---
title: Gemma 4 31B — Advanced Reasoning LLM | ModelsLab
description: Run Google Gemma 4 31B model via API for agentic workflows and multimodal reasoning. Try dense 31B inference on optimized endpoints now.
url: https://modelslab-frontend-v2-927501783998.us-east4.run.app/google-gemma-4-31b
canonical: https://modelslab-frontend-v2-927501783998.us-east4.run.app/google-gemma-4-31b
type: website
component: Seo/ModelPage
generated_at: 2026-05-13T09:42:46.268834Z
---

Available now on ModelsLab · Language Model

Google: Gemma 4 31B
Dense Reasoning Power
---

[Try Google: Gemma 4 31B](/models/open_router/google-gemma-4-31b-it) [API Documentation](https://docs.modelslab.com)

Deploy Gemma 4 31B Now
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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](/models/open_router/google-gemma-4-31b-it).

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.
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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 ](https://docs.modelslab.com)

PythonJavaScriptcURL

Copy

```
<code>import requests

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

FAQ

Common questions about Google: Gemma 4 31B
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[Read the docs ](https://docs.modelslab.com)

### What is Google: Gemma 4 31B?

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

### How to access Google: Gemma 4 31B API?

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

### Is Google: Gemma 4 31B model multimodal?

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

### What makes Google: Gemma 4 31B alternative stand out?

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

### Google Gemma 4 31B LLM context length?

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

### Google: Gemma 4 31B vs 26B MoE?

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

[Try Google: Gemma 4 31B](/models/open_router/google-gemma-4-31b-it) [API Documentation](https://docs.modelslab.com)

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*This markdown version is optimized for AI agents and LLMs.*

**Links:**
- [Website](https://modelslab.com)
- [API Documentation](https://docs.modelslab.com)
- [Blog](https://modelslab.com/blog)

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*Generated by ModelsLab - 2026-05-13*