Claude Fable 5 — Especificaciones
Compiled by Mustafa Ihsan from the vendor’s published documentation · Last updated
| Desarrollador | Anthropic |
|---|---|
| Tipo | LLM (razonamiento de vanguardia) |
| Modalidad | Texto y visión → texto |
| Parámetros | No revelado |
| Ventana de contexto | 1 millón |
| Salida máxima | 128 K |
| Licencia | Propietario |
| Pesos abiertos | No |
| Lanzado | 2026 |
| Precio de entrada | $10.00 /1M |
| Precio de salida | $50.00 /1M |
| Proveedores de API | Anthropic, AWS |
What is Claude Fable 5?
Claude Fable 5 is Anthropic’s most capable widely released model, positioned above the
Opus tier and priced accordingly at $10 in / $50 out per million tokens. Thinking is always
on — there is no non-reasoning mode to fall back to — which is what makes it strong on
long-horizon agentic work and what makes it expensive on anything trivial. It pairs a
1M-token context window with a 128K maximum output, an unusually large generation budget
that matters when the task is producing a long artifact rather than answering a question.
The economics only work at the top of the difficulty curve. At $50 per million output
tokens, Fable 5 costs roughly five times Claude Opus 4.8 on the output side, so routing
ordinary requests to it burns budget for no measurable gain. The sensible pattern is a
router: Fable 5 for the small share of tasks where a wrong answer is expensive — multi-step
agents that run unattended, deep research, refactors across a large codebase — and a
Sonnet- or Haiku-tier model for everything else. If your workload is mostly retrieval and
summarisation, the price gap will dominate your bill long before capability does.
Claude Fable 5 pricing: API cost per 1M tokens
| Entrada (por cada millón de tokens) | $10.00 |
|---|---|
| Salida (por cada millón de tokens) | $50.00 |
| Relación salida/entrada | 5× |
| Combinada (4:1 entrada:salida) | $18.00 por 1 millón de tokens |
What Claude Fable 5 costs per month
Gasto mensual real con una mezcla entrada:salida de 4:1, es decir, la proporción que realmente genera una carga de trabajo típica de chat o RAG.
| Carga de trabajo | Tokens/mes | Coste mensual |
|---|---|---|
| Proyecto secundario | 1 millón de tokens de entrada / 0,25 millones de tokens de salida | $23 |
| Pequeño equipo | 20 millones de tokens de entrada / 5 millones de tokens de salida | $450 |
| Producción | 200 millones de tokens de entrada / 50 millones de tokens de salida | $4,500 |
Calcule sus propios números en la Calculadora de costos de API de IA.
Cheaper alternatives to Claude Fable 5
| Modelo | Dólares por millón combinados | Usted ahorra |
|---|---|---|
| Claude Opus 5 | $9.00 | 50% cheaper |
| GPT-5.6 Sol | $10.00 | 44 % más barato |
| Kimi K3 abierta | $5.40 | 70% cheaper |
Preguntas frecuentes
How much does Claude Fable 5 cost per 1M tokens?
Claude Fable 5 costs $10.00 per 1M input tokens and $50.00 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $18.00 per 1M tokens.
How much does Claude Fable 5 cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $450 on Claude Fable 5. A side project (1M in / 0.25M out) costs roughly $22.50.
What is a cheaper alternative to Claude Fable 5?
Claude Opus 5 is the strongest cheaper option in our database at $9.00 per 1M blended — about 50% less than Claude Fable 5.
Can I run Claude Fable 5 locally?
No. Claude Fable 5 is a closed, API-only model — the weights are not released, so it cannot be self-hosted.
Why does Claude Fable 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 Fable 5 charges 5× more for output, which is why prompt-heavy workloads are far cheaper to run than generation-heavy ones.
Vea todos los modelos Claude comparados lado a lado: Precios de la API de Claude.
Los precios corresponden a las tarifas oficiales publicadas para la API principal del modelo y se revisan periódicamente conforme los proveedores los actualicen. No incluyen descuentos por volumen, procesamiento por lotes ni entradas en caché. Compare todos los modelos uno al lado del otro en la Base de datos de modelos de IA o el Clasificación de modelos de lenguaje grande (LLM).