Mistral NeMo 12B — Especificaciones
| Desarrollador | Mistral AI |
|---|---|
| Tipo | LLM (densa) |
| Modalidad | Texto → Texto |
| Parámetros | 12B |
| Ventana de contexto | 128 K |
| Salida máxima | — |
| Licencia | Apache 2.0 (abierta) |
| Pesos abiertos | Sí |
| Lanzado | 2024 |
| Precio de entrada | 0,02 USD / millón |
| Precio de salida | 0,04 $/millón |
| Proveedores de API | Mistral, OpenRouter, Ollama |
Ejecútelo localmente
| VRAM (4 bits) | ~7,5 GB |
|---|---|
| GPU mínima | RTX 4070 de 12 GB / RTX 3060 |
What is Mistral NeMo 12B?
Mistral NeMo 12B was built by Mistral AI in partnership with NVIDIA — Apache 2.0, a 128K
context, strong multilingual support, and about 7.5 GB of VRAM at 4-bit, which fits an RTX
4070 12GB or an RTX 3060.
The NVIDIA collaboration shows up where it matters: the model was designed with quantisation
in mind, so it holds quality at 4-bit better than models retrofitted to small hardware after
the fact — the usual failure mode being a model that technically fits in 12 GB but degrades
noticeably once it does. Combined with genuinely good multilingual coverage, that makes NeMo
12B a strong pick for non-English local deployments, where the alternatives are often
English-first models that lose more in quantisation than they can afford. At $0.02 in / $0.04
out per million tokens it is also among the cheapest hosted options available, so it works as
the low-cost tier of a routing setup as well as a local model.
Mistral NeMo 12B pricing: API cost per 1M tokens
| Entrada (por cada millón de tokens) | $0.0200 |
|---|---|
| Salida (por cada millón de tokens) | $0.0400 |
| Output/input ratio | 2× |
| Blended (4:1 in:out) | $0.0240 per 1M tokens |
What Mistral NeMo 12B 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 | $0.03 |
| Pequeño equipo | 20 millones de tokens de entrada / 5 millones de tokens de salida | $0.60 |
| Producción | 200 millones de tokens de entrada / 50 millones de tokens de salida | $6.00 |
Run your own numbers in the Calculadora de costos de API de IA.
Cheaper alternatives to Mistral NeMo 12B
| Modelos | Dólares por millón combinados | You save |
|---|---|---|
| Mistral 7B abierta | $0.0220 | 8% cheaper |
| Llama 3.1 8B abierta | $0.0220 | 8% cheaper |
Self-host or pay the API?
Mistral NeMo 12B is open-weight, so you can run it yourself. It needs ~7,5 GB of VRAM at 4-bit (RTX 4070 12GB / RTX 3060). Self-hosting only beats the API once your volume is high enough to keep that hardware busy — the calculadora de autohospedaje frente a API works out the break-even point for your token volume.
Preguntas frecuentes
How much does Mistral NeMo 12B cost per 1M tokens?
Mistral NeMo 12B costs $0.0200 per 1M input tokens and $0.0400 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $0.0240 per 1M tokens.
How much does Mistral NeMo 12B cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $0.60 on Mistral NeMo 12B. A side project (1M in / 0.25M out) costs roughly $0.03.
What is a cheaper alternative to Mistral NeMo 12B?
Mistral 7B is the strongest cheaper option in our database at $0.0220 per 1M blended — about 8% less than Mistral NeMo 12B. It is also open-weight, so self-hosting is an option.
Can I run Mistral NeMo 12B locally?
Yes. Mistral NeMo 12B is open-weight and needs about ~7.5 GB of VRAM at 4-bit quantisation (RTX 4070 12GB / RTX 3060).
Why does Mistral NeMo 12B 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 NeMo 12B charges 2× more for output, which is why prompt-heavy workloads are far cheaper to run than generation-heavy ones.
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).
