Gemma 4 31b It
Google (Gemini)
At a glance
- Price per 1M tokens (input)
- free
- Price per 1M tokens (output)
- free
- Context
- 262,144 tokens
- Free access
- yes (see below)
Refreshed daily; data verified August 31, 2026. Published August 31, 2026.
Gemma 4 31b It is aimed at enterprises that need deep multimodal insight combined with programmable logic. It excels at interpreting visual data, orchestrating external APIs through tool calling, and delivering transparent step‑by‑step reasoning for complex decision flows. Google’s approach integrates a unified architecture that treats images and text as first‑class inputs while exposing a structured function interface, allowing developers to embed the model directly into workflow automation. The model is well suited for tasks such as document analysis, visual quality inspection, and intelligent assistant services that require both perception and controlled execution.
Specifications & pricing
| Input (per 1M tokens) | free |
|---|---|
| Output (per 1M tokens) | free |
| Context window | 262,144 tokens |
| Max output | 32,768 tokens |
| Capabilities | images, tool calling, reasoning |
LiteLLM community dataset (MIT), verified August 31, 2026. Official Google (Gemini) pricing.
What Gemma 4 31b It would cost on your workload — run it through the cost calculator →
Where to try Gemma 4 31b It for free
- Google (Gemini) offers a free chat — Gemini (free plan). A vendor's free chat may run a different model from the same family — the exact model is not guaranteed.
- 🎁 On our promo-codes page: Gemini for free: access and discounts.
Frequently asked questions
Which business problems are a natural fit for Gemma 4 31b It?+
Any scenario that blends visual understanding with actionable outcomes benefits, for example automated invoice processing, product defect detection, and conversational agents that need to retrieve or modify external data as part of a response.
How do we begin integrating this model into our existing applications?+
Start by obtaining access credentials from the provider, then use the official client library to send text or image payloads. Define function schemas that describe the external actions you want the model to invoke, and handle the returned function calls in your application logic.
What sets this model apart from other Gemma offerings?+
Unlike earlier variants, this version adds native image comprehension and a robust tool‑calling mechanism, while preserving the same reasoning engine that can articulate its thought process step by step.
What limitations should we be aware of before deployment?+
The model may produce confident‑sounding but incorrect statements, especially when asked about topics outside its training data. Image inputs are limited to moderate resolution, and latency can increase when complex function chains are involved.
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