Overview

Gemma 3 27B delivers exceptional performance for complex reasoning and long-context tasks. With 27 billion parameters and a 128,000-token context window, this FP16 model handles extensive documentation and multi-turn conversations effortlessly. Developers can integrate it immediately via Resayil standard API endpoints, enabling production-ready deployments without complex infrastructure management. Quick start guides allow API builders to complete their first call within minutes, leveraging the 3x credit multiplier for high-performance tier resource allocation on the starter plan.

For researchers and enterprise teams, Gemma 3 27B demonstrates superior accuracy in bilingual benchmarks, specifically optimized for Arabic and English workflows. Comparative data shows significant gains over alternative 20B-class models in linguistic nuance and code generation across diverse datasets. Business decision makers can access transparent pricing denominated in KWD, SAR, and AED, confirming immediate production readiness without sales inquiries. The GEMMA license permits broad commercial use, making it a secure choice for scalable solutions requiring strict language fidelity and robust support for regional language requirements within enterprise pipelines.

Specifications

Display Name Gemma 3 27B

Family Gemma

Category Chat

Parameters 27B

Context Window 128,000 tokens

Quantization FP16

License GEMMA

Min Tier Starter

Status Available

Pricing

3×

credits per token

1K 3,000 Credits

10K 30,000 Credits

100K 300,000 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="gemma3:27b",
    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="gemma3:27b",
    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: "gemma3:27b",
            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": "gemma3:27b",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Use Cases

Summarizing long documents and research papers efficiently

Building interactive customer support chatbot applications

Generating and debugging software code snippets

Analyzing extensive legal contracts for key clauses

Creating engaging creative writing and story drafts

In-Depth Guide

Full Guide

Complete Guide to Gemma 3 27B — LLM Resayil

Gemma 3 27B