🤖 AI Summary
To address the dual challenge of achieving strong multimodal reasoning capabilities while enabling efficient on-device deployment, this paper introduces the Gemini family of large multimodal models—Gemini Ultra, Pro, and Nano. We propose a novel unified multimodal sequence modeling framework that integrates cross-modal joint representation learning, a hierarchical and scalable architecture, and responsible alignment techniques. Our models achieve human-expert-level performance on the MMLU benchmark—the first such result—and establish new state-of-the-art (SOTA) results on 30 out of 32 comprehensive evaluations, leading across 20 major multimodal benchmarks. Gemini Ultra significantly surpasses prior best methods on critical tasks including MMLU. The entire Gemini series has been deployed across Google AI Studio, Vertex AI, and Gemini Advanced, enabling seamless end-to-end deployment—from cloud-based complex reasoning to resource-constrained edge devices.
📝 Abstract
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.
