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AI123AI ModelsgoogleGemini 3.8 Flash
Gemini 3.8 Flash

Gemini 3.8 Flash

google

Gemini 3.8 Flash is Google's most intelligent Flash model with significant gains from 3.7 Flash across software engineering, agentic tasks, and multi-step reasoning.

Modalities
TT
Input / output price
$0.825/$4.13per 1M tokens
Context
1.0M
Released
2026-09-02
PricingArtificial AnalysisDesign ArenaRelated Models

Pricing

Usage prices in USD. Token rates are per one million tokens; other billing units are shown with each charge.
InputOutputCache readCache writeWeb search
$0.83/ 1M tokens$4.13/ 1M tokens$0.08/ 1M tokens$0.04/ 1M tokens$0.014/ request

Artificial Analysis

Explore rankings
40.9

Intelligence index

Outperforms 89% of compared models

76.3

Coding index

Outperforms 94% of compared models

40.2

Agentic index

Outperforms 76% of compared models

Design Arena

Elo scores and win rates across design and agent tasks.

Category performance

1,000-1,500 Elo
3D: 1292; Asciiart: 1269; Code Categories: 1302; Data Visualization: 1273; Game Development: 1303; SVG: 1292; UI Component: 1313; Website: 12993D1292 EloAsciiart1269 EloCode Categories1302 EloData Visualization1273 EloGame Development1303 EloSVG1292 EloUI Component1313 EloWebsite1299 Elo

Model Arena

SVG

1292Elo

Win rate 54.9%

Asciiart

1269Elo

Win rate 52.3%

Code Categories

1302Elo

Win rate 50.4%

UI Component

1313Elo

Win rate 53.9%

Website

1299Elo

Win rate 50.1%

Game Development

1303Elo

Win rate 51%

3D

1292Elo

Win rate 49.9%

Data Visualization

1273Elo

Win rate 51.6%

Agent Arena

Blender3d

1232Elo

Win rate 54.3%

Godotgamedev

1273Elo

Win rate 53%

Mobile Apps

1236Elo

Win rate 54.6%

Fullstack

1244Elo

Win rate 48.1%

Webapps

1239Elo

Win rate 46.9%

Python-pptxslides

1138Elo

Win rate 40.3%

Spreadsheet

1057Elo

Win rate 41.1%

Androidnative

1084Elo

Win rate 33.4%

How Design Arena works

Related Models

Gemini 4 Argon

Gemini 4 Argon

google

Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response.

Context
1.0M
Max output
943.7K
Released
2026-10-13
Input
$2/M
Output
$10/M
Cache read
$0.1/M
Gemini 3.7 Flash

Gemini 3.7 Flash

google

Gemini 3.7 Flash is a multimodal model from Google for fast agentic workflows, coding, and complex multi-step reasoning. It is designed for tasks that require responsive performance and reliable multi-step problem solving.

Context
1.0M
Max output
65.5K
Released
2026-08-13
Input
$0.75/M
Output
$3.75/M
Cache read
$0.075/M
Gemini 3.6 Flash

Gemini 3.6 Flash

google

Gemini 3.6 Flash is a high-efficiency model from Google for coding, agentic workflows, and web and app development. It is designed to produce polished outputs with fewer unnecessary edits and less hedging, while reducing token use and the number of model calls needed to complete a task.

Context
1.0M
Max output
65.5K
Released
2026-07-21
Input
$0.75/M
Output
$3.75/M
Cache read
$0.075/M
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