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Claude Opus 4.8 KI-Chat-Playground und API

Teste Claude Opus 4.8 online für agentic coding, schwieriges Debugging, komplexes Schlussfolgern und professionelle Wissensarbeit mit transparenter Token-Preisgestaltung.

EingabeOffiziell $5.00 pro 1 Mio. TokensAIReiter $3.50 pro 1 Mio. TokensAusgabeOffiziell $25.00 pro 1 Mio. TokensAIReiter $17.50 pro 1 Mio. TokensCache-LesenOffiziell $0.50 pro 1 Mio. TokensAIReiter $0.35 pro 1 Mio. TokensCache-ErstellungOffiziell $6.25 pro 1 Mio. TokensAIReiter $4.38 pro 1 Mio. Tokens
Modelltyp
Mit API ausführen
PlaygroundREADMEAPI

EINGABE

imagefile[]
Optional input images sent alongside the prompt. Up to 5 files. Images are billed as input tokens.
Let the model reason before answering. The model decides how much thinking each request needs.Default: false
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Install the official Anthropic client — AIReiter speaks the same protocol, so only the base URL changes:

npm install @anthropic-ai/sdk

Set the AIREITER_API_KEY environment variable:

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

Point the client at AIReiter:

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic({
  apiKey: process.env.AIREITER_API_KEY,
  baseURL: "https://aireiter.com/api",
});

Run claude-opus-4-8:

const message = await client.messages.create({
    "model": "claude-opus-4-8",
    "max_tokens": 4096,
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "output_config": {
      "effort": "medium"
    }
  });

console.log(message.content);

Stream the response instead:

const stream = client.messages.stream({
    "model": "claude-opus-4-8",
    "max_tokens": 4096,
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "output_config": {
      "effort": "medium"
    }
  });

stream.on("text", (text) => process.stdout.write(text));
const message = await stream.finalMessage();

Install the official Anthropic client — AIReiter speaks the same protocol, so only the base URL changes:

pip install anthropic

Set the AIREITER_API_KEY environment variable:

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

Point the client at AIReiter:

import os
import anthropic

client = anthropic.Anthropic(
    api_key=os.environ["AIREITER_API_KEY"],
    base_url="https://aireiter.com/api",
)

Run claude-opus-4-8:

message = client.messages.create(
      model = "claude-opus-4-8",
      max_tokens = 4096,
      messages = [
        {
          role = "user",
          content = "Explain what an API rate limit is and how to handle a 429 response in code."
        }
      ],
      output_config = {
        effort = "medium"
      }
)

print(message.content)

Stream the response instead:

with client.messages.stream(
      model = "claude-opus-4-8",
      max_tokens = 4096,
      messages = [
        {
          role = "user",
          content = "Explain what an API rate limit is and how to handle a 429 response in code."
        }
      ],
      output_config = {
        effort = "medium"
      }
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

Set the AIREITER_API_KEY environment variable:

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

Run claude-opus-4-8 against AIReiter's API:

curl -s -X POST \
  -H "x-api-key: $AIREITER_API_KEY" \
  -H "Content-Type: application/json" \
  "https://aireiter.com/api/v1/messages" \
  -d '{
  "model": "claude-opus-4-8",
  "max_tokens": 4096,
  "messages": [
    {
      "role": "user",
      "content": "Explain what an API rate limit is and how to handle a 429 response in code."
    }
  ],
  "output_config": {
    "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": "claude-opus-4-8",
  "input": {
    "model": "claude-opus-4-8",
    "max_tokens": 4096,
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "output_config": {
      "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
claude-opus-4-8
Anbieter
Anthropic
Protokoll
Anthropic Messages
Kontextfenster
1,000,000 Token
Maximale Ausgabe
128,000 Token
Eingabe-Token
350 Credits / 1 Mio. Token
Ausgabe-Token
1,750 Credits / 1 Mio. Token
Cache-Lesen
35 Credits / 1 Mio. Token
Cache-Schreiben
437.5 Credits / 1 Mio. Token

Was du mit Claude Opus 4.8 tun kannst

Wähle Claude Opus 4.8 für schwieriges Coding, Debugging, Agentenplanung und professionelle Analyse, die von einer bewussten mehrstufigen Bearbeitung profitiert.

Agentic Coding

Plane und führe Coding-Arbeiten über mehrere Dateien hinweg aus, während du Einschränkungen und frühere Ergebnisse im Blick behältst.

Schwieriges Debugging

Verfolge Fehler über Komponenten hinweg, teste Hypothesen und erkläre die wahrscheinlichste Ursache.

Architektur-Reasoning

Bewerte Systemgrenzen, Migrationspläne und technische Kompromisse vor der Implementierung.

Professionelle Analyse

Arbeite dich mit explizitem Schlussfolgern durch detaillierte technische, operative oder wissensintensive Fragen.

Claude Opus 4.8 Anwendungsfälle

Am besten geeignet für Senior-Engineering- und Wissens-Workflows, die eine bewusste Analyse statt einer schnellen generischen Antwort benötigen.
01

Große Refactorings

Denke über systemweite Änderungen nach, bevor du einzelne Dateien bearbeitest.

02

Ursachenanalyse

Teste konkurrierende Erklärungen für schwierige Fehler.

03

Technische Designprüfung

Stelle Annahmen infrage und vergleiche architektonische Optionen.

04

Wissensintensive Arbeit

Analysiere detailliertes Quellenmaterial und formuliere eine professionelle Antwort.

So verwendest du Claude Opus 4.8

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 Claude Opus 4.8 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.

Claude Opus 4.8 FAQ

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

/ 01

Wofür eignet sich Claude Opus 4.8 am besten?

Nutze es für agentic coding, schwieriges Debugging, Architektur-Reasoning und professionelle Wissensarbeit.

/ 02

Ist Claude Opus 4.8 für große Refactorings nützlich?

Es ist eine starke Wahl, wenn ein Refactoring Systemkontext, Abhängigkeitsanalyse und einen expliziten Migrationsplan erfordert.

/ 03

Sollte ich Opus 4.8 für einfachen Chat verwenden?

Meistens nicht. Eine leichtere Stufe ist wirtschaftlicher für kurze, routinemäßige oder latenzsensitive Anfragen.

/ 04

Wie wird Claude Opus 4.8 bepreist?

Die Token-Raten für Eingabe und Ausgabe werden oberhalb des Playgrounds angezeigt.

/ 05

Kann ich Claude Opus 4.8 über eine API aufrufen?

Ja. Verwenden Sie die verlinkte API-Dokumentation und die auf dieser Seite angegebene Modell-ID.

AIREITER

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