Google Gemini 1.5 Unveiled: A Leap Forward in AI Technology

Google Gemini 1.5 Unveiled: A Leap Forward in AI Technology

8 mins readComment
Vikram Singh
Assistant Manager - Content
Updated on Feb 21, 2024 10:54 IST

Are you curious about the latest AI developments? Then, you must have heard about Google Gemini - 1.5, the newest release from Google DeepMind. In this Q&A article, we will explore the ins and outs of this cutting-edge technology and how it's unlocking multimodal understanding across millions of tokens of context. From the technical details to the real-world implications, we've got you covered. So, let's dive into the world of Google Gemini 1.5 and see what all the hype is about.

google gemini 1.5

In this article, we will explain all about the Google Gemini-1.5. We will find out what Google Gemini-1.5 is, how it differs from other generative AI tools, their real-life application, and many more.

Table of Content

What is Google Gemini?

In May 2023, Google introduced a language model called Gemini (previously known as Google Bard), which has impressed the AI community with its remarkable capabilities. With an incredible 1.5 trillion parameters, Gemini is one of the largest and most advanced language models developed to date. 

Gemini's architecture is based on the Transformer neural network, a highly effective encoder-decoder model that has revolutionized natural language processing. The Transformer includes numerous self-attention mechanisms, allowing the model to focus on different parts of the input sequence and capture long-term dependencies.

Gemini's Transformer architecture comprises:

  • Encoder: Processes the input sequence and generates a contextualized representation.
  • Decoder: Generates the output sequence based on the encoder's representation and the target sequence.
  • Multi-headed Self-Attention: Allows the model to attend to different parts of the input sequence simultaneously.
  • Feed-Forward Network: Transforms the output of the self-attention layers.

What is Google Gemini - 1.5?

Gemini 1.5 is the next-generation model that has a 1 million token context window, which means it can understand the context of a sentence or paragraph better than previous models. This is significant because it allows Gemini 1.5 to generate more accurate and relevant responses. Gemini 1.5 also uses a MoE (Mixture of Experts) architecture, which allows it to be more efficient than previous models.

The access to Google Gemini is not available to the public. Only developers and enterprises can sign up for limited access in Google AI Studio and Vertex AI.

Key Features of Gemini - 1.5 includes:

  • It can process both text and other formats like image, code, and video.
    • Process up to:
      • 1 hour of video
      • 11 hours of audio
      • Codebases with over 30,000 lines of code or 700,000 words.
  • With a standard 128,000 token window (and an experimental 1 million token window in private preview!), it can analyze extensive information at once. This allows it to understand complex tasks and answer questions based on vast amounts of context.
  • Performance near Gemini 1.0 Ultra: While a mid-size model, it performs on par with Google's largest model to date, Gemini 1.0 Ultra, on standard benchmarks.
  • Improved efficiency: Its "Mixture of Experts" (MoE) architecture makes it more efficient to train and use compared to previous models.

In what ways does Gemini 1.5 Pro demonstrate its understanding of multimodal information across millions of tokens of context?

Gemini 1.5 Pro demonstrates its understanding of multimodal information across millions of tokens of context in several ways:

  1. Joint reasoning across modalities: It doesn't just process each modality (text, code, images, video) separately. Instead, it actively connects and reasons across them, drawing insights from the relationships between different data types.
  • For example, analyzing a medical image alongside its textual report to understand the context and significance of specific findings.
  • Interpreting a scientific paper by considering the relationships between text, figures, and tables to identify key trends and conclusions.
  1. Contextual awareness within modalities: Even within individual modalities, Gemini 1.5 Pro demonstrates understanding by considering the broader context.
  • In text, it analyzes sentiment and meaning based on surrounding sentences and paragraphs, not just individual words.
  • In images, it recognizes objects and their relationships within the entire scene, not just isolated features.
  • In videos, it tracks events and understands their temporal relationships across the entire sequence.
  1. Long-term dependencies and memory: The massive context window allows it to remember and utilize information from millions of tokens back, enabling it to understand complex relationships and answer questions requiring deep understanding.
  • For example, answering a question about a specific character in a long novel by considering their actions, motivations, and interactions throughout the entire story.
  • Analyzing a codebase by understanding the relationships and dependencies between different functions and modules spread across thousands of lines.
  1. Generating multimodal outputs: Gemini 1.5 Pro can not only understand multimodal information but also generate outputs that combine different modalities.
  • For example, creating a video summary of a research paper by combining key findings from the text with relevant images and visuals.
  • Generating a code snippet based on a textual description of its functionality.
  1. Adapting to different contexts: It can adjust its interpretation and reasoning based on the specific context and task at hand.
  • For example, understanding the nuances of humour in text when analyzing a comedy script but using a different approach for scientific documents.
  • Interpreting an image differently depending on whether it's part of a news article, a medical report, or a social media post.

How does Gemini 1.5 Pro perform on reasoning, math, and science tasks compared to previous versions of the model?

Compared to previous versions like Gemini 1.0 Pro and Gemini 1.0 Ultra, Gemini 1.5 Pro shows a substantial leap in performance on reasoning, math, and science tasks. Here's a breakdown:


  • Increased complexity: Handles complex reasoning tasks requiring multi-step inference, understanding of cause-and-effect relationships, and drawing conclusions from diverse data.
  • Multimodal integration: Integrates information from text, code, images, and videos for richer understanding and problem-solving.
  • Long-context awareness: Utilizes its 1 million token window to analyze vast amounts of information, crucial for complex reasoning tasks.


