Mistral 7B — Especificaciones
Compiled by Mustafa Ihsan from the vendor’s published documentation · Last updated
| Desarrollador | Mistral AI |
|---|---|
| Tipo | LLM (densa) |
| Modalidad | Texto → Texto |
| Parámetros | 7B |
| Ventana de contexto | 32K |
| Licencia | Apache 2.0 (abierta) |
| Pesos abiertos | Sí |
| Lanzado | 2023 |
| Precio de entrada | $0.02 /1M |
| Precio de salida | $0.03 /1M |
| Proveedores de API | Mistral, DeepInfra, OpenRouter, Ollama |
Ejecútelo localmente
| VRAM (4 bits) | ~4,5 GB |
|---|---|
| GPU mínima | Cualquier GPU de 6 GB |
What is Mistral 7B?
Mistral 7B is the model that kicked off the open-LLM wave — 7B parameters, Apache 2.0, a
32K context and a footprint of about 4.5 GB at 4-bit that runs on a 6 GB GPU. It is still a
solid, ultra-cheap baseline at $0.02 in / $0.03 out per million tokens.
Its remaining role is as a floor rather than a recommendation. The 32K context is the
binding constraint: every comparable small model now ships with 128K, so anything involving
documents, long conversations or substantial retrieval will hit the wall on Mistral 7B first.
Where it still earns a place is in tightly scoped, high-volume classification and routing
tasks with short inputs, on constrained hardware, or as the historical baseline in an
evaluation — if a newer model cannot beat Mistral 7B on your data, the problem is probably
your prompt or your data, not the model. For new local deployments, Qwen3 8B and Llama 3.1 8B
occupy the same hardware bracket with four times the context, and are the better starting
point.
Mistral 7B pricing: API cost per 1M tokens
| Entrada (por cada millón de tokens) | $0.0200 |
|---|---|
| Salida (por cada millón de tokens) | $0.0300 |
| Relación salida/entrada | 1.5× |
| Combinada (4:1 entrada:salida) | $0.0220 por 1 millón de tokens |
What Mistral 7B 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 | $0.03 |
| Pequeño equipo | 20 millones de tokens de entrada / 5 millones de tokens de salida | $0.55 |
| Producción | 200 millones de tokens de entrada / 50 millones de tokens de salida | $5.50 |
Calcule sus propios números en la Calculadora de costos de API de IA.
¿Autoalojarlo o pagar por la API?
Mistral 7B is open-weight, so you can run it yourself. It needs ~4,5 GB of VRAM at 4-bit (Any 6GB GPU). 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 Mistral 7B cost per 1M tokens?
Mistral 7B costs $0.0200 per 1M input tokens and $0.0300 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $0.0220 per 1M tokens.
How much does Mistral 7B cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $0.55 on Mistral 7B. A side project (1M in / 0.25M out) costs roughly $0.03.
Can I run Mistral 7B locally?
Yes. Mistral 7B is open-weight and needs about ~4.5 GB of VRAM at 4-bit quantisation (Any 6GB GPU).
Why does Mistral 7B 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. Mistral 7B charges 1.5× 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).