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Available now on ModelsLab · Language Model

Gemma 4 31B-it FP8Reasoning. Multimodal. Efficient.

Try Gemma 4 31B-it FP8API Documentation

Dense Model. Frontier Performance.

256K Context

Long-Context Reasoning

Process 256K tokens with hybrid attention for deep reasoning across large inputs.

Multimodal Native

Text and Image Input

Handle variable aspect ratios and resolutions with integrated vision encoder support.

Function Calling

Agentic Workflows

Native function calling and structured JSON output for autonomous task execution.

Examples

See what Gemma 4 31B-it FP8 can create

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

Code Generation

“Write a Python function that implements binary search with detailed comments explaining the algorithm and edge cases.”

Document Analysis

“Analyze this technical whitepaper image and extract the key findings, methodology, and conclusions in structured format.”

Multi-step Reasoning

“Solve this complex math problem step-by-step, showing all work and explaining the reasoning behind each calculation.”

Multilingual Support

“Translate this technical documentation from English to Spanish, French, and Mandarin while preserving formatting.”

For Developers

A few lines of code.
Reasoning. Three lines.

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 Gemma 4 31B-it FP8

Read the docs

Gemma 4 31B-it FP8 is Google DeepMind's 31-billion parameter multimodal model with FP8 quantization for efficient deployment. It supports text and image inputs, handles 256K token context, and delivers frontier-level performance on reasoning, coding, and multimodal tasks across 140+ languages.

Pricing is $0.20 per million input tokens and $0.50 per million output tokens. FP8 quantization reduces memory requirements while maintaining performance, making it cost-effective for production workloads.

Gemma 4 31B-it achieves 89.2% on AIME (reasoning), 80.0% on LiveCodeBench v6 (coding), 84.3% on GPQA Diamond (science), and 76.9% on MMMU Pro (multimodal). These benchmarks demonstrate frontier-level capability across diverse tasks.

Yes. Released April 2, 2026 under Apache 2.0 license, it's production-ready with native function calling, system prompt support, and configurable thinking mode for step-by-step reasoning in agentic workflows.

Deploy via serverless API endpoints, self-hosted on GPU infrastructure using vLLM, or through multiple cloud providers. FP8 quantization enables efficient deployment on consumer GPUs and workstations.

Yes. The model processes video as sequences of frames up to 60 seconds at one frame per second, with configurable visual token budgets for flexible multimodal workflows.

Ready to create?

Start generating with Gemma 4 31B-it FP8 on ModelsLab.

Try Gemma 4 31B-it FP8API Documentation