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GPT-5.6 Terra AI Chat Playground и API

Попробуйте GPT-5.6 Terra онлайн для сбалансированного продакшн-кодинга, анализа и автоматизации. Тестируйте промпты, отслеживайте использование токенов и подключайтесь через API.

ВводОфициально $2.00 za 1 mln tokenovAIReiter $0.60 za 1 mln tokenovВыводОфициально $12.00 za 1 mln tokenovAIReiter $3.60 za 1 mln tokenovЧтение кэшаОфициально $0.20 za 1 mln tokenovAIReiter $0.06 za 1 mln tokenovСоздание кэшаОфициально $2.50 za 1 mln tokenovAIReiter $0.75 za 1 mln tokenov
Запустить через API
ПесочницаREADMEAPI

ВВОД

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Install the official OpenAI client — AIReiter speaks the same protocol, so only the base URL changes:

npm install openai

Set the AIREITER_API_KEY environment variable:

export AIREITER_API_KEY=<paste-your-key-here>

Point the client at AIReiter:

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.AIREITER_API_KEY,
  baseURL: "https://aireiter.com/api/v1",
});

Run gpt-5.6-terra:

const response = await client.chat.completions.create({
    "model": "gpt-5.6-terra",
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "max_tokens": 4096,
    "reasoning_effort": "medium"
  });

console.log(response);

Stream the response instead:

const stream = await client.chat.completions.create({
  ...{
    "model": "gpt-5.6-terra",
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "max_tokens": 4096,
    "reasoning_effort": "medium"
  },
  stream: true,
});

for await (const event of stream) {
  console.log(event);
}

Install the official OpenAI client — AIReiter speaks the same protocol, so only the base URL changes:

pip install openai

Set the AIREITER_API_KEY environment variable:

export AIREITER_API_KEY=<paste-your-key-here>

Point the client at AIReiter:

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AIREITER_API_KEY"],
    base_url="https://aireiter.com/api/v1",
)

Run gpt-5.6-terra:

response = client.chat.completions.create(
      model = "gpt-5.6-terra",
      messages = [
        {
          role = "user",
          content = "Explain what an API rate limit is and how to handle a 429 response in code."
        }
      ],
      max_tokens = 4096,
      reasoning_effort = "medium"
)

print(response)

Stream the response instead:

stream = client.chat.completions.create(
      model = "gpt-5.6-terra",
      messages = [
        {
          role = "user",
          content = "Explain what an API rate limit is and how to handle a 429 response in code."
        }
      ],
      max_tokens = 4096,
      reasoning_effort = "medium",
    stream=True,
)

for event in stream:
    print(event)

Set the AIREITER_API_KEY environment variable:

export AIREITER_API_KEY=<paste-your-key-here>

Run gpt-5.6-terra against AIReiter's API:

curl -s -X POST \
  -H "Authorization: Bearer $AIREITER_API_KEY" \
  -H "Content-Type: application/json" \
  "https://aireiter.com/api/v1/chat/completions" \
  -d '{
  "model": "gpt-5.6-terra",
  "messages": [
    {
      "role": "user",
      "content": "Explain what an API rate limit is and how to handle a 429 response in code."
    }
  ],
  "max_tokens": 4096,
  "reasoning_effort": "medium"
}'

Add "stream": true to the body to receive the response as server-sent events.

ВЫВОД

Example

A codebase you don't know is a risk, not a hurdle. The plan should burn down that risk in order: understand, run, trace, spike, then make the change small enough that "production-ready" is a property you can demonstrate, not a hope.

Here are the first five steps, with the evidence you collect at each.

Step 1 — Get it building and the baseline green (half day) Read the README, the manifest(s), the CI config, and any architecture docs. Boot the app locally, run the full test suite, run the linter/typechecker. Evidence: The exact commands that reproduce a clean build and a passing test suite from a fresh checkout (recorded output, not memory of it). A one-page map you wrote yourself: where the entry points are, the layers, the build/test toolchain, and the main risks to your feature (e.g. "this touches cron jobs and payments").

Step 2 — Trace the smallest end-to-end slice that resembles your feature (half day) Don't start with your feature. Pick a neighboring one that already works and follow it from entry to persistence. Note the conventions along the way: how errors surface, how config is injected, how logging is done, how tests are written. Evidence: An annotated trace — file → function → what it does — for that slice. A written list of "conventions I must follow" (not intuitions; things you observed in real code). A named shortlist of the 3–5 files you will actually touch. If you can't produce this trace, you're not ready to write code.

