Overview

Gemma 3 12B delivers exceptional performance for its size, featuring a massive 128,000 token context window ideal for complex document analysis and long-form generation. Built on the latest Gemma family architecture, this model balances efficiency with high-quality reasoning capabilities in FP16 precision. Developers can integrate this model immediately using our standardized API endpoints, enabling you to complete your first successful API call within minutes of reading this guide. The 12B parameter count offers a sweet spot for production workloads requiring low latency without sacrificing intelligence, making it a robust choice for building scalable chat applications and automated reasoning pipelines.

For enterprise deployments, Gemma 3 12B is fully production-ready with native support for both Arabic and English tasks, ensuring seamless language adaptation for regional audiences. Our pricing structure applies a 2.5x credit multiplier relative to the base rate, providing transparent cost predictability for high-volume usage without hidden fees. This model fits the starter tier, allowing teams to validate proofs of concept before scaling. Whether you are comparing benchmark performance against alternative architectures or calculating operational expenses, Gemma 3 12B provides the reliability and linguistic accuracy required for serious commercial applications.

Specifications

Display Name Gemma 3 12B

Family Gemma

Category Vision

Parameters 12B

Context Window 128,000 tokens

Quantization FP16

License GEMMA

Min Tier Starter

Status Available

Pricing

2.5×

credits per token

1K 2,500 Credits

10K 25,000 Credits

100K 250,000 Credits

View Pricing Plans

Code Examples

from openai import OpenAI

client = OpenAI(
    base_url="https://llmapi.resayil.io/v1/",
    api_key="YOUR_API_KEY"
)

response = client.chat.completions.create(
    model="gemma3:12b",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(response.choices[0].message.content)

const response = await fetch(
    "https://llmapi.resayil.io/v1/chat/completions",
    {
        method: "POST",
        headers: {
            "Content-Type": "application/json",
            "Authorization": "Bearer YOUR_API_KEY"
        },
        body: JSON.stringify({
            model: "gemma3:12b",
            messages: [
                { role: "user", content: "Hello!" }
            ]
        })
    }
);

const data = await response.json();
console.log(data.choices[0].message.content);

curl https://llmapi.resayil.io/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "gemma3:12b",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Use Cases

Processing extensive context windows for detailed document analysis

Building interactive customer support chatbot applications

Generating and debugging software code snippets efficiently

Analyzing legal contracts for specific clause extraction

Facilitating natural dialogue flows for virtual assistant systems

In-Depth Guide

Full Guide

Complete Guide to Gemma 3 12B — LLM Resayil

Gemma 3 12B