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Recipe ·

Gemini 3.1 Flash-Lite

Google's Gemini 3.1 Flash-Lite is a cost-optimized AI model designed for high-volume production workloads, offering a balance of speed and efficiency for developers.

6
Techniques inside
5y
Median research → prod
3y
Fastest adoption
9y
Slowest adoption

Ingredient list

  1. Invented by Nous Research · 2023-08 · Velocity 3y

    Gemini 1.5 models feature a 1 million token context window, achieved via novel research on efficient attention and positional encoding.

    architecturemedium
  2. Invented by Google · 2022-01 · Velocity 4y

    Gemini models are trained to reason step-by-step, and Gemini 1.5 Flash and Pro can be prompted to show their reasoning.

    reasoninghigh
  3. Invented by Google · 2021-09 · Velocity 5y

    Gemini models are instruction-tuned, building on the FLAN instruction-tuning methodology.

    traininghigh
  4. Invented by Zhuiyi Technology · 2021-04 · Velocity 5y

    Gemini models use rotary position embeddings (RoPE) for positional encoding.

    architecturehigh
  5. Invented by Google · 2017-06 · Velocity 9y

    Gemini models are based on the Transformer decoder architecture.

    architecturehigh
  6. Invented by Google · 2017-01 · Velocity 9y

    Gemini 1.5 Pro uses a Mixture-of-Experts (MoE) architecture. Flash-Lite is a distilled version of the larger MoE models.

    architecturehigh

This recipe is part of the gentic.news Deployment Atlas. Every ingredient has an origin paper + evidence. Methodology is public. Dataset is CC BY 4.0.

Gemini 3.1 Flash-Lite Recipe — The Research Behind the Model | gentic.news