v4-flash

Дешевый и быстрый: рассуждения с минимальной задержкой.

gpt-5.4-nano

Наномодель: массовые запросы, автодополнение. Ум не нужен — нужны скорость и копеечная цена.

Qwen3.5 0.8B

Сверхмалая модель: классификация, извлечение полей. Запускается почти на чайнике.

Llama 3.1 8B

Прошлое поколение, маленькая. Локальная, тянется одной видеокартой.

Gemma 3n / Nova Micro

Edge и мобилки: простейшие задачи прямо на устройстве.

Важные пояснения:

  • * Вендорские числа: самоотчет производителя, независимо еще не перепроверены (особенно свежие открытые релизы — MiniMax M3, Qwen).

  • HLE сильно зависит от режима (с инструментами / max effort): разброс у одной модели достигает 1,5–2 раз. Лидеры июня-2026: Mythos/Fable ≈64–65%, Opus 4.8 ≈46%, Gemini 3.1 Pro ≈45%.

  • н/c — несравнимо: поисковые/RAG-модели (Sonar) по GPQA/SWE напрямую не меряются.

  • N/P — не публикуется вендором.

  • SWE-bench Verified для GPT-5.5 разнятся. Независимые прогоны ≈82,6%, самоотчёт OpenAI 88,7%. OpenAI подозревает (!!!), что бечмарки могли случайно утечь и попасть в модель. Потому надо читать как порядок.

Источник

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  • 07.07.26 18:00 robertalfred175

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  • 07.07.26 18:01 robertalfred175

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  • 16.07.26 21:38 patricialovick86

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  • 30.07.26 00:04 Ahmed

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  • 04.08.26 11:05 Kisnoles

    Excellent analysis, thank you. I was particularly struck by how rigidly the skill sets the order: data processing and form come first, with color coming last. This really cures the habit of "first a pretty palette, then we'll figure it out." A palette validator using OKLCH + color blindness + WCAG is something most chart generators lack. And regarding the boundaries of competence, it's very clear. Where there's a self-checking loop (color), you delegate freely. Where there's a heuristic (form, data interpretation), you remain the final filter. This is a universal principle, not just for /dataviz. By the way, when you look at trading dashboards (including those of decent platforms like ExpertOption), it's immediately clear who thought about readability and who just threw in rainbow lines. Tools like this skill could greatly improve the quality of analytics. Thanks again for the detailed analysis – saved.

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  • 23.08.26 20:02 leslieyee

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  • 30.08.26 15:37 [email protected]

    Discovering I had been defrauded, I searched online for services claiming to recover stolen ETH. I contacted several companies, but none succeeded. Although some claimed they could trace assets or recover funds, I could not verify their success or recover my money. I later found SYLVESTER BRYANT Recovery through Google. After contacting him, he recovered 450,000,00. He can be reached at [email protected] or WhatsApp at +1 512 577 7957.

  • 01.09.26 11:16 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 01.09.26 11:16 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 01.09.26 17:34 Garry42

    Really crazy world. These fraudsters go at any length to steal your hard earned funds. I have been a victim of a bitcoin scam about 7 months back. A Con artist gained access to my cashapp account through a phishing scam. They stole $409,000. I was really devastated. I did everything to get back my funds by contacting the FBI but they claimed there was nothing they could do. A friend told me about a recovery expert. He helps fight against various phishing and investment scams and they were able to help trace and recover my funds even though it took over 2 days. you can reach out to him through recoverydarek@gmail. com . I can guarantee his services are still active.

  • 01.09.26 17:34 Garry42

    Really crazy world. These fraudsters go at any length to steal your hard earned funds. I have been a victim of a bitcoin scam about 7 months back. A Con artist gained access to my cashapp account through a phishing scam. They stole $409,000. I was really devastated. I did everything to get back my funds by contacting the FBI but they claimed there was nothing they could do. A friend told me about a recovery expert. He helps fight against various phishing and investment scams and they were able to help trace and recover my funds even though it took over 2 days. you can reach out to him through recoverydarek@gmail. com . I can guarantee his services are still active.

