Claude Haiku 4.5 — Especificaciones
| Desarrollador | Anthropic |
|---|---|
| Tipo | LLM |
| Modalidad | Texto y visión → texto |
| Parámetros | No revelado |
| Ventana de contexto | 200 000 |
| Salida máxima | 64 K |
| Licencia | Propietario |
| Pesos abiertos | No |
| Lanzado | 2025 |
| Precio de entrada | 1,00 USD / millón |
| Precio de salida | 5,00 USD por cada millón |
| Proveedores de API | Anthropic, AWS, Vertex AI, Azure |
What is Claude Haiku 4.5?
Claude Haiku 4.5 is Anthropic’s fast, low-cost tier: $1 per million input tokens and $5
per million output, with a 200K context window and 64K maximum output. It is built for the
high-volume, latency-sensitive end of a workload — classification, extraction, routing,
short-form generation, and the thousands of small calls an application makes that never
needed a frontier model in the first place.
Haiku’s real job in most production systems is to absorb traffic. A blended cost of about
$1.80 per million tokens means it can handle an order of magnitude more requests than an
Opus-tier model for the same money, which is why the standard architecture is Haiku in
front, escalating to Sonnet or Opus only when a confidence check fails. The 200K context is
the one place it is genuinely narrower than its siblings — the Sonnet and Opus tiers reach
1M — so document-scale work that needs the whole corpus in one prompt belongs elsewhere.
For everything that fits, Haiku 4.5 is usually the correct default, and treating it as the
baseline rather than the fallback is what keeps an AI feature’s unit economics viable.
Claude Haiku 4.5 pricing: API cost per 1M tokens
| Entrada (por cada millón de tokens) | $1.00 |
|---|---|
| Salida (por cada millón de tokens) | $5.00 |
| Output/input ratio | 5× |
| Blended (4:1 in:out) | $1.80 per 1M tokens |
What Claude Haiku 4.5 costs per month
Real monthly spend at a 4:1 input-to-output mix — the ratio a typical chat or RAG workload actually produces.
| Carga de trabajo | Tokens/mes | Cost / month |
|---|---|---|
| Proyecto secundario | 1 millón de tokens de entrada / 0,25 millones de tokens de salida | $2.25 |
| Pequeño equipo | 20 millones de tokens de entrada / 5 millones de tokens de salida | $45 |
| Producción | 200 millones de tokens de entrada / 50 millones de tokens de salida | $450 |
Run your own numbers in the Calculadora de costos de API de IA.
Cheaper alternatives to Claude Haiku 4.5
| Modelos | Dólares por millón combinados | You save |
|---|---|---|
| DeepSeek V4-Pro abierta | $0.522 | un 71 % más barato |
| Kimi K2.7 Code abierta | $0.980 | 46% cheaper |
| DeepSeek V4-Flash abierta | $0.168 | 91% cheaper |
Preguntas frecuentes
How much does Claude Haiku 4.5 cost per 1M tokens?
Claude Haiku 4.5 costs $1.00 per 1M input tokens and $5.00 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $1.80 per 1M tokens.
How much does Claude Haiku 4.5 cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $45 on Claude Haiku 4.5. A side project (1M in / 0.25M out) costs roughly $2.25.
What is a cheaper alternative to Claude Haiku 4.5?
DeepSeek V4-Pro is the strongest cheaper option in our database at $0.522 per 1M blended — about 71% less than Claude Haiku 4.5. It is also open-weight, so self-hosting is an option.
Can I run Claude Haiku 4.5 locally?
No. Claude Haiku 4.5 is a closed, API-only model — the weights are not released, so it cannot be self-hosted.
Why does Claude Haiku 4.5 charge more for output than input?
Output tokens are generated one at a time and cannot be batched the way a prompt can, so they cost the provider more to serve. Claude Haiku 4.5 charges 5× more for output, which is why prompt-heavy workloads are far cheaper to run than generation-heavy ones.
See every Claude model priced side by side: Claude API pricing.
Prices are the published list rates for the model's primary API and are reviewed as providers change them. Volume, batch and cached-input discounts are not included. Compare every model side by side in the Base de datos de modelos de IA o el Clasificación de modelos de lenguaje grande (LLM).
