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Google · Fast work with documents, images and audio

Gemini 3.6 Flash

A fast model for documents, images, video and audio. It is suitable for a first pass that finds and organizes information from a large volume of source material.

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Costs
Medium · $1.50 per million input tokens and $7.50 per million output tokens; a token is a small piece of text
Speed
Very fast · generated 304 tokens per second in the measurement
Availability
Online service from Google and Gemini products
Input length
Very long input · up to 1 million tokens (small pieces of text)
Inputs
Text, images, video and audio as input

Data checked . Release date unverified.

Indicative capability profile

Where the model is strong

The five levels are our clear summary of the results below. They are not a ranking that applies to every task.

Analysis Solves practical problems well, but does not reach the very top tier.
Coding Usable code, but not the first choice for the most difficult programs.
Autonomous work across multiple steps A good choice for quick work with documents, images and audio.
Writing and content Suitable for finding data and summarizing; check important outputs.
Speed 304 output tokens per second in the test.

Ideal use

When to choose it

  • Finding and sorting information in documents and media
  • Interactive assistants with fast responses
  • First pass through long documents

Usage boundaries

When to choose another model

  • Final strategic decision without further review
  • The hardest scientific and logic problems

Traceable supporting sources

Data and measurement sources

Distinguish between the manufacturer's documentation and the results of a specific test. Measurements also depend on the settings and the task set used.

Artificial Analysis

Intelligence Index v4.1

50 points

Performance close to the best models in significantly less time.

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Artificial Analysis

Time and cost per task

1.3 min · $0.50

A practical signal for fast, high-volume processing.

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Next step

Compare the model using your actual task.

A benchmark narrows the selection. A short trial on your data determines the choice.

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