  • 02.09.26 03:43 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 02.09.26 03:43 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 04.09.26 21:51 Kovengray

    Recovering your lost investment funds as the case might be, is not what you can do alone, you’d require the service of a trained recovery specialist. A recovery specialist is a person or a group of people who are well equipped to work around the brokerage network. They have vast knowledge about the whole network and have the right software and private keys to follow any transaction. I was ripped off trading online to an investment broker, good thing I got every penny back through the help of Gavin ray he’s a genius Contact : Gavinray78 at gmail com or WhatsApp +1 352 322 2096 It is also important to be patient and really calm during the process.

  • 05.09.26 21:38 [email protected]

    I invested 45,000  Euro, and later aggreviated to 198,000 Euro.  I requested  to place ‎Withdrawal of my funds to be paid to my Bank account. But nothing happened I was subjected to pay more fee until I can get my funds on my account, i got in touch with Theodore ryan here on this platform who had helped a lot of people, I followed all instructions and he legally got back my withheld funds, I got threatened by the company that if I don’t pay they will get my account frozen, all thanks to Theodore ryan and I highly recommend him to anyone dealing with an unregulated broker company… contact him on his Gmail - theodoreryan318@  gmail .  com

  • 09.09.26 21:31 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 09.09.26 21:31 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 09.09.26 23:22 Fraddy Pual

    Hearing that these individuals are focusing on other people makes me very sad. I had a similar situation with them and lost a lot of money, but after learning about (Cruxcipherteam @ proton DoT me), whataqq:+168160-15021, telegram: @Cruxcipherteam I was able to get my money back. It's critical that we all be watchful and keep reporting these occurrences.

  • 11.09.26 03:23 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 11.09.26 03:23 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 15.09.26 03:35 elioduncan

    I fell victim to a freelance web development contract scam that ultimately cost me CAD 4,000. It all started when I came across a job posting on Indeed Canada, a popular online job portal. The position seemed perfect: a freelance web developer role for an established company looking for someone to design and build a fully functional e-commerce website. The job promised a high payment of CAD 20,000 upon successful completion, which sounded like a great opportunity for me to gain experience and earn decent pay.The employer, who introduced himself as a project manager from a "well-known" tech company, was very persuasive and professional in our initial communication. He explained that the project involved developing a user-friendly, responsive online store for their client and that they had a strict timeline to meet. He assured me that the payment would be made promptly after completing the tasks. However, before starting the work, he told me that I would need to pay an upfront fee of CAD 4,000 to cover certain software tools and licensing fees required for the project. This was supposedly a part of their company policy for freelance contractors, ensuring access to their premium resources. The idea of working on a professional project, coupled with the promise of a substantial payout, convinced me to pay the upfront fee.As soon as I made the payment, the project manager became increasingly difficult to reach. He initially responded to my emails and provided some vague instructions on what the project would entail, but as time went on, communication slowed to a complete halt. I never received the required tools or any proper project details. My emails went unanswered, and any attempts to contact the company were met with silence. After waiting for weeks, I realized that I had been scammed.Desperate to recover my money, I turned to TechY Force Cyber Retrieval, a service that specializes in helping victims of online scams. They helped me track the payment and took legal steps to pursue the fraudsters. Through their guidance, I was able to recover the full CAD 4,000. TechY Force Cyber Retrieval worked with my bank to reverse the transaction and liaised with the authorities to trace the scammer's details.This was a hard lesson, and I now know to be highly cautious about online job offers that require upfront payments. It is crucial to research companies thoroughly and avoid any job that seems too good to be true, especially when it involves paying money upfront. WhatsApp https://wa.link/2x6ktp Mail. [email protected]

  • 15.09.26 15:45 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 15.09.26 15:45 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

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