Llama 3.3 70B — Especificaciones
Compiled by Mustafa Ihsan from the vendor’s published documentation · Last updated
| Desarrollador | Meta |
|---|---|
| Tipo | LLM (densa) |
| Modalidad | Texto → Texto |
| Parámetros | 70B |
| Ventana de contexto | 128 K |
| Licencia | Llama 3.3 Community (abierta) |
| Pesos abiertos | Sí |
| Lanzado | 2024 |
| Precio de entrada | $0.10 /1M |
| Precio de salida | $0.32 /1M |
| Proveedores de API | Together, DeepInfra, OpenRouter, Ollama |
Ejecútelo localmente
| VRAM (4 bits) | ~40 GB |
|---|---|
| GPU mínima | 2× RTX 4090 / 1× GPU de 48 GB |
What is Llama 3.3 70B?
Llama 3.3 70B is Meta’s efficient dense 70B — close to 405B-class quality at a fraction of
the size, with a 128K context. It needs about 40 GB of VRAM at 4-bit, so two RTX 4090s or a
single 48 GB card, and it is among the most-deployed open models for self-hosting.
Dense is the operative word. Nearly every large open model released since is a
mixture-of-experts, which lowers inference compute but keeps the full parameter count
resident in memory — DeepSeek V4-Pro activates 49B parameters but still needs roughly 800 GB
of VRAM to hold. A dense 70B has no such gap: what you load is what you use, so 40 GB is the
whole story. For teams sizing hardware, that predictability is worth a lot, and it is why
3.3 70B remains the default for on-premises deployments even as newer architectures post
better benchmark numbers. At $0.10 in / $0.32 out per million tokens the API is cheap enough
that self-hosting only makes sense for data-residency reasons or at sustained high
volumen.
Llama 3.3 70B pricing: API cost per 1M tokens
| Entrada (por cada millón de tokens) | $0.100 |
|---|---|
| Salida (por cada millón de tokens) | $0.320 |
| Relación salida/entrada | 3.2× |
| Combinada (4:1 entrada:salida) | $0.144 por 1 millón de tokens |
What Llama 3.3 70B 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 | $0.18 |
| Pequeño equipo | 20 millones de tokens de entrada / 5 millones de tokens de salida | $3.60 |
| Producción | 200 millones de tokens de entrada / 50 millones de tokens de salida | $36 |
Calcule sus propios números en la Calculadora de costos de API de IA.
Cheaper alternatives to Llama 3.3 70B
| Modelo | Dólares por millón combinados | Usted ahorra |
|---|---|---|
| Mistral 7B abierta | $0.0220 | 85% cheaper |
| Llama 3.1 8B abierta | $0.0220 | 85% cheaper |
| Mistral NeMo 12B abierta | $0.0240 | 83% cheaper |
¿Autoalojarlo o pagar por la API?
Llama 3.3 70B is open-weight, so you can run it yourself. It needs ~40 GB of VRAM at 4-bit (2× RTX 4090 / 1× 48GB). 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 Llama 3.3 70B cost per 1M tokens?
Llama 3.3 70B costs $0.100 per 1M input tokens and $0.320 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $0.144 per 1M tokens.
How much does Llama 3.3 70B cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $3.60 on Llama 3.3 70B. A side project (1M in / 0.25M out) costs roughly $0.18.
What is a cheaper alternative to Llama 3.3 70B?
Mistral 7B is the strongest cheaper option in our database at $0.0220 per 1M blended — about 85% less than Llama 3.3 70B. It is also open-weight, so self-hosting is an option.
Can I run Llama 3.3 70B locally?
Yes. Llama 3.3 70B is open-weight and needs about ~40 GB of VRAM at 4-bit quantisation (2× RTX 4090 / 1× 48GB).
Why does Llama 3.3 70B 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. Llama 3.3 70B charges 3.2× more for output, which is why prompt-heavy workloads are far cheaper to run than generation-heavy ones.
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).