Gemma 3 12B — Spécifications
| Développeur | |
|---|---|
| Type | LLM (multimodale) |
| Modalité | Texte, image → texte |
| Paramètres | 12B |
| Fenêtre de contexte | 128 K |
| Sortie maximale | — |
| Licence | Gemma (ouverte) |
| Poids ouverts | Oui |
| Publié | 2025 |
| Prix de l’entrée | 0,05 $ / 1 million |
| Prix de la sortie | 0,15 $ / 1 million |
| Fournisseurs d'API | Google AI Studio, OpenRouter, Ollama |
Exécutez-le localement
| VRAM (4 bits) | ~8 Go |
|---|---|
| GPU minimal requis | RTX 4070 12 Go |
What is Gemma 3 12B?
Gemma 3 12B is the mid-size member of Google’s open Gemma 3 family: multimodal across text
and images, a 128K context, support for 140+ languages, and a footprint of about 8 GB at
4-bit — which puts it comfortably on an RTX 4070 12GB.
It occupies the position most people actually want from a local model. The 4B is small
enough to feel limited on anything demanding; the 27B needs a 16–24 GB card and pushes past
what a typical developer machine or mid-range gaming GPU offers. The 12B fits the hardware
most people already own while still handling multilingual work and image input properly. That
combination — vision, 140+ languages, 128K context, 8 GB — is the reason it turns up so often
in on-device and privacy-sensitive deployments. If you are choosing a first local model to
build against, this is the one that will run on the widest range of machines without feeling
like a compromise. Size your specific quantisation with the
Calculateur de VRAM before committing to a
card.
Gemma 3 12B pricing: API cost per 1M tokens
| Entrée (par million de jetons) | $0.0500 |
|---|---|
| Sortie (par million de jetons) | $0.150 |
| Output/input ratio | 3× |
| Blended (4:1 in:out) | $0.0700 per 1M tokens |
What Gemma 3 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 | $0.09 |
| Petite équipe | 20 millions en entrée / 5 millions en sortie | $1.75 |
| Production | 200 millions en entrée / 50 millions en sortie | $18 |
Run your own numbers in the Calculateur de coûts des API IA.
Cheaper alternatives to Gemma 3 12B
| Modèle | Coût combiné par million de dollars | You save |
|---|---|---|
| Mistral 7B ouverte | $0.0220 | 69% cheaper |
| Llama 3.1 8B ouverte | $0.0220 | 69% cheaper |
| Mistral NeMo 12B ouverte | $0.0240 | 66% cheaper |
Self-host or pay the API?
Gemma 3 12B is open-weight, so you can run it yourself. It needs ~8 Go of VRAM at 4-bit (RTX 4070 12GB). 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 Gemma 3 12B cost per 1M tokens?
Gemma 3 12B costs $0.0500 per 1M input tokens and $0.150 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $0.0700 per 1M tokens.
How much does Gemma 3 12B cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $1.75 on Gemma 3 12B. A side project (1M in / 0.25M out) costs roughly $0.09.
What is a cheaper alternative to Gemma 3 12B?
Mistral 7B is the strongest cheaper option in our database at $0.0220 per 1M blended — about 69% less than Gemma 3 12B. It is also open-weight, so self-hosting is an option.
Can I run Gemma 3 12B locally?
Yes. Gemma 3 12B is open-weight and needs about ~8 GB of VRAM at 4-bit quantisation (RTX 4070 12GB).
Why does Gemma 3 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. Gemma 3 12B charges 3× 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).
