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Gemma 4 Technical Report

:::info Stub — Full Engineering Breakdown Coming This paper was featured on Hugging Face Daily Papers on 2026-07-02 with 62 upvotes. A full breakdown with production viability rating, implementation notes, and honest limitations is being written. Subscribe to AI Letters → :::

AuthorsGemma Team et al.
Year2026
HF Upvotes62
arXiv2607.02770
PDFDownload
HF PageView on Hugging Face

Abstract​

We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemma 4 model suite features dense and Mixture-of-Experts architectures, ranging from 2.3B to 31B parameters. Alongside improved vision and audio encoders for all model sizes, we propose a unified, encoder-free architecture for our 12B model, which ingests raw audio and image patches. Furthermore, we integrate a thinking mode, enabling Gemma models to generate reasoning traces prior to responding. We improve inference speed, memory, and compute efficiency, as well as long-context abilities through critical design choices. Gemma 4 establishes a leap in performance across STEM, multimodal, and long-context benchmarks, and rivals larger, frontier open models in human-rated tasks.

Engineering Breakdown​

The Problem​

We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family.

The Approach​

We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Alongside improved vision and audio encoders for all model sizes, we propose a unified, encoder-free architecture for our 12B model, which ingests raw audio and image patches.

Key Results​

We improve inference speed, memory, and compute efficiency, as well as long-context abilities through critical design choices.

Research Areas​

This paper contributes to the following areas of AI/ML engineering:

  • Machine learning
  • Deep learning
  • Neural networks
  • Model optimization
  • AI systems
  • Technical

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