Step 3 — Spike the core path (day 2) Build the ugly version: hardcoded values, no error handling, no tests. The purpose is to confirm the path you traced in step 2 is real and to surface what you didn't know you didn't know. Evidence: A working spike demonstrating the feature's central data path, alongside a list of every assumption the spike broke and what you corrected. That correction list is the most valuable document in this whole plan.

Step 4 — Write the contract before the code (half of day 2 / day 3) Once the spike proves the path, pin down what production needs: the inputs/outputs, the error cases, where it sits in the conventions from step 2. Then write the tests — they'll be red, but they're the specification. Evidence: A one-to-two-page design doc, an agreed interface/API shape (with the team if there is one), and a red test suite that encodes intended behavior. If you can't write the contract without consulting the code, you haven't finished step 2.

Step 5 — Implement in small, verified increments (days 3–4) Replace the spike with the real thing in small commits, each one keeping the suite green, leaning on the existing patterns. Run lint/tests/typecheck per commit — CI, not just locally — and exercise the actual path against a real instance (staging or a local environment that isn't stubbed). Evidence: A branch with progressive commits, each green in CI; coverage on the new code; something that proves it works against reality (a test result, a log trace, a screenshot); and a review by at least one person who knows the codebase. The review counts as evidence — an unfamiliar codebase has tribal knowledge you cannot extract from the files alone.

Steps 6+ would be the things that actually make it "shipped": a migration plan and its rollback, feature flagging, observability, the release and post-release verification. But the first five get you to a reviewed, green, working slice in staging — which is the point at which you can say "this will work in production" with evidence behind it, instead of a guess.

{
  "model": "gpt-5.6-terra",
  "input": {
    "model": "gpt-5.6-terra",
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "max_tokens": 4096,
    "reasoning_effort": "medium"
  },
  "output": "A codebase you don't know is a risk, not a hurdle. The plan should burn down that risk in order: understand, run, trace, spike, then make the change small enough that \"production-ready\" is a property you can demonstrate, not a hope.\n\nHere are the first five steps, with the evidence you collect at each.\n\n**Step 1 — Get it building and the baseline green (half day)**\nRead the README, the manifest(s), the CI config, and any architecture docs. Boot the app locally, run the full test suite, run the linter/typechecker.\n*Evidence:* The exact commands that reproduce a clean build and a passing test suite from a fresh checkout (recorded output, not memory of it). A one-page map you wrote yourself: where the entry points are, the layers, the build/test toolchain, and the main risks to your feature (e.g. \"this touches cron jobs and payments\").\n\n**Step 2 — Trace the smallest end-to-end slice that resembles your feature (half day)**\nDon't start with your feature. Pick a neighboring one that already works and follow it from entry to persistence. Note the conventions along the way: how errors surface, how config is injected, how logging is done, how tests are written.\n*Evidence:* An annotated trace — file → function → what it does — for that slice. A written list of \"conventions I must follow\" (not intuitions; things you observed in real code). A named shortlist of the 3–5 files you will actually touch. If you can't produce this trace, you're not ready to write code.\n\n**Step 3 — Spike the core path (day 2)**\nBuild the ugly version: hardcoded values, no error handling, no tests. The purpose is to confirm the path you traced in step 2 is real and to surface what you didn't know you didn't know.\n*Evidence:* A working spike demonstrating the feature's central data path, alongside a list of every assumption the spike broke and what you corrected. That correction list is the most valuable document in this whole plan.\n\n**Step 4 — Write the contract before the code (half of day 2 / day 3)**\nOnce the spike proves the path, pin down what production needs: the inputs/outputs, the error cases, where it sits in the conventions from step 2. Then write the tests — they'll be red, but they're the specification.\n*Evidence:* A one-to-two-page design doc, an agreed interface/API shape (with the team if there is one), and a red test suite that encodes intended behavior. If you can't write the contract without consulting the code, you haven't finished step 2.\n\n**Step 5 — Implement in small, verified increments (days 3–4)**\nReplace the spike with the real thing in small commits, each one keeping the suite green, leaning on the existing patterns. Run lint/tests/typecheck per commit — CI, not just locally — and exercise the actual path against a real instance (staging or a local environment that isn't stubbed).\n*Evidence:* A branch with progressive commits, each green in CI; coverage on the new code; something that proves it works against reality (a test result, a log trace, a screenshot); and a review by at least one person who knows the codebase. The review counts as evidence — an unfamiliar codebase has tribal knowledge you cannot extract from the files alone.\n\nSteps 6+ would be the things that actually make it \"shipped\": a migration plan and its rollback, feature flagging, observability, the release and post-release verification. But the first five get you to a reviewed, green, working slice in staging — which is the point at which you can say \"this will work in production\" with evidence behind it, instead of a guess.",
  "metrics": {
    "input_tokens": 134,
    "output_tokens": 2354,
    "generated_in_seconds": 42.7
  },
  "example": true
}
Generated in
42.7 seconds
Входные Token
134
Выходные Token
2354
Tokens per second
55.13 tokens / second
Time to first token
-

Сведения о модели

Используйте тот же ключ модели в Playground, запросах API и внутренних рабочих процессах.

