From natural image, audio, and video understanding to mathematical reasoning, performance exceeds current state-of-the-art results on 30 of the 32 widely-used academic and multimodal benchmarks used in large language model research and development.

The first model to outperform human experts on massive multitask language understanding, which uses 57 subjects such as math, physics, history, law, medicine, ethics, and more for testing both world knowledge and problem solving abilities.

Can seamlessly analyze, classify and summarize large amounts of content within a given prompt.

Can perform highly sophisticated understanding and reasoning tasks for different modalities.

When given a prompt with more than 100,000 lines of code, it can better reason across examples, suggest helpful modifications and give explanations about how different parts of the code works.

Fine-tuned both to be a coding model to generate proposal solution candidates, and to be a reward model that is leveraged to recognize and extract the most promising code candidates.

Significantly outperforms the USM and Whisper models across all ASR and AST tasks, both for English and multilingual test sets.

Excels at on-device tasks, such as summarization, reading comprehension, text completion tasks, and exhibits impressive capabilities in reasoning, STEM, coding, multimodal, and multilingual tasks relative to their sizes.

With capabilities accessible to a larger set of platforms and devices, the Gemini models expand accessibility to everyone.

Sub-second average first-token latency for the vast majority of developer and enterprise use cases.

On most common tasks, 1.5 Flash achieves comparable quality to larger models, at a fraction of the cost.

Process hours of video and audio, and hundreds of thousands of words or lines of code.

Demonstrates improved capabilities in logic, common sense reasoning, and mathematics.

Improved its ability to understand, generate and translate nuanced text — including idioms, poems and riddles. PaLM 2 also passes advanced language proficiency exams at the “mastery” level.

Excels at popular programming languages like Python and JavaScript, but is also capable of generating specialized code in languages like Prolog, Fortran, and Verilog.

Achieves accurate, high-quality photorealistic outputs with improved image+text understanding and a variety of novel training and modeling techniques.

Text-to-image models often struggle to include text accurately. Imagen 3 improves this process, ensuring the correct words or phrases appear in the generated images.

Imagen 3 understands prompts written in natural, everyday language, making it easier to get the output you want without complex prompt engineering.

Includes built-in safety precautions to help ensure that generated images align with Google’s Responsible AI principles.

Suggests the next few lines based on the existing context of code.

Generates code based on natural language prompts from a developer.

Lets developers converse with a bot to get help with debugging, documentation, learning new concepts, and other code-related questions.

Can transcribe in over 100 languages with excellent speech recognition.

Achieves state-of-the-art Word Error Rate (WER) on a variety of public test sets and languages. It delivers 98% speech recognition accuracy in English and over 300% relative improvement in several languages with less than 10M speakers.

Chirp's 2-billion-parameter model outpaces previous speech models to deliver superior performance.

With just text prompts, it creates high-quality, 1080P videos that can go beyond 60 seconds. Lets you control the camera, and prompt for things like time lapse or aerial shots of a landscape.

Interprets and visualizes the tone of prompts. Subtle cues in body language, lighting, and even color choices could dramatically shift the look of a generated video.

Able to retain visual consistency in appearance, locations and style across multiple scenes in a longer video.

Veo allows users to edit videos through prompts, including modifying, adding or replacing visual elements and it can generate a video from an image input, using the image to fit within any frame of the output and the prompt as guidance for how the video should proceed.

Revolutionizes the way medical information is accessed, analyzed, and applied. Reduces administrative burdens and helps synthesize information seamlessly.

MedLM is a customizable solution that can embed into your workflow and integrate with your data to augment your healthcare capabilities.

Born from a belief that together, technology and medical experts can innovate safely, MedLM helps you stay on the cutting edge.

Allow for practice and healthy struggle with timely feedback.

Present relevant, well-structured information in multiple modalities.

Dynamically adjust to goals and needs, grounding in relevant materials.

Inspire engagement to provide motivation through the learning journey.

Plan, monitor and help the learner reflect on progress.

Tuned, trained and grounded in threat intelligence from Google, VirusTotal, and Mandiant to bring up-to-date security information and context to users.

Gemini in Security agents use SecLM to help defenders protect their organizations.

Cybersecurity professionals can easily make sense of complex information and perform specialized tasks and workflows.

Incorporating comprehensive safety measures, these models help ensure responsible and trustworthy AI solutions through curated datasets and rigorous tuning.

Gemma models achieve exceptional benchmark results at its 2B and 7B sizes, even outperforming some larger open models.

With Keras 3.0, enjoy seamless compatibility with JAX, TensorFlow, and PyTorch, empowering you to effortlessly choose and switch frameworks depending on your task.

Complete lines, functions, and even generate entire blocks of code, whether you're working locally or using Google Cloud resources.

Trained on 500 billion tokens data from web documents, mathematics, and code. Generates code that's not only more syntactically correct but also semantically meaningful, reducing errors and debugging time.

Supports Python, JavaScript, Java, Kotlin, C++, C#, Rust, Go, and other languages.

Lower memory requirements allow for the generation of longer samples on devices with limited memory, such as single GPUs or CPUs.

Can perform inference at significantly higher batch sizes, thus generating substantially more tokens per second (especially when generating long sequences).

Showcases a non-transformer model that achieves high performance, highlighting advancements in deep learning research.

Designed for class-leading fine-tune performance on a wide range of vision-language tasks like:

• image and short video captioning
• visual question answering
• understanding text in images
• object detection
• and object segmentation

Supports a wide range of languages.