Google: Gemma 3n 2B (free)
Gemma 3n E2B IT is a multimodal, instruction-tuned model developed by Google DeepMind, designed to operate efficiently at an effective parameter size of 2B while leveraging a 6B architecture. Based on the MatFormer architecture, it supports nested submodels and modular composition via the Mix-and-Match framework. Gemma 3n models are optimized for low-resource deployment, offering 32K context length and strong multilingual and reasoning performance across common benchmarks. This variant is trained on a diverse corpus including code, math, web, and multimodal data.
Context Window
8K
Input Price
Free
Output Price
Free
Modality
text->text
Cost Example: Processing 10,000 input tokens and generating 1,000 output tokens would cost approximately $0.000000
~6K
Words Equivalent
~4
Pages of Text
~82
Code Functions
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