Mistral NeMo 12B — Spécifications
| Développeur | Mistral AI |
|---|---|
| Type | LLM (dense) |
| Modalité | Texte → Texte |
| Paramètres | 12B |
| Fenêtre de contexte | 128 K |
| Sortie maximale | — |
| Licence | Apache 2.0 (ouverte) |
| Poids ouverts | Oui |
| Publié | 2024 |
| Prix de l’entrée | 0,02 $ / 1 million |
| Prix de la sortie | 0,04 $ / 1 million |
| Fournisseurs d'API | Mistral, OpenRouter, Ollama |
Exécutez-le localement
| VRAM (4 bits) | ~7,5 Go |
|---|---|
| GPU minimal requis | RTX 4070 12 Go / 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
| Entrée (par million de jetons) | $0.0200 |
|---|---|
| Sortie (par million de jetons) | $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.
| Charge de travail | Jetons/mois | Cost / month |
|---|---|---|
| Projet secondaire | 1 million en entrée / 0,25 million en sortie | $0.03 |
| Petite équipe | 20 millions en entrée / 5 millions en sortie | $0.60 |
| Production | 200 millions en entrée / 50 millions en sortie | $6.00 |
Run your own numbers in the Calculateur de coûts des API IA.
Cheaper alternatives to Mistral NeMo 12B
| Modèle | Coût combiné par million de dollars | You save |
|---|---|---|
| Mistral 7B ouverte | $0.0220 | 8% cheaper |
| Llama 3.1 8B ouverte | $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 Go 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 calculateur auto-hébergement vs API works out the break-even point for your token volume.
Questions fréquemment posées
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 données des modèles d'IA ou le Classement des grands modèles linguistiques (LLM).
