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AnthropicText Chat

Claude Opus 4.8 Chat Playground e API de IA

Prueba Claude Opus 4.8 en línea para programación agéntica, depuración difícil, razonamiento complejo y trabajo de conocimiento profesional con precios transparentes por token.

EntradaOficial $5.00 por 1 M de tokensAIReiter $3.50 por 1 M de tokensSalidaOficial $25.00 por 1 M de tokensAIReiter $17.50 por 1 M de tokensLectura de cachéOficial $0.50 por 1 M de tokensAIReiter $0.35 por 1 M de tokensCreación de cachéOficial $6.25 por 1 M de tokensAIReiter $4.38 por 1 M de tokens
Tipo de modelo
Ejecutar con API
PlaygroundReadmeAPI

ENTRADA

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.

SALIDA

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
Token de entrada
134
Token de salida
2354
Tokens per second
55.13 tokens / second
Time to first token
-

Detalles del modelo

Usa la misma clave de modelo en Playground, solicitudes API y flujos de trabajo internos.

ID del modelo
claude-opus-4-8
Proveedor
Anthropic
Protocolo
Anthropic Messages
Ventana de contexto
1,000,000 tokens
Salida máxima
128,000 tokens
Token de entrada
350 créditos / 1 M de tokens
Token de salida
1,750 créditos / 1 M de tokens
Lectura de caché
35 créditos / 1 M de tokens
Escritura de caché
437.5 créditos / 1 M de tokens

Lo que puedes hacer con Claude Opus 4.8

Elige Claude Opus 4.8 para programación difícil, depuración, planificación de agentes y análisis profesional que se benefician de un trabajo deliberado de varios pasos.

Programación Agéntica

Planifica y ejecuta trabajo de programación en varios archivos mientras mantienes en vista las restricciones y los resultados anteriores.

Depuración Difícil

Rastrea fallos entre componentes, prueba hipótesis y explica la causa raíz más probable.

Razonamiento de Arquitectura

Evalúa los límites del sistema, los planes de migración y las compensaciones técnicas antes de la implementación.

Análisis Profesional

Aborda preguntas técnicas detalladas, operativas o con mucha carga de conocimiento con razonamiento explícito.

Casos de uso de Claude Opus 4.8

Más adecuado para flujos de trabajo de ingeniería sénior y de conocimiento que necesitan un análisis deliberado en lugar de una respuesta genérica rápida.
01

Refactorizaciones Grandes

Razona sobre cambios a nivel de sistema antes de editar archivos individuales.

02

Análisis de la Causa Raíz

Prueba explicaciones en competencia para defectos difíciles.

03

Revisión de Diseño Técnico

Cuestiona supuestos y compara opciones arquitectónicas.

04

Trabajo con Mucha Información

Analiza material fuente detallado y produce una respuesta profesional.

Cómo usar Claude Opus 4.8

Prueba el modelo en tres pasos sencillos.

01

Elige tu configuración

Configura los controles de respuesta y las opciones de carga compatibles con el modelo.

02

Envía una instrucción

Describe la tarea, añade el contexto relevante y revisa la respuesta en streaming y el uso de tokens.

03

Conecta la API

Usa el endpoint documentado y tu clave de API para llevar el mismo modelo a tu producto.

Crea con la API de Claude Opus 4.8

Pasa de una prueba interactiva a una integración de producción con controles predecibles e informes de uso.

Protocolos familiares

Usa el protocolo API configurado para este modelo, incluida la transmisión en tiempo real donde esté disponible.

Visibilidad del uso

Haz seguimiento de los tokens de entrada, los tokens de salida y los créditos consumidos después de cada respuesta.

Controles específicos del modelo

Pasa los parámetros de generación compatibles en lugar de depender de valores predeterminados genéricos.

Una sola cuenta y saldo

Prueba y utiliza los modelos de texto compatibles a través de la misma cuenta de AIReiter y el mismo sistema de facturación.

Preguntas frecuentes de Claude Opus 4.8

Preguntas comunes sobre el playground en línea, los precios y el acceso a la API.

/ 01

¿Para qué es mejor Claude Opus 4.8?

Evalúalo para programación agéntica, depuración difícil, razonamiento de arquitectura y trabajo de conocimiento profesional.

/ 02

¿Es útil Claude Opus 4.8 para grandes refactorizaciones?

Es un candidato sólido cuando una refactorización requiere contexto del sistema, análisis de dependencias y un plan de migración explícito.

/ 03

¿Debería usar Opus 4.8 para un chat simple?

Por lo general, no. Un nivel más ligero es más económico para solicitudes cortas, rutinarias o sensibles a la latencia.

/ 04

¿Cómo se fija el precio de Claude Opus 4.8?

Las tarifas de tokens de entrada y salida se muestran encima del área de prueba.

/ 05

¿Puedo usar Claude Opus 4.8 a través de una API?

Sí. Usa la documentación de API enlazada y el ID del modelo que se muestra en esta página.

AIREITER

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