Home / Models / Gemma 4 (E2B, E4B, 12B, 26B A4B, 31B)

Gemma 4 (E2B, E4B, 12B, 26B A4B, 31B)

Google DeepMind · Multimodal model

Gemma 4 is Google's current family of open-weight models, released in April 2026 under Apache 2.0. It ranges from small E2B/E4B models for phones to a 31B dense model, all reading text and images and writing text. A 12B 'unified'…

Current Open weights
ReleasedApr 2026
Context window128K tokens (E2B, E4B); 256K tokens (12B, 26B A4B, 31B)
Input → outputtext, image, audio, video → text
API price—
Consumer accessFree download of weights (Hugging Face, Google AI Studio) under Apache 2.0; runs locally.

Best for

Self-hosted or on-device AI where you need an open, commercially permissive model in sizes from phone to single-GPU server.

Overview

Gemma 4 is Google's current family of open-weight models, released in April 2026 under Apache 2.0. It ranges from small E2B/E4B models for phones to a 31B dense model, all reading text and images and writing text. A 12B 'unified' model and quantized (QAT) versions followed in June 2026.

Strengths and limits

  • Apache 2.0 license (more permissive than earlier Gemma terms)
  • Wide size range, incl. E2B/E4B for mobile and a 26B mixture-of-experts
  • Strong reported scores for its size (31B: 85.2% MMLU Pro)
  • Audio input only on E2B, E4B and 12B; video handled as frame sequences
  • Well behind frontier closed models on hard agentic tasks

Published benchmark scores

MMLU Pro (31B IT)85.2% Source: ai.google.dev
AIME 2026, no tools (31B IT)89.2% Source: ai.google.dev
LiveCodeBench v6 (31B IT)80.0% Source: ai.google.dev
GPQA Diamond (31B IT)84.3% Source: ai.google.dev

Scores are as published by the source; settings (reasoning effort, tools, attempts) differ between labs, so compare with care. How to read benchmarks →