AIREITER
API 문서가격
템플릿
OpenAIText Chat

GPT-5.6 Sol AI 채팅 플레이그라운드 및 API

GPT-5.6 Sol 제품군에서 가장 까다로운 코딩, 추론, 에이전트 워크플로를 위해 GPT-5.6 Sol을 온라인으로 사용해 보세요. 토큰 가격을 비교하고 API를 통합하세요.

입력공식 $4.00 100만 토큰당AIReiter $1.20 100만 토큰당출력공식 $20.00 100만 토큰당AIReiter $6.00 100만 토큰당캐시 읽기공식 $0.40 100만 토큰당AIReiter $0.12 100만 토큰당캐시 생성공식 $5.00 100만 토큰당AIReiter $1.50 100만 토큰당
API로 실행
플레이그라운드READMEAPI

입력

imagefile[]
Optional input images sent alongside the prompt. Up to 5 files. Images are billed as input tokens.
1
2
3
4
5
6
7
8
9
10
11

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.

출력

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
입력 Token
134
출력 Token
2354
Tokens per second
55.13 tokens / second
Time to first token
-

모델 세부정보

플레이그라운드, API 요청, 내부 워크플로에서 동일한 모델 키를 사용하세요.

모델 ID
gpt-5.6-sol
공급자
OpenAI
프로토콜
OpenAI Chat Completions
컨텍스트 창
1,050,000 토큰
최대 출력
128,000 토큰
입력 Token
120 credits / 100만 토큰
출력 Token
600 credits / 100만 토큰
캐시 읽기
12 credits / 100만 토큰
캐시 쓰기
150 credits / 100만 토큰

GPT-5.6 Sol로 할 수 있는 일

GPT-5.6 제품군에서 가장 복잡한 작업, 특히 어려운 코드, 추론, 에이전트 작업에는 GPT-5.6 Sol을 선택하세요.

플래그십 추론

여러 상호작용하는 제약이 있는 어려운 문제에는 가장 강력한 GPT-5.6 티어를 사용하세요.

복잡한 소프트웨어 작업

단순한 패턴 매칭만으로는 부족한 시스템을 설계, 디버그, 검토하세요.

고급 에이전트

더 긴 워크플로를 계획하고, 도구 결과를 해석하며, 중간 단계가 실패했을 때 복구하세요.

심층 기술 분석

명시적인 트레이드오프를 바탕으로 아키텍처, 위험, 구현 경로를 비교하세요.

GPT-5.6 Sol 사용 사례

라우팅된 GPT-5.6 스택에서 가장 어려운 요청에 가장 적합합니다. 작업에 플래그십 수준의 깊이가 필요하지 않다면 Terra 또는 Luna를 사용하세요.
01

어려운 코딩 작업

아키텍처, 멀티파일 디버깅, 복잡한 구현에 사용하세요.

02

고급 에이전트 작업

계획이 여러 번의 도구 호출과 수정에도 견뎌야 할 때 사용하세요.

03

심층 분석

상충하는 증거와 중요한 트레이드오프가 있는 결정을 위해 사용하세요.

04

에스컬레이션 티어

가벼운 모델로는 안정적으로 완료할 수 없는 어려운 요청은 여기로 라우팅하세요.

GPT-5.6 Sol 사용 방법

세 가지 간단한 단계로 모델을 테스트해 보세요.

01

설정 선택

모델이 지원하는 응답 제어 및 업로드 옵션을 설정하세요.

02

프롬프트 보내기

작업을 설명하고, 관련 맥락을 추가한 뒤, 스트리밍 응답과 토큰 사용량을 검토하세요.

03

API 연결

문서화된 엔드포인트와 API 키를 사용해 동일한 모델을 제품에 가져오세요.

GPT-5.6 Sol API로 빌드하기

예측 가능한 제어와 사용량 보고를 통해 인터랙티브 테스트에서 프로덕션 통합까지 진행하세요.

익숙한 프로토콜

사용 가능한 경우 스트리밍을 포함하여 이 모델에 구성된 API 프로토콜을 사용하세요.

사용량 가시성

각 응답 후 입력 토큰, 출력 토큰, 소모된 크레딧을 추적하세요.

모델별 제어

일반적인 기본값에 의존하지 말고 지원되는 생성 파라미터를 전달하세요.

하나의 계정과 잔액

같은 AIReiter 계정과 청구 시스템으로 지원되는 텍스트 모델을 테스트하고 운영하세요.

GPT-5.6 Sol FAQ

온라인 플레이그라운드, 요금, API 액세스에 대한 일반적인 질문입니다.

/ 01

언제 GPT-5.6 Sol을 선택해야 하나요?

GPT-5.6 제품군에서 가장 어려운 코딩, 추론, 에이전트 작업에는 Sol을 선택하세요.

/ 02

Sol은 Terra와 Luna와 어떻게 다른가요?

Sol은 플래그십 티어입니다. Terra는 균형 잡힌 프로덕션 티어이며, Luna는 속도와 비용을 우선시합니다.

/ 03

모든 GPT-5.6 트래픽을 Sol로 사용해야 하나요?

아니요. 라우팅과 평가를 사용해 일반 요청은 Terra 또는 Luna에 유지하세요.

/ 04

GPT-5.6 Sol의 가격은 어떻게 책정되나요?

현재 입력 및 출력 토큰 요금은 플레이그라운드 위에 표시됩니다.

/ 05

API를 통해 GPT-5.6 Sol을 호출할 수 있나요?

네. 연결된 API 문서를 따라 모델 ID gpt-5.6-sol을 사용하세요.

AIREITER

문의가 있으신가요? 연락처
[email protected]

新速率有限公司NEWRATE LIMITED香港九龍花園街 2-16 號好景商業中心 2304 室Room 2304, Haojing Commercial Center, 2-16 Garden Street, Kowloon, Hong Kong

LLM

GPT-6 AstraGemini 3.8 FlashClaude Fable 5.1GLM-5.3 FlashGemini 3.6 Flash

AI 비디오

Gemini Omni 1.1 Flash ExtMiniMax H3Kling 3.0 Motion ControlKling 3.0 TurboKling 3.0

AI 이미지

GPT-Image 2.5Grok Imagine Image 2.0Midjourney V8.1Midjourney V7Z-Image Turbo

블로그

모두 보기 →

회사

개인정보 처리방침서비스 약관환불 정책

© 2026 AIReiter. All rights reserved.