Google: Gemini 3 Flash Preview

by Google

Gemini 3 Flash Preview is a high speed, high value thinking model designed for agentic workflows, multi turn chat, and coding assistance. It delivers near Pro level reasoning and tool use performance with substantially lower latency than larger Gemini variants, making it well suited for interactive development, long running agent loops, and collaborative coding tasks. Compared to Gemini 2.5 Flash, it provides broad quality improvements across reasoning, multimodal understanding, and reliability. The model supports a 1M token context window and multimodal inputs including text, images, audio, video, and PDFs, with text output. It includes configurable reasoning via thinking levels (minimal, low, medium, high), structured output, tool use, and automatic context caching. Gemini 3 Flash Preview is optimized for users who want strong reasoning and agentic behavior without the cost or latency of full scale frontier models.

Avg Score

91.2%

269 answers

Avg Latency

2.4s

36 runs

Pricing

$0.50

input

$3.00

output

per 1M tokens

Context

1049K

tokens

Alternatives

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No alternatives found

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Other Models from Google

Compare performance with other models from the same creator

Model	Score	Latency	Cost/1M
Google: Gemini 3 Pro Preview	91.5%	10.8s	$7.00
Google: Gemini 2.5 Pro	87.5%	10.0s	$5.63
Google: Gemini 2.5 Flash	80.9%	2.3s	$1.40
Google: Gemini 2.0 Flash	80.0%	2.2s	$0.25
Google: Gemini 2.5 Flash Lite	75.4%	1.7s	$0.25
Google: Nano Banana Pro (Gemini 3 Pro Image Preview)	—	—	$7.00
Google: Gemini 2.5 Flash Image (Nano Banana)	—	—	$1.40
Google: Gemini 2.5 Flash Preview 09-2025	—	—	$1.40
Google: Gemini 2.5 Flash Lite Preview 09-2025	—	—	$0.25
Google: Gemini 2.5 Flash Image Preview (Nano Banana)	—	—	$1.40
Google: Gemma 3n 2B (free)	—	—	$0.0000
Google: Gemini 2.5 Pro Preview 06-05	—	—	$5.63
Google: Gemma 3n 4B	—	—	$0.0000
Google: Gemma 3n 4B	—	—	$0.03
Google: Gemini 2.5 Pro Preview 05-06	—	—	$5.63
Google: Gemma 3 4B	—	—	$0.0000
Google: Gemma 3 4B	—	—	$0.04
Google: Gemma 3 12B	—	—	$0.0000
Google: Gemma 3 12B	—	—	$0.07
Google: Gemma 3 27B	—	—	$0.0000
Google: Gemini 2.0 Flash Lite	—	—	$0.19
Google: Gemini 2.0 Flash Experimental (free)	—	—	$0.0000
Google: Gemma 2 27B	—	—	$0.65
Google: Gemma 2 9B	—	—	$0.06
Google: Gemma 3 27B	—	—	$0.09

Benchmark Performance

How this model performs across different benchmarks

Benchmark	Score	Rank
moose	100.0%	4 / 17
Math Bench: Subtraction (1-10)	100.0%	30 / 35
Enhancing FIDO UV Consistency with UXWG 2026 priority plan	100.0%	7 / 23
Math Bench: Arithmetic (1-100)	100.0%	6 / 35
Product Recommendations	100.0%	2 / 17
Customer service	100.0%	13 / 17
Math Bench: Division (1-10)	100.0%	13 / 35
Testing_website	100.0%	14 / 17
Venture Capital Terms Benchmark	100.0%	6 / 25
Current Global Political Leaders	100.0%	1 / 17
solve n-queens in clojure in one shot	100.0%	1 / 17
Best bathroom technique	100.0%	13 / 17
Breakfast	100.0%	6 / 17
Repetitive Onomatopoeia Response Benchmark	100.0%	13 / 17
Math Bench: Addition (1-10)	100.0%	30 / 35
Customer support	100.0%	1 / 17
Cheeky_test	100.0%	13 / 17
Customer Support Ticket Generation	100.0%	13 / 17
Math Bench: Multiplication (1-10)	100.0%	30 / 35
Structured Customer Support Extraction	100.0%	13 / 17
Smell test	100.0%	14 / 17
SaaS Customer Support Extraction and Strategy	100.0%	13 / 17
Math Bench: Arithmetic (1-1000)	100.0%	9 / 35
Spatial Reasoning: Germany	95.0%	4 / 35
KUKA force Torque control programming skills	95.0%	5 / 17
Motor production test	95.0%	27 / 36
Rust performance	90.0%	1 / 16
Money Boy Cultural Literacy Test	90.0%	1 / 35
Karlsruhe Local Knowledge Benchmark	89.6%	6 / 35
Product Recommendations	85.6%	5 / 36
Character Frequency Bench	85.5%	18 / 35
German Memelord Bench	65.3%	2 / 35
Define data taxonomy for product catalog suitable for PIM	50.0%	14 / 17
Karl Lorey knowlege	33.3%	1 / 36

Price vs Performance

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X-axis uses log scale for better visualization of price range

Score Over Time

Performance trends across all benchmark runs

Benchmark Activity

Number of benchmark runs over time

Quickstart

Get started with this model using OpenRouter

View on OpenRouter

import { OpenRouter } from "@openrouter/sdk";

const openrouter = new OpenRouter({
  apiKey: "<OPENROUTER_API_KEY>"
});

const completion = await openrouter.chat.completions.create({
  model: "google/gemini-3-flash-preview",
  messages: [
    {
      role: "user",
      content: "Hello!"
    }
  ]
});

console.log(completion.choices[0].message.content);

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