title: Gemini 1.5: Unlocking multimodal understanding across millions of tokens
    of context
summary: In this report, we present the latest model of the Gemini family, Gemini 1.5
  Pro, a highly compute-efficient multimodal mixture-of-experts model capable of
  recalling and reasoning over fine-grained information from millions of tokens
  of context, including multiple long documents and hours of video and audio.
  Gemini 1.5 Pro achieves near-perfect recall on long-context retrieval tasks
  across modalities, improves the state-of-the-art in long-document QA,
  long-video QA and long-context ASR, and matches or surpasses Gemini 1.0 Ultra's
  state-of-the-art performance across a broad set of benchmarks. Studying the
  limits of Gemini 1.5 Pro's long-context ability, we find continued improvement
  in next-token prediction and near-perfect retrieval (>99%) up to at least 10M
  tokens, a generational leap over existing models such as Claude 2.1 (200k) and
  GPT-4 Turbo (128k). Finally, we highlight surprising new capabilities of large
  language models at the frontier; when given a grammar manual for Kalamang, a
  language with fewer than 200 speakers worldwide, the model learns to translate
  English to Kalamang at a similar level to a person who learned from the same
  content.
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paper_title: Gemini 1.5: Unlocking multimodal understanding across millions of tokens
    of context
paper_summary: In this report, we present the latest model of the Gemini family, Gemini 1.5
  Pro, a highly compute-efficient multimodal mixture-of-experts model capable of
  recalling and reasoning over fine-grained information from millions of tokens
  of context, including multiple long documents and hours of video and audio.
  Gemini 1.5 Pro achieves near-perfect recall on long-context retrieval tasks
  across modalities, improves the state-of-the-art in long-document QA,
  long-video QA and long-context ASR, and matches or surpasses Gemini 1.0 Ultra's
  state-of-the-art performance across a broad set of benchmarks. Studying the
  limits of Gemini 1.5 Pro's long-context ability, we find continued improvement
  in next-token prediction and near-perfect retrieval (>99%) up to at least 10M
  tokens, a generational leap over existing models such as Claude 2.1 (200k) and
  GPT-4 Turbo (128k). Finally, we highlight surprising new capabilities of large
  language models at the frontier; when given a grammar manual for Kalamang, a
  language with fewer than 200 speakers worldwide, the model learns to translate
  English to Kalamang at a similar level to a person who learned from the same
  content.
paper_ai_summary: Gemini 1.5 Pro, a multimodal mixture-of-experts model, achieves near-perfect recall and state-of-the-art performance in long-context tasks, including QA and ASR, surpassing previous models in token prediction and translation capabilities.
paper_ai_keywords: multimodal mixture-of-experts long-context retrieval long-document QA long-video QA long-context ASR next-token prediction