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On-Device Text, Image & Audio Understanding


Google DeepMind's smallest and most efficient Gemma 4 model, designed to run locally on phones and laptops, with full support for text, images, and audio.

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What it does

Gemma 4 E2B is the smallest model in Google DeepMind's Gemma 4 family, built specifically for on-device and edge deployment. Despite its compact size, it is a fully multimodal AI that understands text, images, and audio, and is one of only two models in the Gemma 4 family to natively support audio input. It handles a wide range of everyday AI tasks including answering questions, analyzing images, transcribing and translating speech, summarizing documents, generating code, and powering conversational assistants. The "E" in E2B stands for "effective" and the model is architecturally optimized to deliver strong performance well above what its size would normally suggest.

Problem it solves

Input/Output

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Accuracy & Speed

Benchmark Performance

Benchmark Gemma 4 E2B What it measures
MMLU-Pro 60.0% Broad expert knowledge
AIME 2026 37.5% Advanced mathematics
LiveCodeBench v6 44.0% Real-world coding ability
GPQA Diamond 43.4% Expert-level science reasoning
MMMLU (multilingual) 67.4% Cross-language knowledge
MMMU-Pro 44.2% Multimodal academic understanding
FLEURS (speech, lower is better) 0.09 Speech recognition accuracy

For context: these scores significantly outperform the previous generation Gemma 3 27B model on reasoning and coding, despite being a fraction of the size.

Technical Details

Architecture Dense transformer with hybrid local + global attention
Effective parameters 2.3 billion (5.1B including embeddings)
Context window 128,000 tokens (~100,000 words)
Modalities supported Text, Image, Audio, Video
Audio support Speech recognition (ASR) and speech translation (AST) — up to 30 seconds
Video support Up to 60 seconds at 1 frame per second
Thinking mode Built-in — enable for step-by-step reasoning on complex tasks
Function calling Native — connects to external tools and APIs for agentic workflows
Languages 35+ out-of-the-box; pre-trained on 140+ languages
Supported frameworks Hugging Face Transformers