Claude Opus 4.8 — Especificaciones
| Desarrollador | Anthropic |
|---|---|
| Tipo | LLM (razonamiento) |
| 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 | 5,00 USD por cada millón |
| Precio de salida | 25,00 USD por cada millón |
| Proveedores de API | Anthropic, AWS, Vertex AI, Azure |
What is Claude Opus 4.8?
Claude Opus 4.8 is Anthropic’s Opus-tier flagship for state-of-the-art long-horizon
agentic work, coding and knowledge tasks. It runs adaptive thinking only, and carries a
1M-token context window at standard pricing — there is no long-context premium, which
distinguishes it from several rivals that step the input rate up once a prompt passes a
threshold. At $5 in / $25 out per million tokens it sits squarely in the frontier bracket.
The no-premium 1M context is the practical reason to choose it. Models that double their
input rate above 200K tokens make large-context work unpredictable to budget: the same
feature costs a different amount depending on how much a user pasted in. Opus 4.8 bills the
same rate from the first token to the millionth, so a retrieval-heavy application can size
its prompts around what produces the best answer rather than around a pricing cliff. It has
since been joined by Claude Opus 5, which ranks higher on the Artificial Analysis
Intelligence Index at identical pricing — so for new builds, check whether Opus 5 is
available in your region and provider before defaulting to 4.8.
Claude Opus 4.8 pricing: API cost per 1M tokens
| Entrada (por cada millón de tokens) | $5.00 |
|---|---|
| Salida (por cada millón de tokens) | $25.00 |
| Output/input ratio | 5× |
| Blended (4:1 in:out) | $9.00 per 1M tokens |
What Claude Opus 4.8 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 | $11 |
| Pequeño equipo | 20 millones de tokens de entrada / 5 millones de tokens de salida | $225 |
| Producción | 200 millones de tokens de entrada / 50 millones de tokens de salida | $2,250 |
Run your own numbers in the Calculadora de costos de API de IA.
Cheaper alternatives to Claude Opus 4.8
| Modelos | Dólares por millón combinados | You save |
|---|---|---|
| Kimi K3 abierta | $5.40 | 40% cheaper |
| GLM 5.2 abierta | $2.00 | 78% cheaper |
| Gemini 3.5 Flash | $3.00 | un 67 % más barato |
Preguntas frecuentes
How much does Claude Opus 4.8 cost per 1M tokens?
Claude Opus 4.8 costs $5.00 per 1M input tokens and $25.00 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $9.00 per 1M tokens.
How much does Claude Opus 4.8 cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $225 on Claude Opus 4.8. A side project (1M in / 0.25M out) costs roughly $11.25.
What is a cheaper alternative to Claude Opus 4.8?
Kimi K3 is the strongest cheaper option in our database at $5.40 per 1M blended — about 40% less than Claude Opus 4.8. It is also open-weight, so self-hosting is an option.
Can I run Claude Opus 4.8 locally?
No. Claude Opus 4.8 is a closed, API-only model — the weights are not released, so it cannot be self-hosted.
Why does Claude Opus 4.8 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 Opus 4.8 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).
