Gemma 3 4B — Especificaciones
| Desarrollador | |
|---|---|
| Tipo | LLM (multimodal) |
| Modalidad | Texto, imagen → texto |
| Parámetros | 4 mil millones |
| Ventana de contexto | 128 K |
| Salida máxima | — |
| Licencia | Gemma (abierta) |
| Pesos abiertos | Sí |
| Lanzado | 2025 |
| Precio de entrada | 0,05 USD por millón |
| Precio de salida | 0,10 USD / millón |
| Proveedores de API | Google AI Studio, Ollama |
Ejecútelo localmente
| VRAM (4 bits) | ~3 GB |
|---|---|
| GPU mínima | Cualquier GPU de 6 GB o más |
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
| Entrada (por cada millón de tokens) | $0.0500 |
|---|---|
| Salida (por cada millón de tokens) | $0.100 |
| Output/input ratio | 2× |
| Blended (4:1 in:out) | $0.0600 per 1M tokens |
What Gemma 3 4B 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 | $0.08 |
| Pequeño equipo | 20 millones de tokens de entrada / 5 millones de tokens de salida | $1.50 |
| Producción | 200 millones de tokens de entrada / 50 millones de tokens de salida | $15 |
Run your own numbers in the Calculadora de costos de API de IA.
Cheaper alternatives to Gemma 3 4B
| Modelos | Dólares por millón combinados | You save |
|---|---|---|
| Mistral 7B abierta | $0.0220 | 63% cheaper |
| Llama 3.1 8B abierta | $0.0220 | 63% cheaper |
| Mistral NeMo 12B abierta | $0.0240 | 60% cheaper |
Self-host or pay the API?
Gemma 3 4B is open-weight, so you can run it yourself. It needs ~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 calculadora de autohospedaje frente a API works out the break-even point for your token volume.
Preguntas frecuentes
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.
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).