ID модели
gpt-5.6-terra
Провайдер
OpenAI
Протокол
OpenAI Chat Completions
Окно контекста
1,050,000 токенов
Максимальный вывод
128,000 токенов
Входные Token
60 credits / 1 млн токенов
Выходные Token
360 credits / 1 млн токенов
Чтение кэша
6 credits / 1 млн токенов
Запись кэша
75 credits / 1 млн токенов

Что можно делать с GPT-5.6 Terra

Выбирайте GPT-5.6 Terra, когда вам нужен практичный баланс возможностей, отзывчивости и стоимости для продакшн-нагрузок.

Сбалансированный кодинг

Работайте над разработкой функций, отладкой, тестами и объяснением кода с качеством, ориентированным на продакшн.

Операционный анализ

Преобразуйте логи, отчеты и требования в структурированные выводы и следующие шаги.

Автоматизация рабочих процессов

Поддерживайте агентов и backend-процессы, которым требуется больше рассуждений, чем у облегченного тарифа.

Деловые тексты

Создавайте понятные спецификации, брифы и контент для клиентов при сбалансированной стоимости.

Сценарии использования GPT-5.6 Terra

Лучше всего подходит для продакшн-вариантов по умолчанию, где нужен лучший баланс возможностей и стоимости, чем при постоянном использовании флагманского тарифа.
01

Продакшн-кодинг

Используйте как практичный вариант по умолчанию для разработки функций и поддержки.

02

Пайплайны анализа

Извлекайте выводы и рекомендации из операционных материалов.

03

Бизнес-ассистенты

Поддерживайте повторяющиеся задачи по письму, планированию и работе со знаниями.

04

Маршрутизация среднего уровня

Обрабатывайте задачи, которые выходят за рамки Luna, не оплачивая Sol для каждого запроса.

Как использовать GPT-5.6 Terra

Протестируйте модель в три простых шага.

01

Выберите настройки

Настройте параметры ответа и варианты загрузки, поддерживаемые моделью.

02

Отправьте запрос

Опишите задачу, добавьте релевантный контекст и проверьте потоковый ответ и использование токенов.

03

Подключите API

Используйте документированный endpoint и ваш API key, чтобы внедрить ту же модель в ваш продукт.

Создавайте с помощью GPT-5.6 Terra API

Переходите от интерактивного теста к production-интеграции с предсказуемыми настройками и отчетностью по использованию.

Знакомые протоколы

Используйте протокол API, настроенный для этой модели, включая потоковую передачу, где она доступна.

Прозрачность использования

Отслеживайте входные токены, выходные токены и потраченные кредиты после каждого ответа.

Специфичные для модели настройки

Передавайте поддерживаемые параметры генерации вместо того, чтобы полагаться на общие значения по умолчанию.

Один аккаунт и баланс

Тестируйте и используйте поддерживаемые текстовые модели через тот же аккаунт AIReiter и систему биллинга.

FAQ по GPT-5.6 Terra

Частые вопросы об онлайн-playground, ценах и доступе через API.

/ 01

Когда следует выбрать GPT-5.6 Terra?

Выбирайте Terra как сбалансированный вариант по умолчанию для продакшн-кодинга, анализа, написания текстов и автоматизации.

/ 02

Чем Terra отличается от Sol и Luna?

Terra находится между флагманским Sol и экономичным Luna, поэтому подходит для маршрутизации по соотношению возможностей и стоимости.

/ 03

Можно ли использовать Terra как вариант по умолчанию для приложения?

Да, но проверьте его на ваших реальных рабочих нагрузках и повышайте уровень только для тех запросов, которым нужен Sol.

/ 04

Какова цена GPT-5.6 Terra?

Текущие тарифы за входные и выходные токены показаны над playground.

/ 05

Можно ли вызывать GPT-5.6 Terra через API?

Да. Следуйте по ссылке в документации API и используйте идентификатор модели gpt-5.6-terra.

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