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GPT-5.6 Sol KI-Chat-Playground und API

Testen Sie GPT-5.6 Sol online für die anspruchsvollsten Coding-, Reasoning- und Agent-Workflows in der GPT-5.6-Familie. Vergleichen Sie die Token-Preise und integrieren Sie die API.

EingabeOffiziell $4.00 pro 1 Mio. TokensAIReiter $1.20 pro 1 Mio. TokensAusgabeOffiziell $20.00 pro 1 Mio. TokensAIReiter $6.00 pro 1 Mio. TokensCache-LesenOffiziell $0.40 pro 1 Mio. TokensAIReiter $0.12 pro 1 Mio. TokensCache-ErstellungOffiziell $5.00 pro 1 Mio. TokensAIReiter $1.50 pro 1 Mio. Tokens
Mit API ausführen
PlaygroundREADMEAPI

EINGABE

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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.

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-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
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-5.6-sol
Anbieter
OpenAI
Protokoll
OpenAI Chat Completions
Kontextfenster
1,050,000 Token
Maximale Ausgabe
128,000 Token
Eingabe-Token
120 Credits / 1 Mio. Token
Ausgabe-Token
600 Credits / 1 Mio. Token
Cache-Lesen
12 Credits / 1 Mio. Token
Cache-Schreiben
150 Credits / 1 Mio. Token

Was Sie mit GPT-5.6 Sol tun können

Wählen Sie GPT-5.6 Sol für die komplexesten Aufgaben in der GPT-5.6-Familie, insbesondere schwierige Code-, Reasoning- und Agent-Aufgaben.

Flaggschiff-Reasoning

Nutzen Sie die stärkste GPT-5.6-Stufe für schwierige Probleme mit mehreren miteinander verknüpften Einschränkungen.

Komplexe Softwarearbeit

Entwerfen, debuggen und überprüfen Sie Systeme, bei denen oberflächliches Pattern Matching nicht ausreicht.

Fortgeschrittene Agents

Planen Sie längere Workflows, interpretieren Sie Tool-Ergebnisse und erholen Sie sich, wenn ein Zwischenschritt fehlschlägt.

Tiefgehende technische Analyse

Vergleichen Sie Architekturen, Risiken und Implementierungswege mit klaren Abwägungen.

GPT-5.6 Sol Anwendungsfälle

Am besten geeignet für die schwierigsten Anfragen in einem gerouteten GPT-5.6-Stack; verwenden Sie Terra oder Luna, wenn die Aufgabe keine Flaggschiff-Tiefe erfordert.
01

Schwierige Coding-Aufgaben

Verwenden Sie es für Architektur, Debugging über mehrere Dateien hinweg und komplexe Implementierungen.

02

Fortgeschrittene Agent-Aufgaben

Verwenden Sie es, wenn Pläne mehrere Tool-Aufrufe und Überarbeitungen überstehen müssen.

03

Tiefgehende Analyse

Verwenden Sie es für Entscheidungen mit widersprüchlichen Belegen und wichtigen Abwägungen.

04

Eskalationsstufe

Leiten Sie schwierige Anfragen hierher, nachdem ein leichteres Modell sie nicht zuverlässig abschließen konnte.

So verwenden Sie GPT-5.6 Sol

Teste das Modell in drei einfachen Schritten.

01

Wähle deine Einstellungen

Lege die vom Modell unterstützten Antwortsteuerungen und Upload-Optionen fest.

02

Sende einen Prompt

Beschreibe die Aufgabe, füge relevanten Kontext hinzu und prüfe die gestreamte Antwort sowie die Token-Nutzung.

03

Verbinde die API

Nutze den dokumentierten Endpunkt und deinen API key, um dasselbe Modell in dein Produkt zu integrieren.

Entwickeln mit der GPT-5.6 Sol API

Wechseln Sie von einem interaktiven Test zu einer Produktionsintegration mit vorhersehbaren Steuerungen und Nutzungsberichten.

Vertraute Protokolle

Verwenden Sie das für dieses Modell konfigurierte API-Protokoll, einschließlich Streaming, sofern verfügbar.

Nutzbarkeitstransparenz

Verfolgen Sie Eingabe-Tokens, Ausgabe-Tokens und verbrauchte Credits nach jeder Antwort.

Modellspezifische Steuerungen

Übergeben Sie die unterstützten Generierungsparameter, statt sich auf generische Standardwerte zu verlassen.

Ein Konto und ein Guthaben

Testen und betreiben Sie unterstützte Textmodelle über dasselbe AIReiter-Konto und dasselbe Abrechnungssystem.

GPT-5.6 Sol FAQ

Häufige Fragen zum Online-Playground, zur Preisgestaltung und zum API-Zugriff.

/ 01

Wann sollte ich GPT-5.6 Sol wählen?

Wählen Sie Sol für die schwierigsten Coding-, Reasoning- und Agent-Aufgaben in der GPT-5.6-Familie.

/ 02

Worin unterscheidet sich Sol von Terra und Luna?

Sol ist die Flaggschiff-Stufe; Terra ist die ausgewogene Produktionsstufe, während Luna Geschwindigkeit und Kosten priorisiert.

/ 03

Soll der gesamte GPT-5.6-Traffic Sol nutzen?

Nein. Verwenden Sie Routing und Evaluierungen, damit Routineanfragen auf Terra oder Luna bleiben.

/ 04

Wie ist GPT-5.6 Sol bepreist?

Die aktuellen Raten für Input- und Output-Tokens werden oberhalb des Playgrounds angezeigt.

/ 05

Kann ich GPT-5.6 Sol über eine API aufrufen?

Ja. Folgen Sie der verlinkten API-Dokumentation und verwenden Sie die Modell-ID gpt-5.6-sol.

AIREITER

Fragen? Kontaktieren Sie uns unter
[email protected]

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