Gemini 2.5 Flash Preview 09.2025
Google (Gemini)
At a glance
- Price per 1M tokens (input)
- $0.30
- Price per 1M tokens (output)
- $2.50
- Context
- 1,048,576 tokens
- Free access
- yes (see below)
Refreshed daily; data verified August 15, 2026. Published August 12, 2026.
The Gemini model is designed for users who need to analyze and understand visual data, as well as perform complex tasks that require sequential reasoning. This model suits tasks such as image classification, object detection, and image segmentation, where the ability to reason step-by-step is crucial. The vendor's approach to model development focuses on creating a robust and flexible architecture that can be applied to a wide range of applications. One key feature that distinguishes this model is its ability to perform tool calling, which allows it to leverage external functions and services to augment its capabilities. This enables the model to tackle complex tasks that would be difficult or impossible for a standalone model to solve.
Specifications & pricing
| Input (per 1M tokens) | $0.30 |
|---|---|
| Output (per 1M tokens) | $2.50 |
| Cache read (per 1M tokens) | $0.075 |
| Context window | 1,048,576 tokens |
| Max output | 65,535 tokens |
| Capabilities | images, tool calling, reasoning |
LiteLLM community dataset (MIT), verified August 15, 2026. Official Google (Gemini) pricing.
Where to try Gemini 2.5 Flash Preview 09.2025 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
What is this model good for+
This model is well-suited for tasks that involve image understanding and step-by-step reasoning, such as image classification, object detection, and image segmentation
How do I get started with this model+
To get started with this model, you will need to integrate it into your application or workflow, which may involve setting up an API or other interface to interact with the model
How does this model differ from other models in the Gemini line+
This model differs from other models in the Gemini line in its ability to perform tool calling, which allows it to leverage external functions and services to augment its capabilities
What are the limitations of this model+
The limitations of this model include its reliance on high-quality training data and its potential vulnerability to bias or errors in the data it is trained on
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