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Available now on ModelsLab · Language Model

Google: Gemma 4 31BDense Reasoning Power

Try Google: Gemma 4 31BAPI Documentation

Deploy Gemma 4 31B Now

Dense Architecture

31B Parameter Core

Bridges server performance and local execution with 58GB BF16 size.

Agentic Workflows

Multi-Step Planning

Handles complex logic, function calling, and autonomous agents via Google: Gemma 4 31B API.

Multimodal Input

Text Vision Audio

Processes images and audio alongside text in Google: Gemma 4 31B model.

Examples

See what Google: Gemma 4 31B can create

Copy any prompt below and try it yourself in the playground.

Code Agent

“You are a coding agent. Analyze this Python function for bugs, suggest fixes, and generate unit tests. Function: def factorial(n): if n == 0: return 1 else: return n * factorial(n+1)”

Logic Puzzle

“Solve this riddle step-by-step: A bat and ball cost $1.10 total. Bat costs $1 more than ball. How much is the ball? Explain reasoning chain.”

Tech Summary

“Summarize key differences between dense and MoE architectures in LLMs like Gemma 4, with examples from 31B and 26B variants.”

Workflow Plan

“Plan a multi-step agentic workflow to research, outline, and draft a technical blog post on quantization techniques for Gemma 4 31B.”

For Developers

A few lines of code.
Inference. One Call.

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

  • Serverless: scales to zero, scales to millions
  • Pay per token, no minimums
  • Python and JavaScript SDKs, plus REST API
API Documentation 
import requests


response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

FAQ

Common questions about Google: Gemma 4 31B

Read the docs

Dense 31B parameter model from Google DeepMind. Ranks #3 on Arena AI leaderboard. Supports 256K context and multimodal inputs.

Use ModelsLab LLM endpoint for inference. Deploy via serverless GPUs. Handles BF16, SFP8, or Q4_0 quantization.

Processes text, images, and audio. Designed for vision and real-time edge tasks. Generates text outputs.

reasoning per parameter. Agentic capabilities without fine-tuning. Apache 2.0 license for commercial use.

Supports 256K tokens for medium models. Enables long agentic workflows. Dynamic handling on CPUs and GPUs.

31B optimizes output quality in dense setup. 26B MoE prioritizes speed with 3.8B active params. Both excel in coding and reasoning.

Ready to create?

Start generating with Google: Gemma 4 31B on ModelsLab.

Try Google: Gemma 4 31BAPI Documentation