Se facturan los tokens realmente utilizados; la parte no utilizada de la reserva se reembolsa.
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10 créditos gratis (USD 0.10) cuando te registras con Google
Utilizable 24 horas después del registro, hasta 5 ejecuciones por día y como máximo 2 créditos por ejecución. Otros métodos de inicio de sesión comienzan sin créditos. Suficiente para 16 ejecuciones de este modelo.
02
Acerca de Claude Haiku 4.5
ResumenA partir de 23 de septiembre de 2026
Claude Haiku 4.5 es un modelo de Anthropic en la categoría Multimodal. En Railwail, Claude Haiku 4.5 cuesta USD 1.20 por 1M tokens de entrada y USD 6.00 por 1M tokens de salida. La ventana de contexto contiene 200,000 tokens, y una respuesta puede tener hasta 64,000 tokens.
Claude Haiku 4.5 brings Sonnet-class reliability to the cost-and-latency tier. 200K context, full vision input, native tool use and extended thinking on demand. Designed for high-volume agents, real-time chat, classification, simple coding subagents, and consumer applications where p50 latency matters more than peak reasoning.
Fondo
Acerca de Anthropic
Fundado en 2021 · San Francisco, USA
Anthropic was founded in 2021 by Dario and Daniela Amodei. The Claude model family launched in March 2023 with Haiku as the lightweight tier optimized for low latency and cost. Successive Haiku generations have steadily closed the gap to Sonnet: Haiku 3 (March 2024), Haiku 3.5 (October 2024), Haiku 4 (May 2025) and Haiku 4.5 (late 2025/early 2026). Anthropic is structured as a Public Benefit Corporation focused on AI safety, with total funding exceeding $20 billion and a 2026 valuation above $150 billion.
Claude Haiku 4.5 is the fastest and cheapest model in the Claude 4.x family. Architecturally it is a smaller decoder-only Transformer distilled from the Sonnet/Opus 4 training stack, optimized aggressively for inference latency and throughput while retaining the full Claude feature set (vision, extended thinking, tool use). It was trained on the same multi-trillion-token curated corpus as its larger siblings with a knowledge cutoff in early 2026. Post-training combined RLHF, Constitutional AI, RLAIF and distillation from larger teacher models. Despite its small size, Haiku 4.5 supports the same 200K context window and tool-use API as Sonnet 4.6 and Opus 4.7, making it a drop-in cheaper option for high-concurrency workloads.
Parámetros
Undisclosed (estimated tens of billions of parameters)
Contexto
200,000 tokens
Capacidades
Lowest p50 latency in the Claude 4.x family
200K token context window matching the larger Claude models
Vision input for images, PDFs and screenshots
Native tool use with parallel tool calling
Optional extended thinking mode for harder reasoning tasks
Sonnet-class reliability at one-third the cost
Strong instruction following and structured JSON output
Constitutional-AI alignment with low refusal rate
Available via Anthropic API, Bedrock and Vertex AI
Pretrained on the same curated multi-trillion-token mixture used by Sonnet and Opus 4.x. Post-training includes distillation from larger Anthropic models alongside RLHF, Constitutional AI and RLAIF. Knowledge cutoff in early 2026.
Licencia: Proprietary commercial license via Anthropic API, Amazon Bedrock and Google Vertex AI.
Pruebas de seguridad: Same Responsible Scaling Policy ASL-3 safeguards as Sonnet and Opus, with constitutional classifiers and third-party evaluations.
Limitaciones conocidas
Below Sonnet 4.6 on hard reasoning and agentic coding benchmarks
No native audio or video input
Knowledge cutoff in early 2026
Less depth on niche or long-tail topics than larger models
curl https://railwail.com/api/v1/chat/completions \
-H "Authorization: Bearer $RAILWAIL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "claude-haiku-4-5",
"messages": [
{
"role": "user",
"content": "Explain what a vector database is in two sentences."
}
],
"max_tokens": 1024
}'
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["RAILWAIL_API_KEY"],
base_url="https://railwail.com/api/v1",
)
completion = client.chat.completions.create(
model="claude-haiku-4-5",
messages=[
{
"role": "user",
"content": "Explain what a vector database is in two sentences.",
},
],
max_tokens=1024,
)
print(completion.choices[0].message.content)
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.RAILWAIL_API_KEY,
baseURL: "https://railwail.com/api/v1",
});
const completion = await client.chat.completions.create({
model: "claude-haiku-4-5",
messages: [
{
role: "user",
content: "Explain what a vector database is in two sentences."
}
],
max_tokens: 1024
});
console.log(completion.choices[0].message.content);
// npm install railwail
import railwail from "railwail";
const rw = railwail(process.env.RAILWAIL_API_KEY);
const res = await rw.chat("claude-haiku-4-5", [
{ role: "user", content: "Explain what a vector database is in two sentences." },
], { max_tokens: 1024 });
console.log(res.choices[0].message.content);
Claude Haiku 4.5 es un modelo de Anthropic en la categoría Multimodal. En Railwail puedes llamarlo con una clave API a través de la API de Railwail.
¿Cuánto cuesta Claude Haiku 4.5 en Railwail?
En Railwail, Claude Haiku 4.5 cuesta USD 1.20 por 1M tokens de entrada y USD 6.00 por 1M tokens de salida. Se te cobra por lo que cada solicitud realmente usa. El uso se paga con créditos prepagados; 1 crédito equivale a USD 0.01.
¿Cuál es la ventana de contexto de Claude Haiku 4.5?
La ventana de contexto de Claude Haiku 4.5 contiene 200,000 tokens. Una respuesta puede tener hasta 64,000 tokens.
¿Qué tan rápido es Claude Haiku 4.5?
Aún no hay suficientes ejecuciones medidas de Claude Haiku 4.5 en Railwail para indicar un tiempo de ejecución. Depende de la entrada, la configuración y la carga en el proveedor.
¿Es Claude Haiku 4.5 mejor que BLIP?
Eso depende de la tarea. Claude Haiku 4.5 (Anthropic) y BLIP (Salesforce) son ambos modelos en la categoría Multimodal. La página de comparación muestra sus precios y especificaciones lado a lado.
Sí. Claude Haiku 4.5 acepta imágenes como entrada además de texto.
¿Cómo uso Claude Haiku 4.5 a través de la API?
Crea una clave API de Railwail y envía tu solicitud con el ID de modelo claude-haiku-4-5. Los ejemplos de código para curl, Python y JavaScript están en la sección API de esta página.
