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Gemini 2.5 Flash-Lite

gemini-2.5-flash-lite

Google's smallest and most cost-effective 2.5 model, built for at-scale usage. Built by Google.

Prices updated

Input price

$0.10

per 1M tokens · Standard

Output price

$0.40

per 1M tokens · Standard

Input limit

1M

tokens

Output limit

66K

tokens

Input formats

TextImageVideoAudioPDF

Output formats

TextImageVideoAudioPDF
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Gemini 2.5 Flash-Lite price history

2 price records since 9/25/2026

Gemini 2.5 Flash-Lite cost calculator

Input20M tokens
Output5M tokens

$4.00/month

Standard pricing

    Overview

    What is Gemini 2.5 Flash-Lite?

    Gemini 2.5 Flash-Lite is a text & reasoning and vision model from Google. Google's smallest and most cost-effective 2.5 model, built for at-scale usage. Its 1M context window and $0.10 input price make it a candidate for cost-sensitive, high-throughput applications.

    Image understandingDocument analysisContent workflowsClassification

    Benchmarks

    Gemini 2.5 Flash-Lite benchmarks & speed

    How Gemini 2.5 Flash-Lite scores on standardized evaluations, and where it lands among every model we track.

    6.7

    Intelligence Index

    Better than 7% of 153 models

    268tok/s

    Output speed

    Better than 96% of 119 models

    Reasoning

    GPQA Diamond

    47.4%

    Graduate-level scientific reasoning · top 89%

    HLE

    3.7%

    Humanity's Last Exam · top 93%

    Latency & design

    Time to first token
    0.31s

    Independent scores from Artificial Analysis and DesignArena · updated 10/5/2026. Higher is better; ranks compare against every model we track with that score.

    Rates

    Gemini 2.5 Flash-Lite pricing

    Pricing is based on token usage. Provider-specific caching, batch, regional, and tool charges may affect the final cost.

    Input · Standard

    $0.10

    Per 1M tokens

    Cached input · Standard

    $0.01

    Per 1M tokens

    Capabilities

    Gemini 2.5 Flash-Lite Tools

    Tools available when using Gemini 2.5 Flash-Lite through supported provider APIs.

    Function calling

    Supported

    Structured outputs

    Supported

    JSON mode

    Supported

    Reasoning

    Supported

    Built-in web search

    Not supported

    Log probabilities

    Not supported

    Deterministic seed

    Supported

    Parallel tool calls

    Not supported

    Prompt caching

    Supported

    Where to run it

    Gemini 2.5 Flash-Lite API providers

    5 providers serve Gemini 2.5 Flash-Lite. Prices are per 1M tokens; uptime is the last 24 hours.

    Google
    Input
    $0.10
    Output
    $0.40
    Cached
    $0.01
    Context
    1M
    Uptime
    99.96%
    Google
    Input
    $0.10
    Output
    $0.40
    Cached
    $0.01
    Context
    1M
    Uptime
    99.26%
    Google AI Studio
    Input
    $0.05
    Output
    $0.20
    Cached
    $0.0050
    Context
    1M
    Uptime
    99.99%
    Google AI Studio
    Input
    $0.10
    Output
    $0.40
    Cached
    $0.01
    Context
    1M
    Uptime
    99.96%
    Google AI Studio
    Input
    $0.18
    Output
    $0.72
    Cached
    $0.02
    Context
    1M
    Uptime
    99.95%

    Strengths and limitations

    Strengths

    • Low input cost at $0.10 per million tokens suits high-volume workloads.
    • 1M context supports large documents, repositories and extended conversations.
    • Supports text & reasoning and vision workloads in one model.

    Limitations

    • Generated tokens cost 4× more than input tokens, which matters for verbose responses.
    • Large context capacity does not guarantee consistent retrieval across the entire prompt.
    • Arena Elo and MMLU-Pro are directional; test accuracy, latency and reliability on your own workload before committing.

    Arena Elo, MMLU-Pro and pricing figures are illustrative; validate current vendor terms before purchase.

    Same provider

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    Frequently asked questions about Gemini 2.5 Flash-Lite

    Gemini 2.5 Flash-Lite is a text & reasoning and vision model from Google. Google's smallest and most cost-effective 2.5 model, built for at-scale usage.