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GPT-6 Astra API 与在线 Playground | AIReiter

GPT-6 Astra 适用于复杂推理、编程和长文档分析。在 AIReiter Playground 试用,通过 API 接入 GPT-6 Astra。

输入 Token输入输出缓存读取缓存创建
≤ 272,000$3.00 每 100 万 Tokens$15.00 每 100 万 Tokens$0.30 每 100 万 Tokens$3.75 每 100 万 Tokens
> 272,000$6.00 每 100 万 Tokens$22.50 每 100 万 Tokens$0.60 每 100 万 Tokens$7.50 每 100 万 Tokens

价格按每百万 token 计。按包含缓存读写的总输入 token 数选择档位,整次请求按该档计费。

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-6-astra:

const response = await client.chat.completions.create({
    "model": "gpt-6-astra",
    "messages": [
      {
        "role": "user",
        "content": "Review a database migration plan for data-loss risks and propose verification steps."
      }
    ],
    "max_tokens": 4096,
    "reasoning_effort": "medium"
  });

console.log(response);

Stream the response instead:

const stream = await client.chat.completions.create({
  ...{
    "model": "gpt-6-astra",
    "messages": [
      {
        "role": "user",
        "content": "Review a database migration plan for data-loss risks and propose verification steps."
      }
    ],
    "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-6-astra:

response = client.chat.completions.create(
      model = "gpt-6-astra",
      messages = [
        {
          role = "user",
          content = "Review a database migration plan for data-loss risks and propose verification steps."
        }
      ],
      max_tokens = 4096,
      reasoning_effort = "medium"
)

print(response)

Stream the response instead:

stream = client.chat.completions.create(
      model = "gpt-6-astra",
      messages = [
        {
          role = "user",
          content = "Review a database migration plan for data-loss risks and propose verification steps."
        }
      ],
      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-6-astra 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-6-astra",
  "messages": [
    {
      "role": "user",
      "content": "Review a database migration plan for data-loss risks and propose verification steps."
    }
  ],
  "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-6-astra",
  "input": {
    "model": "gpt-6-astra",
    "messages": [
      {
        "role": "user",
        "content": "Review a database migration plan for data-loss risks and propose verification steps."
      }
    ],
    "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-6-astra
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

GPT-6 Astra 的核心能力

OpenAI 面向复杂推理、编程和长上下文工作的前沿模型。

复杂推理

处理多个约束相互影响、证据不完整且验收条件明确的决策任务。

软件工程

规划、实现、调试和审查跨文件变更,同时关注架构、测试与运行风险。

长文档分析

在 1,050,000 token 上下文内分析大型报告、需求、规范和研究资料。

图像理解工作流

当任务需要视觉检查时,将文字指令与参考图片一起提交。

什么时候选择 GPT-6 Astra

当任务复杂度比最低 token 成本更重要时选择 Astra。
01

架构与迁移审查

实施前评估系统边界、数据迁移、回滚路径和失败模式。

02

高难度编程

适合跨模块调试、大型重构和多约束代码生成。

03

研究资料综合

区分资料中的证据、假设和仍待确认的问题。

04

技术文档

按明确读者和格式草拟技术方案、规范和决策记录。

如何选择推理强度

从 medium 开始,只在任务需要更深入分析时提高强度。

Low

适合直接转换、短摘要和常规问题。

Medium

默认档,适合日常编程、分析和文档任务。

High 与 xhigh

适合复杂调试、架构取舍和多个竞争约束。

Max

用于最难任务,应基于真实工作负载评估推理 token、延迟和质量。

普通与长上下文价格

本页顶部和 Pricing 页的实时表格是当前价格的事实来源。
01

输入不超过 272,000 token

每百万 token:输入 $3、输出 $15、缓存读取 $0.30、缓存写入 $3.75。

02

输入超过 272,000 token

每百万 token:输入 $6、输出 $22.50、缓存读取 $0.60、缓存写入 $7.50。整次请求使用长上下文档。

03

阈值统计

普通输入、缓存读取和缓存写入计入总输入;输出不参与档位判断。

04

示例

100,000 输入加 10,000 输出为 $0.45;300,000 输入加 10,000 输出为 $2.025。

调用 GPT-6 Astra API

使用 AIReiter API key 和公开模型名 gpt-6-astra。

01

创建 API key

创建服务端 key,不要放进浏览器代码或公开仓库。

02

发送请求

POST 到 https://aireiter.com/api/v1/chat/completions,model 填 gpt-6-astra,并提供 messages。

03

核验结果

检查状态、结束原因、回答和 usage;临时失败采用有限次数退避重试。

GPT-6 Astra、GPT-5.6 Sol 与 Terra

按任务复杂度选择。GPT-5.6 Sol 适合高难度专业工作,GPT-5.6 Terra 平衡成本与能力。

模型适合场景
GPT-6 Astra最复杂的推理、编程和长上下文任务
GPT-5.6 Sol高难度编码、代理和专业工作
GPT-5.6 Terra兼顾成本与能力的日常工作

继续了解与接入

比较模型、查看实时价格或阅读 API 文档。

GPT-5.6 Sol Playground

比较 Astra 与 GPT-5.6 Sol。

GPT-5.6 Terra Playground

需要更低成本时选择 Terra。

AIReiter Pricing

查看当前 token 价格。

API 文档

阅读认证与请求格式。

GPT-6 Astra 常见问题

/ 01

什么时候选择 GPT-6 Astra?

适合最复杂的推理、编程、架构和长上下文任务。

/ 02

上下文和输出上限是多少?

页面显示 1,050,000 token 上下文和最高 128,000 输出 token,账号限制也可能生效。

/ 03

为什么有两个价格档位?

总输入超过 272,000 token 后,整次请求采用长上下文价格;缓存读写计入阈值。

/ 04

API 使用什么模型名?

API 请求使用 gpt-6-astra。

/ 05

可以提交图片吗?

可以,图片按上游报告的输入 token 用量计费。

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AI 图片

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