Overview

Gemma 4 31B delivers high-performance inference for complex workflows requiring deep contextual understanding. With 31 billion parameters and a massive 128,000 token context window, this vision-enabled model handles extensive documentation analysis and multimodal tasks efficiently. Developers can integrate immediately via the LLM Resayil API endpoints, ensuring your first successful call happens within minutes of reviewing the documentation. The FP16 quantization balances precision and speed, making it ideal for production environments requiring robust reasoning without excessive latency. This architecture supports seamless integration into existing stacks, allowing engineering teams to prototype and deploy sophisticated AI features rapidly.

For researchers, this model offers competitive performance on multilingual benchmarks, including strong Arabic and English proficiency alongside visual reasoning capabilities. It fits seamlessly into pipelines requiring nuanced language understanding alongside visual data processing. Business leaders will appreciate the transparent credit multiplier of 3.5x relative to base rates, providing predictable costing for scalable deployments without hidden fees. Production readiness is confirmed through rigorous testing, ensuring reliability for enterprise applications handling sensitive data. Access requires only a starter tier account, removing barriers to entry for teams validating new AI-driven products. Visit our model documentation to view detailed performance metrics and start building today.

Specifications

Display Name Gemma 4 31B

Family Gemma

Category Vision

Parameters 31B

Context Window 128,000 tokens

Quantization FP16

License GEMMA

Min Tier Starter

Status Available

Pricing

3.5×

credits per token

1K 3,500 Credits

10K 35,000 Credits

100K 350,000 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="gemma4:31b",
    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: "gemma4:31b",
            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": "gemma4:31b",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Use Cases

Analyzing complex charts and graphs from uploaded images directly

Extracting text from scanned documents and screenshots with high accuracy

Summarizing long reports containing embedded images and text content

Generating functional code based on provided user interface screenshots

Identifying objects within uploaded photography images for auto tagging

In-Depth Guide

Full Guide

Complete Guide to Gemma 4 31B — LLM Resayil

Gemma 4 31B