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Google: Gemma 3n 4B (free)

Gemma 3n E4B-it is optimized for efficient execution on mobile and low-resource devices, such as phones, laptops, and tablets. It supports multimodal inputs—including text, visual data, and audio—enabling diverse tasks such as text generation, speech recognition, translation, and image analysis. Leveraging innovations like Per-Layer Embedding (PLE) caching and the MatFormer architecture, Gemma 3n dynamically manages memory usage and computational load by selectively activating model parameters, significantly reducing runtime resource requirements. This model supports a wide linguistic range (trained in over 140 languages) and features a flexible 32K token context window. Gemma 3n can selectively load parameters, optimizing memory and computational efficiency based on the task or device capabilities, making it well-suited for privacy-focused, offline-capable applications and on-device AI solutions. [Read more in the blog post](https://developers.googleblog.com/en/introducing-gemma-3n/)

Context Window

8K

Input Price

Free

Output Price

Free

Modality

text->text

Pricing Breakdown
Cost per million tokens for input and output
InputFree
OutputFree

Cost Example: Processing 10,000 input tokens and generating 1,000 output tokens would cost approximately $0.000000

Context Window Size
Maximum tokens the model can process in a single request
Context Window8K tokens
1% of 1M max

~6K

Words Equivalent

~4

Pages of Text

~82

Code Functions

Best Use Cases
What this model excels at
Data-analysisAnalytics

Related Categories:

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