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Gemini 3.6 Flash AI-Chat-Playground und API

Testen Sie Gemini 3.6 Flash online für reaktionsschnelle Assistenten, schnelle Inhaltsverarbeitung und API-Workflows mit hohem Volumen, mit gestreamter Ausgabe und sichtbarer Token-Nutzung.

EingabeOffiziell $0.75 pro 1 Mio. TokensAIReiter $0.225 pro 1 Mio. TokensAusgabeOffiziell $3.75 pro 1 Mio. TokensAIReiter $1.125 pro 1 Mio. TokensCache-LesenOffiziell $0.075 pro 1 Mio. TokensAIReiter $0.0225 pro 1 Mio. Tokens
Mit API ausführen
PlaygroundREADMEAPI

EINGABE

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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 gemini-3.6-flash:

const response = await client.chat.completions.create({
    "model": "gemini-3.6-flash",
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "max_tokens": 4096,
    "temperature": 1,
    "top_p": 1
  });

console.log(response);

Stream the response instead:

const stream = await client.chat.completions.create({
  ...{
    "model": "gemini-3.6-flash",
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "max_tokens": 4096,
    "temperature": 1,
    "top_p": 1
  },
  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 gemini-3.6-flash:

response = client.chat.completions.create(
      model = "gemini-3.6-flash",
      messages = [
        {
          role = "user",
          content = "Explain what an API rate limit is and how to handle a 429 response in code."
        }
      ],
      max_tokens = 4096,
      temperature = 1,
      top_p = 1
)

print(response)

Stream the response instead:

stream = client.chat.completions.create(
      model = "gemini-3.6-flash",
      messages = [
        {
          role = "user",
          content = "Explain what an API rate limit is and how to handle a 429 response in code."
        }
      ],
      max_tokens = 4096,
      temperature = 1,
      top_p = 1,
    stream=True,
)

for event in stream:
    print(event)

Set the AIREITER_API_KEY environment variable:

export AIREITER_API_KEY=<paste-your-key-here>

Run gemini-3.6-flash 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": "gemini-3.6-flash",
  "messages": [
    {
      "role": "user",
      "content": "Explain what an API rate limit is and how to handle a 429 response in code."
    }
  ],
  "max_tokens": 4096,
  "temperature": 1,
  "top_p": 1
}'

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": "gemini-3.6-flash",
  "input": {
    "model": "gemini-3.6-flash",
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "max_tokens": 4096,
    "temperature": 1,
    "top_p": 1
  },
  "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
gemini-3.6-flash
Anbieter
Google
Protokoll
OpenAI Chat Completions
Kontextfenster
1,048,576 Token
Maximale Ausgabe
65,536 Token
Eingabe-Token
22.5 Credits / 1 Mio. Token
Ausgabe-Token
112.5 Credits / 1 Mio. Token
Cache-Lesen
2.25 Credits / 1 Mio. Token
Cache-Schreiben
-

Was Sie mit Gemini 3.6 Flash tun können

Wählen Sie Gemini 3.6 Flash für reaktionsschnelle Produkte, die schnelle Antworten und zuverlässigen Durchsatz bei vielen Anfragen benötigen.

Reaktionsschnelle Assistenten

Halten Sie interaktive Chat- und Produktivitätserlebnisse mit gestreamten Antworten am Laufen.

Schnelle Dokumentenverarbeitung

Fassen Sie eingehende Texte in Produktionsgeschwindigkeit zusammen, transformieren Sie sie und extrahieren Sie Informationen daraus.

Content-Operationen

Erstellen Sie Varianten, Metadaten, Gliederungen und strukturierte Entwürfe über große Warteschlangen hinweg.

API-Automatisierung

Führen Sie häufige Textaufgaben aus, bei denen vorhersehbarer Durchsatz ebenso wichtig ist wie die Antwortqualität.

Gemini 3.6 Flash Anwendungsfälle

Am besten geeignet für interaktive Produkte mit hohem Durchsatz, die ein stärkeres Modell der Flash-Klasse benötigen.
01

Interaktiver Chat

Halten Sie Assistenten bei wiederholten Nutzereingaben reaktionsschnell.

02

Dokumenten-Pipelines

Fassen Sie eingehende Inhalte schnell zusammen und transformieren Sie sie.

03

Marketing-Operationen

Erstellen Sie Varianten, Tags und Briefings in großen Chargen.

04

Automatisierungs-Backends

Verarbeiten Sie wiederkehrende Textaufgaben mit vorhersehbarem Durchsatz.

So verwenden Sie Gemini 3.6 Flash

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.

Mit der Gemini 3.6 Flash API entwickeln

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.

Gemini 3.6 Flash FAQ

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

/ 01

Wofür eignet sich Gemini 3.6 Flash am besten?

Verwenden Sie es für reaktionsschnelle Assistenten, schnelle Dokumentenverarbeitung, Content-Operationen und häufige API-Jobs.

/ 02

Wie sollte ich Gemini 3.6 Flash bewerten?

Testen Sie repräsentative Prompts sowohl auf Antwortqualität als auch auf Latenz, bevor Sie Produktionsverkehr mit hohem Volumen zuweisen.

/ 03

Streamt Gemini 3.6 Flash Antworten?

Ja. Das Playground zeigt die Ausgabe als Stream an, während sie generiert wird.

/ 04

Wie wird Gemini 3.6 Flash bepreist?

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

/ 05

Kann ich über eine API auf Gemini 3.6 Flash zugreifen?

Ja. Verwenden Sie die verlinkte API-Dokumentation und die Seitenmodell-ID.

AIREITER

Fragen? Kontaktieren Sie uns unter
[email protected]

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