Gemma 3 4B — Spezifikationen
Compiled by Mustafa Ihsan from the vendor’s published documentation · Last updated
| Entwickler | |
|---|---|
| Typ | LLM (multimodal) |
| Modality | Text, Bild → Text |
| Parameter | 4 Mrd. |
| Kontextfenster | 128 K |
| Lizenz | Gemma (offen) |
| Offene Gewichte | Ja |
| Veröffentlicht | 2025 |
| Eingabepreis | $0.05 /1M |
| Ausgabepreis | $0.1 /1M |
| API-Anbieter | Google AI Studio, Ollama |
Lokal ausführen
| VRAM (4-Bit) | ca. 3 GB |
|---|---|
| Mindest-GPU | Jede GPU mit mindestens 6 GB VRAM |
What is Gemma 3 4B?
Gemma 3 4B is the compact end of Google’s open Gemma 3 family: multimodal text and image
input, a 128K context, and roughly 3 GB of VRAM at 4-bit — small enough for almost any modern
GPU, and for a good deal of hardware that is not a GPU at all.
What is unusual here is the context window. A 4B model with 128K context is not the normal
trade-off; small models historically shipped with small windows, which limited them to short
prompts and made them useless for document work. Gemma 3 4B can hold a substantial document
in memory on a 6 GB card, which opens up edge and on-device use cases — local document
search, offline assistants, in-browser or in-app inference — that previously required sending
data to a server. Do not expect it to reason like a frontier model; expect it to be the
model that makes a privacy-preserving feature feasible at all. At $0.05 in / $0.10 out per
million tokens the hosted option is close to free, so self-hosting here is about data
residency and latency, not cost.
Gemma 3 4B pricing: API cost per 1M tokens
| Eingabe (pro 1 Mio. Token) | $0.0500 |
|---|---|
| Ausgabe (pro 1 Mio. Token) | $0.100 |
| Verhältnis Output/Input | 2× |
| Gemischt (4:1 Input:Output) | $0.0600 pro 1 Mio. Tokens |
What Gemma 3 4B 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 | $0.08 |
| Kleines Team | 20 Mio. Eingabe / 5 Mio. Ausgabe | $1.50 |
| Produktion | 200 Mio. Eingabe / 50 Mio. Ausgabe | $15 |
Stellen Sie Ihre eigenen Berechnungen im KI-API-Kostenrechner.
Cheaper alternatives to Gemma 3 4B
| Modell | Gewichteter Preis pro Million US-Dollar | Sie sparen |
|---|---|---|
| Mistral 7B offenere | $0.0220 | 63 % günstiger |
| Llama 3.1 8B offenere | $0.0220 | 63 % günstiger |
| Mistral NeMo 12B offenere | $0.0240 | 60% cheaper |
Selbst hosten oder die API nutzen?
Gemma 3 4B is open-weight, so you can run it yourself. It needs ca. 3 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 Selbsthosting vs. API-Rechner berechnet den Break-even-Punkt für Ihr Token-Volumen.
Häufig gestellte Fragen
How much does Gemma 3 4B cost per 1M tokens?
Gemma 3 4B costs $0.0500 per 1M input tokens and $0.100 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $0.0600 per 1M tokens.
How much does Gemma 3 4B cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $1.50 on Gemma 3 4B. A side project (1M in / 0.25M out) costs roughly $0.08.
What is a cheaper alternative to Gemma 3 4B?
Mistral 7B is the strongest cheaper option in our database at $0.0220 per 1M blended — about 63% less than Gemma 3 4B. It is also open-weight, so self-hosting is an option.
Can I run Gemma 3 4B locally?
Yes. Gemma 3 4B is open-weight and needs about ~3 GB of VRAM at 4-bit quantisation (Any 6GB+ GPU).
Why does Gemma 3 4B 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 4B charges 2× 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.