What makes Gemini 3 Flash different from Gemini 2.5 Flash?

Gemini 3 Flash is built on the newer Gemini 3 architecture rather than Gemini 2.5. The generation change brings a substantial capability lift: Gemini 3 Flash surpasses Gemini 2.5 Pro on most benchmarks, so a speed-tier model in the 3 generation now exceeds the previous generation's flagship.

Can I control how much the model thinks before answering?

Yes. You can set `thinkingLevel` (e.g., `'high'`) and `includeThoughts: true` inside `providerOptions.google` when using the AI SDK. This gives you visibility into intermediate reasoning steps.

Does Gemini 3 Flash support streaming?

Yes. Use `streamText` from the AI SDK with `model: 'google/gemini-3-flash'` for streaming responses.

Do I need a Google Cloud account to use this model on AI Gateway?

No. AI Gateway handles all provider authentication. You authenticate to AI Gateway using a Vercel API key or OIDC token and do not need to configure Google credentials separately.

How does Gemini 3 Flash compare to Gemini 3 Pro on reasoning tasks?

Gemini 3 Pro targets the most challenging reasoning and agentic workflows. Gemini 3 Flash prioritizes speed and cost while still delivering pro-grade quality. The right tradeoff depends on your latency budget and task complexity.

What is Zero Data Retention and does Gemini 3 Flash support it?

Yes, Zero Data Retention is available for this model. Zero Data Retention is offered on a per-provider basis. See https://vercel.com/docs/ai-gateway/capabilities/zdr for details.

What token efficiency improvements does Gemini 3 Flash offer?

Gemini 3 Flash uses fewer tokens than the previous Gemini 2.5 generation on comparable tasks. Combined with lower per-token pricing, this lowers cost at scale for applications processing large request volumes.

Is Gemini 3 Flash suitable for agentic multi-step workflows?

Yes. The model's combination of reasoning capability, token efficiency, and low latency makes it well-suited for agents that execute multiple tool calls or reasoning steps in sequence within a budget-constrained environment.

Google

Gemini 3 Flash

Gemini 3 Flash delivers Gemini 3's pro-grade reasoning at flash-level latency and cost, outperforming Gemini 2.5 Pro across most benchmarks with meaningful gains in token efficiency.

View API reference

Input and output price: Prices from: Input $0.50, Output $3, Per 1M tokens
24h uptime: Loading AI Gateway uptime

Get API key Read the docs

import { streamText } from 'ai'

const result = streamText({
  model: 'google/gemini-3-flash',
  prompt: 'Why is the sky blue?'
})

Read docs

Overview Playground Providers Uptime Status Throughput Latency API Similar About FAQ

Copy link to headingPlayground

Try out Gemini 3 Flash by Google. Usage is billed to your team at API rates. Free users (those who haven't made a payment) get $5 of credits every 30 days.

Gemini 3 Flash

Copy link to headingProviders

Route requests across multiple providers. Copy a provider slug to set your preference. Visit the docs for more info. Using a provider means you agree to their terms, listed under Legal.

Provider

Context	Max Output	Latency	Throughput	Input	Output	Cache	Web Search	Capabilities	ZDR	No Training	Free Tier	Release Date

Google Vertex AI

Legal:Terms•Privacy

65K

0.6 s

128 tps

$0.50/M+3 more

$3/M+3 more

Read$0.05/M

$14/K+1 more

12/17/2025

Google

Legal:Terms•Privacy

64K

0.6 s

197 tps

$0.50/M+3 more

$3/M+3 more

Read$0.05/M

$14/K+1 more

12/17/2025

Getting started

Call Gemini 3 Flash through AI Gateway with the AI SDK generateText and streamText functions, or through the OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages APIs by changing the base URL. AI Gateway authenticates the request and routes it to an available provider.

