Gemma 4 Technical Report
Gemma 4, the latest generation of open-weight, natively multimodal language models in the Gemma family, featuring dense and Mixture-of-Experts architectures ranging from 2.3B to 31B parameters. It includes improved vision/audio encoders, a unified encoder-free architecture for the 12B model, and a thinking mode that generates reasoning traces before responding. Enhancements in inference speed, memory efficiency, and long-context capabilities lead to strong performance on STEM, multimodal, and long-context benchmarks, rivaling larger frontier open models.
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[Submitted on 2 Jul 2026]
Title:Gemma 4 Technical Report
et al. (201 additional authors not shown)
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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.
Comments: 17 pages, 2 figures, technical report
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.02770 [cs.CL]
(or arXiv:2607.02770v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.02770
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Johan Ferret [view email] [v1] Thu, 2 Jul 2026 21:08:53 UTC (330 KB)
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