TLDR - Gemini 1.5 Pro is a new LLM in Google's Gemini family known for its advanced capabilities. Gemini 1.5 Pro outperforms models like Claude 2.1 and GPT-4 Turbo by handling up to 10 million tokens of information context (vs. 200k and 128k respectively).

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Introducing Gemini 1.5 Pro

• Gemini 1.5 Pro is a major advancement within Google's Gemini line of large language models (LLMs). It pushes the limits of efficiency, reasoning capabilities, and handling extremely long contexts.

• A new mixture-of-experts (MoE) architecture contributes to its efficiency and improved performance.

Long Context Handling

• Scale: Gemini 1.5 Pro processes up to 10 million tokens, far exceeding other LLMs. This includes handling collections of documents, hours of video, or days of audio.

• Performance: The model surpasses the earlier Gemini 1.0 Pro and matches the state-of-the-art Gemini 1.0 Ultra on numerous benchmarks, while being more computationally efficient.

• Reasoning within Long Contexts: It demonstrates near-perfect recall of specific information from vast datasets, a breakthrough for LLMs.

New Capabilities

• In-Context Learning: Gemini 1.5 Pro exhibits remarkable in-context learning capabilities. In the provided example, the model learns to translate the Kalamang language after being given linguistic documentation about it – showcasing its ability to learn new skills from information within its context.

Performance vs. Earlier Models

• Gemini 1.0 Pro: Gemini 1.5 Pro significantly outperforms the earlier model, especially in areas like Math, Science, Reasoning, Multilinguality, and various understanding tasks related to video, images, and code.

• Gemini 1.0 Ultra: Despite using less computational power, Gemini 1.5 Pro outperforms the state-of-the-art Ultra model in over half of the benchmarks, excelling in text and various vision tasks.

Key Takeaways

• Gemini 1.5 Pro signifies a substantial leap in LLM long-context performance. The advances are not just in scale, but in retrieval reliability and new in-context learning abilities.

• This progress comes without sacrificing core capabilities – demonstrating the potential for future models to be even more capable and efficient

--> For complete details, refer to Gemini 1.5 Pro paper.