Gemma 3 12B — Spezifikationen
Compiled by Mustafa Ihsan from the vendor’s published documentation · Last updated
| Entwickler | |
|---|---|
| Typ | LLM (multimodal) |
| Modality | Text, Bild → Text |
| Parameter | 12B |
| Kontextfenster | 128 K |
| Lizenz | Gemma (offen) |
| Offene Gewichte | Ja |
| Veröffentlicht | 2025 |
| Eingabepreis | $0.05 /1M |
| Ausgabepreis | $0.15 /1M |
| API-Anbieter | Google AI Studio, OpenRouter, Ollama |
Lokal ausführen
| VRAM (4-Bit) | ~8 GB |
|---|---|
| Mindest-GPU | RTX 4070 12 GB |
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
VRAM-Rechner before committing to a
card.
Gemma 3 12B pricing: API cost per 1M tokens
| Eingabe (pro 1 Mio. Token) | $0.0500 |
|---|---|
| Ausgabe (pro 1 Mio. Token) | $0.150 |
| Verhältnis Output/Input | 3× |
| Gemischt (4:1 Input:Output) | $0.0700 pro 1 Mio. Tokens |
What Gemma 3 12B costs per month
Tatsächliche monatliche Ausgaben bei einem 4:1-Input-zu-Output-Mix – dem Verhältnis, das typische Chat- oder RAG-Arbeitslasten tatsächlich erzeugen.
| Workload | Tokens/Monat | Kosten pro Monat |
|---|---|---|
| Nebenprojekt | 1 Mio. Eingabe / 0,25 Mio. Ausgabe | $0.09 |
| Kleines Team | 20 Mio. Eingabe / 5 Mio. Ausgabe | $1.75 |
| Produktion | 200 Mio. Eingabe / 50 Mio. Ausgabe | $18 |
Stellen Sie Ihre eigenen Berechnungen im KI-API-Kostenrechner.
Cheaper alternatives to Gemma 3 12B
| Modell | Gewichteter Preis pro Million US-Dollar | Sie sparen |
|---|---|---|
| Mistral 7B offenere | $0.0220 | 69% cheaper |
| Llama 3.1 8B offenere | $0.0220 | 69% cheaper |
| Mistral NeMo 12B offenere | $0.0240 | 66% cheaper |
Selbst hosten oder die API nutzen?
Gemma 3 12B is open-weight, so you can run it yourself. It needs ~8 GB 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 Selbsthosting vs. API-Rechner berechnet den Break-even-Punkt für Ihr Token-Volumen.
Häufig gestellte Fragen
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.
Die Preise entsprechen den offiziell veröffentlichten Listenpreisen für die primäre API des jeweiligen Modells und werden regelmäßig aktualisiert, sobald Anbieter diese ändern. Volumen-, Batch- und Cached-Input-Rabatte sind nicht enthalten. Vergleichen Sie alle Modelle nebeneinander im Datenbank für KI-Modelle oder das LLM-Leaderboard.