The annual developer conference, Google I/O 2024 concluded on May 14, in the tech giant’s hometown, Mountain View, California. The conference unveiled Google’s latest advancements in AI technology, including the highly anticipated new Gemini models- Flash and Nano. The event also provided insights into the Gemini 1.5 Pro model, which was released earlier this year.
While both Gemini 1.5 Pro and Flash are “Lightweight, fast, and cost-efficient while featuring multimodal reasoning and a breakthrough long context window of up to one million tokens,” they have quite a few differences between them.
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About Gemini 1.5 Pro
Gemini 1.5 Pro is a multi-purpose model capable of text-to-text generation, translation, question answering, code generation, and summarization tasks. Initially released in December with a token window of 128,000, the multimodal model went through an upgrade and returned with improved speed and a revolutionary extended context window of one million tokens.
About Gemini 1.5 Flash
Introduced at the Google I/O conference, Gemini Flash is a newer and upgraded model. It is optimized for speed and efficiency, and has a “one-million-token context window by default.”
This article will cover the key differences between Gemini AI 1.5 Pro vs Gemini Flash.
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Gemini AI 1.5 Pro vs Flash
Gemini AI 1.5 Pro and Google Flash employ different model architectures.
- Gemini AI 1.5 Pro relies on a transformer-based architecture, leveraging Google’s in-house research and expertise in language models. This architecture enables Gemini AI 1.5 Pro to perform various Natural Language Processing (NLP) tasks, such as sentiment analysis and question-answering.
- Gemini Flash, on the other hand, utilizes a hybrid approach combining traditional and cutting-edge neural network techniques. This unique architecture ensures enhanced accuracy and flexibility in tackling NLP tasks while providing improved personalization.
Gemini AI 1.5 Pro and Flash also differ in their strengths in different areas:
- Gemini AI 1.5 Pro excels in multimodal search, allowing users to conduct searches across various modes, including text, images, and videos. Additionally, it supports a wide range of NLP tasks, making it a comprehensive AI solution.
- Gemini Flash, however, focuses on personalized user experience. Its adaptability enables it to fine-tune responses based on user preferences and intent, making it an ideal choice for tasks requiring a nuanced understanding of user needs.
More between Gemini AI 1.5 Pro and Flash:
- Model Architecture: Gemini AI 1.5 Pro uses a transformer-based architecture, while Google Flash employs a hybrid neural network approach.
- Multimodal Search vs. Personalization: Gemini AI 1.5 Pro prioritizes multimodal search capabilities, whereas Google Flash concentrates on personalization.
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Gemini AI 1.5 Pro vs Flash: Key Differences
| Capability | Benchmark | Description | GEMINI 1.0 PRO | GEMINI 1.5 PRO(Feb 2024) | GEMINI 1.5 FLASH |
| General | MMLU | Representation of questions in 57 subjects (incl. STEM, humanities, and others) | 71.8% | 81.9% | 78.9% |
| Code | Natural2Code | Python code generation. Held out dataset HumanEval-like, not leaked on the web | 69.6% | 77.7% | 77.2% |
| Math | MATH | Challenging math problems (incl. algebra, geometry, pre-calculus, and others) | 32.6% | 58.5% | 54.9% |
| Reasoning | GPQA (main) | Challenging dataset of questions written by domain experts in biology, physics, and chemistry | 27.9% | 41.5% | 39.5% |
| Big-Bench Hard | Diverse set of challenging tasks requiring multi-step reasoning | 75.0% | 84.0% | 85.5% | |
| Multilingual | WMT23 | Language translation | 71.7 | 75.2 | 74.1 |
| Image | MMMU | Multi-discipline college-level reasoning problems | 47.9% | 58.5% | 56.1% |
| MathVista | Mathematical reasoning in visual contexts | 45.2% | 52.1% | 54.3% | |
| Audio | FLEURS (55 languages) | Automatic speech recognition (based on word error rate, lower is better) | 6.4 | 6.6 | 9.8 |
| Video | EgoSchema | Video question answering | 55.7% | 63.2% | 63.5% |
Both Gemini 1.5 Pro and Gemini Flash are available in the new 2 million token context window. There is a waitlist to access them. You can join the waitlist here.
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