Overview

Gemma 3 4B delivers exceptional efficiency for high-throughput applications requiring low latency. Built on the latest Gemma family architecture, this model features a massive 128,000 token context window, enabling comprehensive document analysis and long-form conversation retention without performance degradation. Developers can integrate this model immediately via our standardized API endpoints, ensuring seamless deployment into existing pipelines. The FP16 quantization balances precision and speed, making it ideal for real-time inference tasks where response time is critical. Whether you are building chatbots or data extraction tools, the starter tier access allows you to validate performance with minimal upfront commitment.

For researchers and enterprise teams, Gemma 3 4B offers robust bilingual proficiency, excelling in both English and Arabic language tasks. Benchmark data indicates competitive performance against larger models in reasoning and code generation, providing a cost-effective solution for production environments. Our credit system applies a 1.5x multiplier, ensuring transparent pricing aligned with usage volume. This model is production-ready, supporting complex instruction following and nuanced cultural contexts essential for regional applications. By choosing Gemma 3 4B on our platform, you gain access to a reliable infrastructure designed for scalability, allowing you to focus on innovation rather than management. Teams can estimate costs accurately using the credit multiplier before scaling operations.

Specifications

Display Name Gemma 3 4B

Family Gemma

Category Chat

Parameters 4B

Context Window 128,000 tokens

Quantization FP16

License GEMMA

Min Tier Starter

Status Available

Pricing

1.5×

credits per token

1K 1,500 Credits

10K 15,000 Credits

100K 150,000 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="gemma3:4b",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

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

import anthropic

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

message = client.messages.create(
    model="gemma3:4b",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(message.content[0].text)

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: "gemma3:4b",
            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": "gemma3:4b",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Use Cases

Summarizing extensive documents using large context window

Customer support automation for handling user inquiries

Reviewing long code files for potential bugs

Extracting data from lengthy technical manuals quickly

Personalized learning assistant for answering student questions

In-Depth Guide

Full Guide

Complete Guide to Gemma 3 4B — LLM Resayil

Gemma 3 4B