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GPT-5.6 Sol AI Chat Playground и API

Попробуйте GPT-5.6 Sol онлайн для самых требовательных задач программирования, рассуждений и агентных рабочих процессов в семействе GPT-5.6. Сравните цены за токены и интегрируйте API.

ВводОфициально $4.00 za 1 mln tokenovAIReiter $1.20 za 1 mln tokenovВыводОфициально $20.00 za 1 mln tokenovAIReiter $6.00 za 1 mln tokenovЧтение кэшаОфициально $0.40 za 1 mln tokenovAIReiter $0.12 za 1 mln tokenovСоздание кэшаОфициально $5.00 za 1 mln tokenovAIReiter $1.50 za 1 mln tokenov
Запустить через API
ПесочницаREADMEAPI

ВВОД

imagefile[]
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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-sol:

const response = await client.chat.completions.create({
    "model": "gpt-5.6-sol",
    "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-sol",
    "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-sol:

response = client.chat.completions.create(
      model = "gpt-5.6-sol",
      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-sol",
      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-sol 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-sol",
  "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-sol",
  "input": {
    "model": "gpt-5.6-sol",
    "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-sol
Провайдер
OpenAI
Протокол
OpenAI Chat Completions
Окно контекста
1,050,000 токенов
Максимальный вывод
128,000 токенов
Входные Token
120 credits / 1 млн токенов
Выходные Token
600 credits / 1 млн токенов
Чтение кэша
12 credits / 1 млн токенов
Запись кэша
150 credits / 1 млн токенов

Что можно делать с GPT-5.6 Sol

Выбирайте GPT-5.6 Sol для самых сложных задач в семействе GPT-5.6, особенно для трудного кода, рассуждений и агентных задач.

Флагманское рассуждение

Используйте самый мощный уровень GPT-5.6 для сложных задач с несколькими взаимодействующими ограничениями.

Сложная работа с ПО

Проектируйте, отлаживайте и проверяйте системы, где поверхностного распознавания шаблонов недостаточно.

Продвинутые агенты

Планируйте более длинные рабочие процессы, интерпретируйте результаты инструментов и восстанавливайтесь, когда промежуточный шаг завершается сбоем.

Глубокий технический анализ

Сравнивайте архитектуры, риски и пути реализации с явными компромиссами.

Сценарии использования GPT-5.6 Sol

Лучше всего подходит для самых сложных запросов в маршрутизируемом стеке GPT-5.6; используйте Terra или Luna, когда задаче не нужна глубина флагманского уровня.
01

Сложные задачи программирования

Используйте для архитектуры, отладки в нескольких файлах и сложной реализации.

02

Продвинутые агентные задачи

Используйте, когда планы должны выдерживать несколько вызовов инструментов и правок.

03

Глубокий анализ

Используйте для решений при противоречивых данных и важных компромиссах.

04

Уровень эскалации

Перенаправляйте сюда сложные запросы после того, как более лёгкая модель не смогла надёжно завершить задачу.

Как использовать GPT-5.6 Sol

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 API GPT-5.6 Sol

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 Sol

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

/ 01

Когда мне следует выбрать GPT-5.6 Sol?

Выбирайте Sol для самых сложных задач программирования, рассуждений и агентных задач в семействе GPT-5.6.

/ 02

Чем Sol отличается от Terra и Luna?

Sol — это флагманский уровень; Terra — сбалансированный уровень для продакшена, а Luna делает приоритетом скорость и стоимость.

/ 03

Должен ли весь трафик GPT-5.6 использовать Sol?

Нет. Используйте маршрутизацию и оценки, чтобы обычные запросы оставались на Terra или Luna.

/ 04

Как формируется цена на GPT-5.6 Sol?

Текущие тарифы за входные и выходные токены показаны над playground.

/ 05

Могу ли я вызывать GPT-5.6 Sol через API?

Да. Следуйте ссылке на документацию API и используйте ID модели gpt-5.6-sol.

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