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GPT-5.6 Sol AI 聊天 Playground 和 API

在线试用 GPT-5.6 Sol,体验 GPT-5.6 家族中最严苛的编码、推理和 agent 工作流。比较 token 定价并集成 API。

输入官方 $4.00 每 100 万 TokensAIReiter $1.20 每 100 万 Tokens输出官方 $20.00 每 100 万 TokensAIReiter $6.00 每 100 万 Tokens缓存读取官方 $0.40 每 100 万 TokensAIReiter $0.12 每 100 万 Tokens缓存创建官方 $5.00 每 100 万 TokensAIReiter $1.50 每 100 万 Tokens
使用 API 运行
Playground说明文档API

输入

imagefile[]
Optional input images sent alongside the prompt. Up to 5 files. Images are billed as input tokens.
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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.

输出

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
输入 Token
134
输出 Token
2354
Tokens per second
55.13 tokens / second
Time to first token
-

模型详情

在 Playground、API 请求和内部工作流中使用相同的模型 key。

模型 ID
gpt-5.6-sol
供应商
OpenAI
协议
OpenAI Chat Completions
上下文窗口
1,050,000 Token
最大输出
128,000 Token
输入 Token
120 credits / 1M Token
输出 Token
600 credits / 1M Token
缓存读取
12 credits / 1M Token
缓存写入
150 credits / 1M Token

使用 GPT-5.6 Sol 你能做什么

选择 GPT-5.6 Sol 来处理 GPT-5.6 家族中复杂度最高的工作,尤其是困难的代码、推理和 agent 任务。

旗舰级推理

针对具有多个相互制约条件的复杂问题,使用最强的 GPT-5.6 级别。

复杂软件工作

设计、调试和审查仅靠浅层模式匹配远远不够的系统。

高级 Agent

规划更长的工作流,解读工具结果,并在中间步骤失败时恢复。

深度技术分析

在明确权衡下比较架构、风险和实现路径。

GPT-5.6 Sol 使用场景

最适合路由式 GPT-5.6 栈中最困难的请求;当任务不需要旗舰级深度时,请使用 Terra 或 Luna。
01

高难度编码任务

适用于架构设计、多文件调试和复杂实现。

02

高级 Agent 任务

适用于计划必须经受多次工具调用和修订的场景。

03

深度分析

适用于存在相互矛盾证据和重要权衡的决策。

04

升级层级

当较轻量的模型无法可靠完成时,将困难请求路由到这里。

如何使用 GPT-5.6 Sol

通过三个简单步骤测试该模型。

01

选择你的设置

设置模型支持的响应控制和上传选项。

02

发送提示词

描述任务,添加相关上下文,并查看流式响应和 token 使用情况。

03

连接 API

使用文档中的端点和你的 API key,将同一模型接入你的产品。

使用 GPT-5.6 Sol API 构建

从交互式测试到生产集成,使用可预测的控制和用量报告顺利过渡。

熟悉的协议

使用为此模型配置的 API 协议,包括可用的流式传输。

用量可见性

在每次响应后跟踪输入 token、输出 token 和已消耗的积分。

模型专属控制

传入支持的生成参数,而不是依赖通用默认值。

一个账户和余额

通过同一个 AIReiter 账户和计费系统测试并使用受支持的文本模型。

GPT-5.6 Sol 常见问题

关于在线 playground、价格和 API 访问的常见问题。

/ 01

我应该什么时候选择 GPT-5.6 Sol?

对于 GPT-5.6 家族中最困难的编码、推理和 agent 任务,选择 Sol。

/ 02

Sol 与 Terra 和 Luna 有什么不同?

Sol 是旗舰层级;Terra 是平衡的生产层级,而 Luna 则优先考虑速度和成本。

/ 03

所有 GPT-5.6 流量都应该使用 Sol 吗?

不。请使用路由和评估,让常规请求继续留在 Terra 或 Luna 上。

/ 04

GPT-5.6 Sol 如何定价?

当前的输入和输出 token 费率显示在 playground 上方。

/ 05

我可以通过 API 调用 GPT-5.6 Sol 吗?

可以。请查看链接中的 API 文档,并使用模型 ID gpt-5.6-sol。

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