Google

Gemini 2.0 Flash-Lite

Public pricingHuge memoryVision

Gemini 2.0 Flash-Lite is Google's huge-memory model with vision. This page shows current pricing, an interactive cost calculator, and a side-by-side with similar models.

Input

$0.07/1M tokens

Output

$0.30/1M tokens

Cached

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Batch

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Interactive

Calculate your Gemini 2.0 Flash-Lite bill.

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What would Gemini 2.0 Flash-Lite cost you?

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1,00010,00050,000250,0001M10M

Technical specifications

Gemini 2.0 Flash-Lite at a glance.

Memory

1,000,000

tokens

Max reply

8,192

tokens

Memory tier

Huge

multiple books or whole repositories

Tokenizer

sentencepiece

Released

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Training cutoff

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Availability

Public pricing

Status

active

What it can do

Capabilities & limits.

  • Understands images
  • Deep step-by-step thinking
  • Uses tools / calls functions
  • Strict JSON output
  • Streams replies
  • Fine-tunable on your data

When to pick Gemini 2.0 Flash-Lite

  • Screenshot analysis, image understanding, or document OCR.
  • Long documents, full codebases, or extensive chat histories.
  • High-volume workloads where unit cost matters.

When to look elsewhere

  • You need tool-use / function calling for agent workflows.

Alternatives

Similar models to compare.

FAQ

Gemini 2.0 Flash-Lite — the questions we see most.

Pricing, capabilities, alternatives — generated from the same data that powers the calculator above.

Get instant answers from our AI agent

At a typical workload of 50,000 conversations a month with 1,500-token prompts and 800-token replies, Gemini 2.0 Flash-Lite costs roughly $18 per month. Input is $0.07 /1M tokens and output is $0.30 /1M tokens.
Gemini 2.0 Flash-Lite has a 1,000,000-token context window (huge memory — multiple books or whole repositories). That means you can fit about 187,500 words of input and history in a single call.
Beyond text generation, Gemini 2.0 Flash-Lite supports understanding images. It streams replies by default.
Models in a similar class include Gemini 2.0 Flash, Gemini 2.5 Flash, Gemini 3 Flash. The "Similar models" section below this FAQ links into each.

Still unsure?

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