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GPT-5.6 Sol AI 聊天 Playground 與 API

線上試用 GPT-5.6 Sol,體驗 GPT-5.6 家族中最要求嚴苛的程式開發、推理與 agent 工作流程。比較 token 定價並整合 API。

输入官方 $4.00 每 100 萬 TokensAIReiter $1.20 每 100 萬 Tokens输出官方 $20.00 每 100 萬 TokensAIReiter $6.00 每 100 萬 Tokens缓存读取官方 $0.40 每 100 萬 TokensAIReiter $0.12 每 100 萬 Tokens缓存创建官方 $5.00 每 100 萬 TokensAIReiter $1.50 每 100 萬 Tokens
使用 API 執行
PlaygroundREADMEAPI

輸入

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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.

輸出

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
輸入 Token
134
輸出 Token
2354
Tokens per second
55.13 tokens / second
Time to first token
-

模型詳情

在 Playground、API 請求與內部工作流程中使用相同的模型鍵。

模型 ID
gpt-5.6-sol
供應商
OpenAI
協定
OpenAI Chat Completions
上下文視窗
1,050,000 Token
最大輸出
128,000 Token
輸入 Token
120 credits / 1M Token
輸出 Token
600 credits / 1M Token
快取讀取
12 credits / 1M Token
快取寫入
150 credits / 1M Token

你能用 GPT-5.6 Sol 做什麼

在 GPT-5.6 家族中,選擇 GPT-5.6 Sol 來處理複雜度最高的工作,特別是困難的程式碼、推理與 agent 任務。

旗艦級推理

針對具有多重交互限制的困難問題,使用最強大的 GPT-5.6 層級。

複雜軟體工作

設計、除錯並審查僅靠表層模式比對遠遠不夠的系統。

進階 Agents

規劃更長的工作流程、解讀工具結果,並在中間步驟失敗時進行復原。

深度技術分析

以明確的取捨比較架構、風險與實作路徑。

GPT-5.6 Sol 使用情境

最適合路由式 GPT-5.6 架構中最困難的請求;當任務不需要旗艦級深度時,請使用 Terra 或 Luna。
01

困難的程式設計任務

適用於架構設計、多檔案除錯與複雜實作。

02

進階 Agent 任務

當規劃必須經得起多次工具呼叫與修訂時使用。

03

深度分析

適用於存在相互矛盾證據與重要取捨的決策。

04

升級處理層級

當較輕量的模型無法可靠完成時,將困難請求導向此處。

如何使用 GPT-5.6 Sol

透過三個簡單步驟測試這個模型。

01

選擇設定

設定模型支援的回應控制與上傳選項。

02

送出提示

描述任務、加入相關脈絡,並查看串流回應與 token 使用量。

03

連接 API

使用文件中的端點與你的 API key,將同一模型整合到你的產品中。

使用 GPT-5.6 Sol API 開發

從互動式測試一路進階到正式整合,並享有可預測的控制與用量報告。

熟悉的協議

使用此模型設定的 API 協議,包括可用時的串流。

用量可視化

在每次回應後追蹤輸入 token、輸出 token 和已消耗的點數。

模型專屬控制

傳入支援的生成參數,而不是依賴通用預設值。

一個帳戶與餘額

透過相同的 AIReiter 帳戶與計費系統測試並操作支援的文字模型。

GPT-5.6 Sol 常見問題

關於線上 playground、價格與 API 存取的常見問題。

/ 01

我應該何時選擇 GPT-5.6 Sol?

在 GPT-5.6 家族中,選擇 Sol 來處理最困難的程式設計、推理與 agent 任務。

/ 02

Sol 與 Terra、Luna 有何不同?

Sol 是旗艦級層級;Terra 是均衡的生產層級,而 Luna 則優先考量速度與成本。

/ 03

所有 GPT-5.6 流量都應該使用 Sol 嗎?

不需要。請使用路由與評估機制,讓例行請求維持在 Terra 或 Luna。

/ 04

GPT-5.6 Sol 的價格如何計算?

目前的輸入與輸出 token 費率顯示在 playground 上方。

/ 05

我可以透過 API 呼叫 GPT-5.6 Sol 嗎?

可以。請參考連結的 API 文件,並使用模型 ID gpt-5.6-sol。

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