DeepSeek R1 — Especificaciones
Compiled by Mustafa Ihsan from the vendor’s published documentation · Last updated
| Desarrollador | DeepSeek |
|---|---|
| Tipo | LLM (MoE, razonamiento) |
| Modalidad | Texto → Texto |
| Parámetros | 671 mil millones en total / 37 mil millones activos (MoE) |
| Ventana de contexto | 128 K |
| Licencia | MIT (abierto) |
| Pesos abiertos | Sí |
| Lanzado | 2025 |
| Precio de entrada | $0.50 /1M |
| Precio de salida | $2.15 /1M |
| Proveedores de API | DeepSeek, DeepInfra, OpenRouter |
Ejecútelo localmente
| VRAM (4 bits) | ~400 GB |
|---|---|
| GPU mínima | Servidor multi-GPU |
What is DeepSeek R1?
DeepSeek R1 is the landmark open reasoning model — a 671B mixture-of-experts activating
37B parameters per token, MIT-licensed, with a 128K context window. It delivered
frontier-class chain-of-thought reasoning at a fraction of proprietary cost and is the model
that made open reasoning weights a mainstream option rather than a research curiosity.
Two things follow from the architecture. First, the API is genuinely cheap at $0.50 in /
$2.15 out per million tokens, because only 37B of the 671B parameters are active on any given
token. Second, self-hosting is not cheap at all: the full weights need roughly 400 GB of VRAM
even at 4-bit quantisation, which means a multi-GPU server, not a workstation. The MIT
licence gives you the right to run it, but the hardware bill is what decides whether you
will. For most teams the correct reading is that R1 is an API model that happens to be
auditable and portable — you can move providers, inspect the weights, and never be locked in,
without personally operating an eight-GPU node. If you want R1-style reasoning on hardware
you own, the 70B distill is the model to look at instead.
DeepSeek R1 pricing: API cost per 1M tokens
| Entrada (por cada millón de tokens) | $0.500 |
|---|---|
| Salida (por cada millón de tokens) | $2.15 |
| Relación salida/entrada | 4.3× |
| Combinada (4:1 entrada:salida) | $0.830 por 1 millón de tokens |
What DeepSeek R1 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 | $1.04 |
| Pequeño equipo | 20 millones de tokens de entrada / 5 millones de tokens de salida | $21 |
| Producción | 200 millones de tokens de entrada / 50 millones de tokens de salida | $208 |
Calcule sus propios números en la Calculadora de costos de API de IA.
Cheaper alternatives to DeepSeek R1
| Modelo | Dólares por millón combinados | Usted ahorra |
|---|---|---|
| DeepSeek V4-Pro abierta | $0.522 | 37% cheaper |
| DeepSeek V4-Flash abierta | $0.168 | 80% cheaper |
| Llama 4 Maverick abierta | $0.320 | 61% cheaper |
¿Autoalojarlo o pagar por la API?
DeepSeek R1 is open-weight, so you can run it yourself. It needs ~400 GB of VRAM at 4-bit (Multi-GPU server). Self-hosting only beats the API once your volume is high enough to keep that hardware busy — the calculadora de autohospedaje frente a API calcula el punto de equilibrio para su volumen de tokens.
Preguntas frecuentes
How much does DeepSeek R1 cost per 1M tokens?
DeepSeek R1 costs $0.500 per 1M input tokens and $2.15 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $0.830 per 1M tokens.
How much does DeepSeek R1 cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $21 on DeepSeek R1. A side project (1M in / 0.25M out) costs roughly $1.04.
What is a cheaper alternative to DeepSeek R1?
DeepSeek V4-Pro is the strongest cheaper option in our database at $0.522 per 1M blended — about 37% less than DeepSeek R1. It is also open-weight, so self-hosting is an option.
Can I run DeepSeek R1 locally?
Yes. DeepSeek R1 is open-weight and needs about ~400 GB of VRAM at 4-bit quantisation (Multi-GPU server).
Why does DeepSeek R1 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. DeepSeek R1 charges 4.3× more for output, which is why prompt-heavy workloads are far cheaper to run than generation-heavy ones.
See every DeepSeek model priced side by side: Precios de la API de DeepSeek.
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).