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.
- 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.
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.
Intelligence Index v4.1
50 pointsPerformance close to the best models in significantly less time.
Open original source ↗Time and cost per task
1.3 min · $0.50A practical signal for fast, high-volume processing.
Open original source ↗Trends over time
Related events from AI Radar
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New: The recommended replacement for Claude Opus 4.7 is Claude Opus 5.5, not Claude Opus 5.
When switching from Claude Opus 4.7, follow the current recommendation to use Claude Opus 5.5; the earlier announcement listed a different replacement.
Migrating company workflows and integrations to supported models may require enabling replacement models through access policies in Copilot Enterprise or Copilot Business.
Next step
Compare the model using your actual task.
A benchmark narrows the selection. A short trial on your data determines the choice.