Google: Gemma 4 31B
Dense Reasoning Power
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
import requestsresponse = requests.post("https://modelslab.com/api/v7/llm/chat/completions",json={"key": "YOUR_API_KEY","prompt": "","model_id": ""})print(response.json())
Ready to create?
Start generating with Google: Gemma 4 31B on ModelsLab.