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GPT-6 Astra API & Playground | AIReiter

Use GPT-6 Astra for complex reasoning, coding, and long-document analysis. Try it in the AIReiter Playground or connect through the API.

Eingabe-TokenEingabeAusgabeCache-LesenCache-Erstellung
≤ 272,000$3.00 pro 1 Mio. Tokens$15.00 pro 1 Mio. Tokens$0.30 pro 1 Mio. Tokens$3.75 pro 1 Mio. Tokens
> 272,000$6.00 pro 1 Mio. Tokens$22.50 pro 1 Mio. Tokens$0.60 pro 1 Mio. Tokens$7.50 pro 1 Mio. Tokens

Preise pro Million Token. Die Preisstufe gilt für die gesamte Anfrage, basierend auf allen Eingabe-Token einschließlich Cache-Lese- und Schreibvorgängen.

Mit API ausführen
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EINGABE

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.

AUSGABE

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
Eingabe-Token
134
Ausgabe-Token
2354
Tokens per second
55.13 tokens / second
Time to first token
-

Modelldetails

Verwenden Sie denselben Modellschlüssel im Playground, in API-Anfragen und in internen Workflows.

Modell-ID
gpt-6-astra
Anbieter
OpenAI
Protokoll
OpenAI Chat Completions
Kontextfenster
1,050,000 Token
Maximale Ausgabe
128,000 Token

What GPT-6 Astra is built for

OpenAI's frontier model for complex reasoning, coding, and long-context work.

Complex reasoning

Work through decisions with interacting constraints, incomplete evidence, and explicit acceptance criteria.

Software engineering

Plan, implement, debug, and review multi-file changes while keeping architecture, tests, and operational risk in view.

Long-document analysis

Analyze reports, requirements, specifications, and research collections within a 1,050,000-token context window.

Image-aware workflows

Combine text instructions with reference images when the task requires visual inspection.

When to choose GPT-6 Astra

Use Astra when task complexity matters more than minimizing token cost.
01

Architecture and migration reviews

Evaluate system boundaries, data migration plans, rollback paths, and failure modes before implementation.

02

Difficult coding tasks

Use it for cross-module debugging, substantial refactors, and code generation with multiple constraints.

03

Research synthesis

Distinguish evidence, assumptions, and unresolved questions across supplied source material.

04

Technical documents

Draft proposals, specifications, and decision records for a defined audience and format.

Choose a reasoning effort

Start at medium and increase effort when the task benefits from deeper analysis.

Low

Direct transformations, short summaries, and routine questions where latency matters.

Medium

The default for everyday coding, analysis, and document work.

High and xhigh

Difficult debugging, architecture tradeoffs, and competing constraints.

Max

The hardest tasks. Measure the additional reasoning tokens, latency, and quality on your workload.

Standard and long-context pricing

The live tables on this page and the Pricing page are the source of truth for current rates.
01

Up to 272,000 input tokens

Per 1M tokens: input $3, output $15, cached input $0.30, and cache writes $3.75.

02

Above 272,000 input tokens

Per 1M tokens: input $6, output $22.50, cached input $0.60, and cache writes $7.50. The rate applies to the entire request.

03

How the threshold is counted

Uncached input, cached reads, and cache writes count toward total input. Output does not select the band.

04

Example

100,000 input plus 10,000 output tokens costs $0.45. At 300,000 input plus 10,000 output tokens, the token charge is $2.025.

Call the GPT-6 Astra API

Use the public model ID gpt-6-astra with an AIReiter API key.

01

Create an API key

Create a server-side key in your account and keep it out of browser code and public repositories.

02

Send the request

POST to https://aireiter.com/api/v1/chat/completions with model gpt-6-astra and a messages array.

03

Verify the result

Check status, finish reason, response content, and usage. Retry transient failures with bounded backoff.

GPT-6 Astra vs GPT-5.6 Sol vs Terra

Choose by task complexity. GPT-5.6 Sol fits demanding professional work, while GPT-5.6 Terra balances cost and capability.

ModelBest for
GPT-6 AstraThe hardest reasoning, coding, and long-context tasks
GPT-5.6 SolDemanding coding, agents, and professional work
GPT-5.6 TerraEveryday work with a cost and capability balance

Explore and integrate

Compare models, check live pricing, or read the API documentation.

GPT-5.6 Sol Playground

Compare Astra with GPT-5.6 Sol.

GPT-5.6 Terra Playground

Choose Terra for a lower-cost balance.

AIReiter Pricing

Review current token prices.

API documentation

Read authentication and request guidance.

GPT-6 Astra FAQ

/ 01

When should I choose GPT-6 Astra?

Choose Astra for the hardest reasoning, coding, architecture, and long-context tasks.

/ 02

What are the limits?

The page shows a 1,050,000-token context and up to 128,000 output tokens. Account limits may also apply.

/ 03

Why are there two pricing bands?

Above 272,000 total input tokens, the entire request uses long-context rates. Cache reads and writes count toward the threshold.

/ 04

Which model ID should I use?

Use gpt-6-astra in API requests.

/ 05

Can I send images?

Yes. Image inputs are billed as input tokens according to reported usage.

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