Gemma 3n E4B Instructed

Gemma 3n is a multimodal model designed to run locally on hardware, supporting image, text, audio, and video inputs. It features a language decoder, audio encoder, and vision encoder, and is available in two sizes: E2B and E4B. The model is optimized for memory efficiency, allowing it to run on devices with limited GPU RAM. Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma models are well-suited for a variety of content understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. Gemma 3n models are designed for efficient execution on low-resource devices. They are capable of multimodal input, handling text, image, video, and audio input, and generating text outputs, with open weights for instruction-tuned variants. These models were trained with data in over 140 spoken languages.

Benchmark results

Benchmark Score Tags Source
AIME 2025 11.6% self-reported llm-stats link →
Codegolf v2.2 16.8% self-reported llm-stats link →
ECLeKTic 19.0% self-reported llm-stats link →
Global-MMLU 60.3% self-reported llm-stats link →
Global-MMLU-Lite 64.5% self-reported llm-stats link →
GPQA 23.7% self-reported llm-stats link →
HiddenMath 37.7% self-reported llm-stats link →
HumanEval 75.0% self-reported llm-stats link →
Include 57.2% self-reported llm-stats link →
LiveCodeBench 13.2% self-reported llm-stats link →
LiveCodeBench v5 25.7% self-reported llm-stats link →
MBPP 63.6% self-reported llm-stats link →
MGSM 67.0% self-reported llm-stats link →
MMLU 64.9% self-reported llm-stats link →
MMLU-Pro 50.6% self-reported llm-stats link →
MMLU-ProX 19.9% self-reported llm-stats link →
OpenAI MMLU 35.6% self-reported llm-stats link →
WMT24++ 50.1% self-reported llm-stats link →