Overview

Gemma 4 31B delivers exceptional performance for complex reasoning and long-context tasks within the Gemma family. Engineered with 31 billion parameters and a massive 128,000-token context window, this model excels at maintaining coherence across extensive documents and multi-turn conversations. Developers can integrate this FP16 quantized model immediately via our standard API endpoints, ensuring seamless compatibility with existing pipelines without requiring extensive configuration. The architecture balances efficiency and power, making it an ideal choice for building sophisticated chat applications or analyzing large datasets where precision matters.

Operating under the GEMMA license, this model is production-ready for enterprise deployments requiring robust compliance and scalability. Access starts at the starter tier with a 3.5x credit multiplier, offering a cost-effective balance between advanced capability and operational expenditure. While optimized for English tasks, it demonstrates strong multilingual proficiency including Arabic, supporting diverse linguistic requirements for global applications. Researchers will find the 31B parameter count provides a competitive edge in benchmark scenarios, particularly when compared to similar mid-range models. Choose Gemma 4 31B on LLM Resayil to leverage high-fidelity outputs and reliable uptime for your critical workloads.

Specifications

Display Name Gemma 4 31B

Family Gemma

Category Chat

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)

import anthropic

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

message = client.messages.create(
    model="gemma4:31b",
    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: "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

Summarizing long documents and research papers efficiently

Building customer support chatbots with long context memory

Analyzing legal contracts for key clauses and risks

Generating code snippets and debugging assistance for developers

Extracting structured data from extensive text corpora accurately

In-Depth Guide

Full Guide

Complete Guide to Gemma 4 31B — LLM Resayil

Gemma 4 31B