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GPT-5.6 Terra AI 聊天 Playground 和 API

在线试用 GPT-5.6 Terra,适用于平衡的生产级编码、分析和自动化。测试提示词,监控 token 使用量,并通过 API 连接。

输入官方 $2.00 每 100 万 TokensAIReiter $0.60 每 100 万 Tokens输出官方 $12.00 每 100 万 TokensAIReiter $3.60 每 100 万 Tokens缓存读取官方 $0.20 每 100 万 TokensAIReiter $0.06 每 100 万 Tokens缓存创建官方 $2.50 每 100 万 TokensAIReiter $0.75 每 100 万 Tokens
使用 API 运行
Playground说明文档API

输入

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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-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.

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-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
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-terra
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
60 credits / 1M Token
输出 Token
360 credits / 1M Token
缓存读取
6 credits / 1M Token
缓存写入
75 credits / 1M Token

使用 GPT-5.6 Terra 你能做什么

当你需要在生产工作负载中兼顾能力、响应速度和成本时,选择 GPT-5.6 Terra。

均衡编码

以面向生产的质量处理功能开发、调试、测试和代码说明。

运营分析

将日志、报告和需求转化为结构化发现和后续行动。

工作流自动化

支持那些比轻量级层级更需要推理的智能体和后端流程。

商务写作

以均衡的成本创建清晰的规格说明、简报和面向客户的内容。

GPT-5.6 Terra 使用场景

最适合需要比始终使用旗舰层级更优能力/成本平衡的生产默认场景。
01

生产级编码

将其作为功能开发和维护工作的实用默认选择。

02

分析流水线

从运营材料中提取发现和建议。

03

商务助手

支持重复性的写作、规划和知识任务。

04

中阶路由

处理那些超出 Luna 能力范围但又不想每次请求都为 Sol 支付成本的工作。

如何使用 GPT-5.6 Terra

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 Terra 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 Terra 常见问题

关于在线 playground、价格和 API 访问的常见问题。

/ 01

我应该在什么时候选择 GPT-5.6 Terra?

将 Terra 作为生产级编码、分析、写作和自动化的均衡默认选择。

/ 02

Terra 与 Sol 和 Luna 有什么不同?

Terra 介于旗舰 Sol 和经济型 Luna 之间,因此适合进行能力/成本路由。

/ 03

Terra 可以作为应用默认模型吗?

可以,但请在你的真实工作负载上进行验证,并且只将需要 Sol 的请求升级。

/ 04

GPT-5.6 Terra 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 Terra 吗?

可以。请参阅链接中的 API 文档,并使用模型 ID gpt-5.6-terra。

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