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Gemini 3.1 Flash Live Preview

Google (Gemini)

At a glance

Price per 1M tokens (input)
$0.75
Price per 1M tokens (output)
$4.50
Context
131,072 tokens
Free access
yes (see below)

Refreshed daily; data verified August 15, 2026. Published August 12, 2026.

The Gemini model is designed for businesses and organizations that require advanced image understanding and automation capabilities. This model is well-suited for tasks such as image analysis, object detection, and automated workflow processing. What sets the vendor's approach apart is the integration of tool calling, which allows the model to call external functions and leverage their capabilities, enabling more complex and dynamic processing. This feature enables users to create customized workflows and automate tasks more efficiently. The model's capabilities make it a valuable asset for companies looking to streamline their operations and improve productivity.

Specifications & pricing

Input (per 1M tokens)$0.75
Output (per 1M tokens)$4.50
Context window131,072 tokens
Max output65,536 tokens
Capabilitiesimages, tool calling

LiteLLM community dataset (MIT), verified August 15, 2026. Official Google (Gemini) pricing.

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Frequently asked questions

What is the Gemini model good for+

The Gemini model is good for tasks that require advanced image understanding and automation, such as image analysis, object detection, and automated workflow processing

How do I get started with the Gemini model+

To get started with the Gemini model, users can begin by reviewing the model's documentation and guidelines, which provide information on how to integrate the model into their existing workflows and systems

How does the Gemini model differ from other models in the line+

The Gemini model differs from other models in the line due to its advanced image understanding capabilities and tool calling feature, which allows for more complex and dynamic processing

What are the limitations of the Gemini model+

The limitations of the Gemini model include its reliance on high-quality input data and its potential for biased results if the training data is not diverse or representative

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