Install the AI SDK (pnpm add ai dotenv), create an API key from the API Keys page, and set it as AI_GATEWAY_API_KEY in your environment. Full setup is covered in the text generation quickstart.

index.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'google/gemini-3-flash',
    prompt: 'Why is the sky blue?',
  });

  console.log(result.text);
}

main().catch(console.error);

Top-level parameters

The same Gemini 3 Flash request in each API format AI Gateway supports.

top-level-params.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'google/gemini-3-flash',
    system: 'You are a concise technical assistant.',
    prompt: 'Summarize the tradeoffs between static generation and SSR.',
    maxOutputTokens: 1024,
  });

  console.log(result.text);
}

main().catch(console.error);

Standard parameters like prompt, messages, temperature, and tools work as documented in the AI SDK docs. These are the parameters with model-specific behavior.

Parameter	Type	Required	Description
`model`	`string`	Yes	Model ID in the form `creator/model`, e.g. `google/gemini-3-flash`. AI Gateway routes the request to an available provider.
`maxOutputTokens`	`number`	No	Hard cap on generated tokens. Gemini 3 Flash supports up to `65,000` output tokens. Reasoning tokens count toward this limit.
`reasoning`	`'provider-default' \| 'none' \| 'minimal' \| 'low' \| 'medium' \| 'high' \| 'xhigh'`	No	Provider-agnostic reasoning effort, available in AI SDK 7 or later. Maps to the provider’s native reasoning configuration; reasoning settings under `providerOptions` take precedence when both are set. See the Reasoning section below.
`providerOptions`	`Record<string, JSONValue>`	No	AI Gateway routing options under `gateway`, plus any provider-native options under the provider’s own namespace — see the table below.

Input limits

Input	Formats	Sources	Max count	Max size	Limits
Text	—	—	—	—	Prompt and response share the 1M-token context window
Image	—	URL, base64, Uint8Array	—	—	Sent as image parts in messages; counts as input tokens
PDF	—	URL, base64, Uint8Array	—	—	Sent as file parts in messages; counts as input tokens

Provider options

Set AI Gateway routing options under providerOptions.gateway. For provider-specific options, pass them under the provider’s namespace as documented by the AI SDK.

Learn more in the AI SDK google provider docs.

provider-options.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'google/gemini-3-flash',
    prompt: 'Why is the sky blue?',
    providerOptions: {
      gateway: {
        only: ['vertex', 'google'],
      },
    },
  });

  console.log(result.text);
}

main().catch(console.error);

These AI Gateway routing options apply to every model. Provider-specific options pass through under the provider’s own namespace (for example providerOptions.anthropic) exactly as documented by the AI SDK.

Parameter	Type	Required	Description
`providerOptions.gateway.only`	`string[]`	No	Restrict routing to these provider slugs. Requests fail over only within the listed providers.
`providerOptions.gateway.order`	`string[]`	No	Preferred provider order. Listed providers are tried first; unlisted providers remain available as fallbacks.
`providerOptions.gateway.sort`	`'cost' \| 'ttft' \| 'tps'`	No	Rank candidate providers by price, time to first token, or tokens per second instead of the default routing order.
`providerOptions.gateway.zeroDataRetention`	`boolean`	No	Route only to providers with a zero-data-retention policy for this model.

Routing across providers

AI Gateway serves the same model through multiple providers and fails over automatically. order expresses a preference while keeping every provider eligible; only is a hard allowlist — if none of the listed providers are available the request fails instead of falling back.

Options under a provider's own namespace (for example providerOptions.anthropic) are forwarded to that provider with the request. Providers ignore option namespaces that don't apply to them, so it is safe to set provider options alongside gateway routing options.

Reasoning

AI Gateway bridges reasoning across every API format. The AI SDK exposes a provider-agnostic top-level reasoning level (none, minimal, low, medium, high, or xhigh); the Chat Completions and Responses formats take the same effort under reasoning.effort; and the Anthropic Messages format uses a native thinking token budget. Whichever you send, the gateway maps it to the target model’s native configuration, converting between effort levels and token budgets as needed. Reasoning-related settings under providerOptions take full precedence over the top-level reasoning value and are never merged. Reasoning tokens typically count toward your output-token usage, though how they’re reported and billed varies by provider.