  • Symbolic and computational tasks: Solves mathematical problems involving both symbolic algebra and numerical calculations.
  • Word problem understanding: Accurately interprets word problems and translates them into mathematical equations.
  • Real-world application: Applies mathematical knowledge to solve problems in various domains like physics, engineering, and finance.


  • Scientific text comprehension: Accurately understands scientific concepts, theories, and data presented in research papers, textbooks, and other resources.
  • Reasoning and analysis: Draws conclusions from scientific data, identifies patterns, and generates hypotheses.
  • Knowledge integration: Integrates knowledge from various scientific disciplines for comprehensive understanding.

Performance Improvement:

  • Benchmarks: Reports indicate a 28.9% improvement in performance on reasoning, math, and science tasks compared to Gemini 1.0 Pro and a 5.2% improvement over Gemini 1.0 Ultra.
  • Generalizability: This improvement seems consistent across various benchmarks and real-world tasks.

Factors contributing to improved performance:

  • Larger training dataset: Trained on a massive dataset, including scientific literature and mathematical problems, increasing knowledge and understanding.
  • Mixture of Experts (MoE) architecture: Optimizes processing for specific tasks, leading to improved efficiency and accuracy.
  • Improved attention mechanisms: Focuses on relevant information while processing complex tasks.

What are the Different Applications of Google Gemini - 1.5?

  1. Scientific Research:
  • Analyzing scientific papers: By processing both text and accompanying figures, tables, and graphs, Gemini 1.5 Pro could gain a deeper understanding of complex scientific research, aiding researchers in areas like:
    • Identifying important patterns and relationships across various data sources.
    • Summarizing key findings and generating hypotheses.
    • Fact-checking and verifying information within papers.
  • Analyzing and interpreting medical scans: Integrating image analysis with textual reports could help diagnose diseases more accurately, predict patient outcomes, and personalize treatment plans.
  • Exploring historical documents and artifacts: By interpreting text, images, and even audio recordings, Gemini 1.5 Pro could offer novel insights into historical events and cultural understanding.
  1. Content Creation and Media Production:
  • Generating multimedia content: It could create video scripts based on accompanying images, compose music pieces with specific emotional tones, or generate poems inspired by paintings.
  • Personalizing news articles and summaries: Tailoring news content to individual preferences by considering both text and accompanying images or videos.
  • Generating educational materials: Creating interactive learning experiences that combine text, visuals, and audio explanations.
  1. Business and Industry:
  • Analyzing customer reviews and feedback: Understanding sentiment and extracting key insights from text, images, and video reviews to improve products and services.
  • Automating document analysis and processing: Efficiently extracting information from complex documents like contracts, invoices, and legal documents.
  • Facilitating communication and collaboration: Enabling cross-cultural communication by translating and interpreting text, images, and audio in real-time.
  1. Education and Training:
  • Providing personalized learning experiences: Adapting learning materials and explanations based on individual needs and learning styles, utilizing text, images, and videos.
  • Creating immersive learning environments: Simulating real-world scenarios by combining text with virtual reality or augmented reality experiences.
  • Evaluating student performance: Analyzing multiple data sources like essays, presentations, and recordings to provide more comprehensive feedback.
  1. Personal Use and Entertainment:
  • Generating personalized travel itineraries: Creating plans based on user preferences, incorporating text descriptions, images, and videos of destinations.
  • Personalizing entertainment recommendations: Suggesting movies, music, or books based on user preferences and their emotional responses to trailers, snippets, and reviews.
  • Creating interactive storytelling experiences: Engaging users in stories that combine text, audio, and visuals that respond to their choices and actions.

Comparing Large Language Models: Gemini 1.5, Gemini 1.0, ChatGPT 4, Perplexity AI, Claude


Gemini 1.5

Gemini 1.0




Model Size

Mid-size (parameters not disclosed)

Large (parameters not disclosed)

Large (175B parameters)

Large (137B parameters)

Large (137B parameters)


Multimodal (text, code, images, video)

Primarily text and code

Primarily text

Primarily text

Primarily text

Context Window

Standard 128,000 tokens (experimental 1 million tokens)

2048 tokens




Performance Benchmark

Near Gemini 1.0 Ultra






Improved due to MoE architecture

Less efficient





Private preview

Generally available

Generally available

Generally available

Private beta


Long-context understanding, multimodal reasoning

Powerful and versatile

Conversational AI, creative text generation

Conversational AI, factual language understanding

Summarization, translation, question answering


Multimodality, long-context understanding, efficiency

Large model size, high performance

User-friendly interface, large community

Efficient, factual understanding

Diverse skills, summarization, translation


Private preview, limited access

Not multimodal, large and resource-intensive

Lacks some factual accuracy

Lacks multimodal capabilities

Closed beta, limited access

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About the Author
Vikram Singh
Assistant Manager - Content

Vikram has a Postgraduate degree in Applied Mathematics, with a keen interest in Data Science and Machine Learning. He has experience of 2+ years in content creation in Mathematics, Statistics, Data Science, and Mac... Read Full Bio