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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

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
MathReasoningAnalysisData-analysisAnalytics

Related Categories:

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