Learn more in the AI Gateway reasoning guide.

reasoning.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'google/gemini-3-flash',
    prompt: 'Explain the Monty Hall problem step by step.',
    reasoning: 'high',
  });

  console.log(result.text);
}

main().catch(console.error);

Image input

Send images alongside text as message parts. Images count as input tokens.

image-input.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'google/gemini-3-flash',
    messages: [
      {
        role: 'user',
        content: [
          { type: 'text', text: 'Describe this image.' },
          { type: 'image', image: 'https://example.com/photo.jpg' },
        ],
      },
    ],
  });

  console.log(result.text);
}

main().catch(console.error);

PDF input

Attach PDFs as file parts. Their contents count as input tokens.

pdf-input.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'google/gemini-3-flash',
    messages: [
      {
        role: 'user',
        content: [
          { type: 'text', text: 'Summarize this document.' },
          {
            type: 'file',
            mediaType: 'application/pdf',
            data: 'https://example.com/document.pdf',
          },
        ],
      },
    ],
  });

  console.log(result.text);
}

main().catch(console.error);

Tool calling

Expose tools the model can call. Define each tool’s inputs with a Zod schema.

tool-calling.ts

import { generateText, tool } from 'ai';
import { z } from 'zod';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'google/gemini-3-flash',
    prompt: 'What is the weather in San Francisco?',
    tools: {
      getWeather: tool({
        description: 'Get the current weather for a location',
        inputSchema: z.object({ location: z.string() }),
        execute: async ({ location }) => ({ location, temperatureC: 18 }),
      }),
    },
  });

  console.log(result.text);
}

main().catch(console.error);

Copy link to headingMore models by Google

Model

Context	Latency	Throughput	Input	Output	Cache	Web Search	Capabilities	Providers	ZDR	No Training	Free Tier	Release Date

google/gemini-3.8-flash

1.9 s

412 tps

$0.75/M

$3.75/M

Read$0.08/M

$14/K+1 more

09/02/2026

google/gemini-3.7-flash

0.8 s

280 tps

$0.75/M

$3.75/M

Read$0.08/M

$14/K+1 more

08/13/2026

google/gemini-3.5-flash-lite

0.5 s

302 tps

$0.30/M

$2.50/M

Read$0.03/M

$14/K+1 more

07/21/2026

google/gemini-3.5-flash

1.8 s

192 tps

$1.50/M

$9/M

Read$0.15/M

$14/K+1 more

05/19/2026

google/gemini-3.1-flash-lite

0.6 s

257 tps

$0.25/M

$1.50/M

Read$0.03/M

$14/K+1 more

05/07/2026

google/gemini-2.5-flash-lite

0.2 s

392 tps

$0.10/M

$0.40/M

Read$0.01/M

$35/K+1 more

06/17/2025

Copy link to headingAbout Gemini 3 Flash

Gemini 3 Flash is Google's speed-optimized model in the Gemini 3 generation, combining Gemini 3's reasoning depth with the efficiency profile of the Flash tier. It outperforms Gemini 2.5 Pro across most benchmarks, meaning a speed-tier model now surpasses a previous-generation flagship. Gemini 3 Flash achieves this with meaningful gains in token efficiency over the 2.5 generation. See live metrics on this page for current throughput.

Thinking is first-class in Gemini 3 Flash. The thinkingLevel and includeThoughts provider options let you surface intermediate reasoning steps. This helps when debugging multi-step pipelines, constructing chain-of-thought datasets, or validating that Gemini 3 Flash reasons through a problem correctly. Set thinkingLevel to high when the task demands deeper inference and your latency budget allows it.

Because Gemini 3 Flash sits at the intersection of quality and throughput, it fits a wide range of real-world traffic patterns, from low-latency chat interfaces to batch document processing pipelines. Accessing it through AI Gateway adds observability, automatic retries, and provider failover without requiring a Google Cloud account.

Copy link to headingWhat To Consider When Choosing a Provider

Configuration: Gemini 3 Flash supports configurable thinking levels (high included) via providerOptions, giving you direct control over how much reasoning compute the model applies per request.
Zero Data Retention: Zero Data Retention is available for this model. It is offered on a per-provider and model basis. See the documentation for details.
Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.

