Gemma 3 12B
VerifiedGoogle's open multimodal model for text and image understanding.
About Gemma 3 12B
Gemma 3 12B uses a transformer-based architecture that integrates vision encoders with language modeling layers. This design allows the model to process image and text data jointly within a single forward pass. The open-weight release gives researchers direct access to model parameters for inspection and modification.
Its strengths include native multimodal support and a large context window that accommodates lengthy documents paired with images. Because the weights are openly available, the model can be fine-tuned or quantized for deployment on consumer or enterprise hardware. Google provides it as part of the Gemma family to encourage experimentation and local inference.
Typical usage covers visual question answering, image captioning, and multimodal chat interfaces. Developers also apply it to document analysis tasks where both textual content and visual layout must be understood together. The 12B scale offers a balance between capability and the ability to run on mid-range GPUs or CPUs.
Capabilities
How Gemma 3 12B compares
Gemma 3 12B (striped bar) vs other multimodal on intelligence, speed and price.
Price
USD per 1M output tokens · Lower is better · Gemma 3 12B ranks #6 of 124
Sources: Artificial Analysis (intelligence, speed) · OpenRouter (price).
Best for
Long-context multimodal document analysis
Processes 128k-token inputs combining text and images, such as full research papers with embedded figures and charts.
Extended visual reasoning tasks
Handles sequences of images alongside lengthy instructions for tasks like storyboarding or technical diagram interpretation.
Large-scale code and UI review
Reviews entire repositories or multi-screen app designs by ingesting both code files and screenshots in one context window.
Strengths & limitations
Strengths
- +Efficient 12B scale for deployment
- +Strong context window utilization
- +Native text and image support
- +Open-weight accessibility
Limitations
- –Smaller scale than frontier models
- –Multimodal depth constrained by size
- –Performance varies on complex tasks
Cost calculator
Estimate what Gemma 3 12B would cost for your usage.
Based on Gemma 3 12B's $0.05/1M input · $0.15/1M output. Estimate only — actual cost varies by provider and caching.
Download & self-host Gemma 3 12B
This is an open-weight model. Download the weights from Hugging Face or load it directly with Transformers.
# Install the Hugging Face CLI
pip install -U "huggingface_hub[cli]"
# Download the model weights
hf download google/gemma-3-12b-it
# Or load it directly in Python
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("google/gemma-3-12b-it")
model = AutoModelForCausalLM.from_pretrained("google/gemma-3-12b-it", device_map="auto")Inference providers
Hosted APIs that serve Gemma 3 12B (via Hugging Face Inference Providers).
Quick start
OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. Only the model slug changes between models.
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://openrouter.ai/api/v1",
apiKey: process.env.OPENROUTER_API_KEY,
});
const completion = await client.chat.completions.create({
model: "google/gemma-3-12b-it",
messages: [{ role: "user", content: "Hello!" }],
});
console.log(completion.choices[0].message.content);Model slug: google/gemma-3-12b-it
Editor's verdict
Gemma 3 12B is Google's open-weight multimodal with a 131K-token context window.
At $0.15 per 1M output tokens, it is very cost-efficient for its class.
As an open-weight model you can self-host it (12B parameters) or call it through a hosted API.
Best suited to efficient 12b scale for deployment and strong context window utilization.
Frequently asked questions
The model supports a context length of 131072 tokens.
User reviews
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