Copy link to headingWhen to Use Gemini 3 Flash

Best for

Real-time chat and assistants: Interfaces that require pro-level reasoning without high latency
High-volume agentic pipelines: Per-token cost directly affects operating expenses
Step-by-step analysis: Tasks where surfacing intermediate reasoning (includeThoughts) adds value
Throughput-bottlenecked apps: Applications previously constrained by Gemini 2.5 Pro throughput limits
Cost-sensitive production workloads: Production traffic where per-token cost matters but quality still has to stay benchmark-competitive

Consider alternatives when

Maximum reasoning depth: Your task requires the deepest reasoning regardless of cost or speed (consider google/gemini-3-pro-preview or google/gemini-3.1-pro-preview)
Native image generation needed: You require image output alongside text (consider google/gemini-3-pro-image or google/gemini-3.1-flash-image-preview)
Budget and latency dominate: Task quality requirements are low (consider google/gemini-3.1-flash-lite-preview)

Copy link to headingConclusion

Gemini 3 Flash resets expectations for what a speed-tier model can deliver, matching or exceeding previous-generation Pro quality at a fraction of the cost and latency. For teams that need scalable intelligence rather than raw capability, it represents a cost- and latency-efficient entry point into the Gemini 3 generation on AI Gateway.

Copy link to headingFrequently Asked Questions

What makes Gemini 3 Flash different from Gemini 2.5 Flash?
Gemini 3 Flash is built on the newer Gemini 3 architecture rather than Gemini 2.5. The generation change brings a substantial capability lift: Gemini 3 Flash surpasses Gemini 2.5 Pro on most benchmarks, so a speed-tier model in the 3 generation now exceeds the previous generation's flagship.
Can I control how much the model thinks before answering?
Yes. You can set thinkingLevel (e.g., 'high') and includeThoughts: true inside providerOptions.google when using the AI SDK. This gives you visibility into intermediate reasoning steps.
Does Gemini 3 Flash support streaming?
Yes. Use streamText from the AI SDK with model: 'google/gemini-3-flash' for streaming responses.
Do I need a Google Cloud account to use this model on AI Gateway?
No. AI Gateway handles all provider authentication. You authenticate to AI Gateway using a Vercel API key or OIDC token and do not need to configure Google credentials separately.
How does Gemini 3 Flash compare to Gemini 3 Pro on reasoning tasks?
Gemini 3 Pro targets the most challenging reasoning and agentic workflows. Gemini 3 Flash prioritizes speed and cost while still delivering pro-grade quality. The right tradeoff depends on your latency budget and task complexity.
What is Zero Data Retention and does Gemini 3 Flash support it?
Yes, Zero Data Retention is available for this model. Zero Data Retention is offered on a per-provider basis. See https://vercel.com/docs/ai-gateway/capabilities/zdr for details.
What token efficiency improvements does Gemini 3 Flash offer?
Gemini 3 Flash uses fewer tokens than the previous Gemini 2.5 generation on comparable tasks. Combined with lower per-token pricing, this lowers cost at scale for applications processing large request volumes.
Is Gemini 3 Flash suitable for agentic multi-step workflows?
Yes. The model's combination of reasoning capability, token efficiency, and low latency makes it well-suited for agents that execute multiple tool calls or reasoning steps in sequence within a budget-constrained environment.

Your use is subject to Google's Terms & Privacy Policies.

Agent Stack

Core Platform

Tools

Learn

Build

Explore

Gemini 3 Flash

Copy link to headingPlayground

Copy link to headingProviders

Copy link to headingUptime24 hours

Copy link to headingThroughput24 hours

Copy link to headingLatency24 hours

Getting started

Top-level parameters

Input limits

Provider options

Routing across providers

Reasoning

Image input

PDF input

Tool calling

Copy link to headingMore models by Google

Copy link to headingAbout Gemini 3 Flash

Copy link to headingWhat To Consider When Choosing a Provider

Copy link to headingWhen to Use Gemini 3 Flash

Best for

Consider alternatives when

Copy link to headingConclusion

Copy link to headingFrequently Asked Questions