What changed in Gemini 3.5 Flash Lite compared to Gemini 3.1 Flash-Lite?

Gemini 3.5 Flash Lite outperforms Gemini 3.1 Flash-Lite across thinking levels, most visibly on agentic work. It scores 54% on Terminal-Bench 2.1 against 31%, 72.2% on GDM-MRCR v2 against 60.1%, and 1140 on GDPval-AA v2 against 642. Computer use also ships as a built-in tool.

Why is Gemini 3.5 Flash Lite described as a subagent model?

The upgraded agentic capability lets it handle a scoped part of a larger task reliably. A master agent on a heavier model decomposes the work, then dispatches bounded steps such as field extraction, batch translation, or JSON parsing to Gemini 3.5 Flash Lite, where per-step cost stays low enough to fan out widely.

How do thinking levels affect cost on Gemini 3.5 Flash Lite?

Gemini 3.5 Flash Lite defaults to `minimal`. Higher levels add reasoning compute that counts toward output tokens, so each step up raises per-request cost. Use `minimal` and `low` for high-volume, latency-sensitive execution, and higher levels for multi-step subagent workloads.

Does Gemini 3.5 Flash Lite beat Gemini 3 Flash on any tasks?

Yes, on several agentic and coding evaluations. Gemini 3.5 Flash Lite scores 54.2% on SWE-Bench Pro against 49.6% for Gemini 3 Flash, and 74.0% on OSWorld-Verified against 65.1%. Compare on your own workload before you move production traffic between tiers.

Does Gemini 3.5 Flash Lite support computer use?

Yes, as a built-in tool. That lets a subagent step drive a browser or an application surface instead of only processing text, which is what makes scoped delegation practical at this tier.

How much does Gemini 3.5 Flash Lite cost on AI Gateway?

Current rates are $0.3 per million input tokens and $2.50 per million output tokens, listed on this page and subject to provider updates. AI Gateway reflects provider pricing with no markup and charges no platform fee on inference.

How do I call Gemini 3.5 Flash Lite on AI Gateway?

Set the model to `google/gemini-3.5-flash-lite` using the AI SDK, the OpenAI-compatible Chat Completions endpoint, the Responses API, or another supported interface. AI Gateway handles provider routing, retries, and failover automatically.

When should I use `google/gemini-3.6-flash` instead?

Use `google/gemini-3.6-flash` for the planning layer, hard coding tasks, and multimodal knowledge work. Use Gemini 3.5 Flash Lite for the high-volume steps underneath it, where the number of calls rather than the difficulty of any one call drives your bill.

How does Zero Data Retention work with Gemini 3.5 Flash Lite through AI Gateway?

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.

Google

Gemini 3.5 Flash Lite

Gemini 3.5 Flash Lite upgrades the agentic capability of the Flash-Lite tier, outperforming Gemini 3.1 Flash-Lite across thinking levels on coding, long context, and real-world task execution, with built-in computer use and a minimal default thinking level for high-throughput work.

View API reference

Input and output price: Prices from: Input $0.30, Output $2.50, 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-3.5-flash-lite',
  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 3.5 Flash Lite 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 3.5 Flash Lite

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	Regional Inference	Free Tier	Release Date

Google

Legal:Terms•Privacy

65K

0.5 s

288 tps

$0.30/M+2 more

$2.50/M+2 more

Read$0.03/M

$14/K+1 more

07/21/2026

Google Vertex AI

Legal:Terms•Privacy

65K

0.7 s

302 tps

$0.30/M+4 more

$2.50/M+4 more

Read$0.03/M

$14/K+1 more

07/21/2026

Getting started

Call Gemini 3.5 Flash Lite 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-3.5-flash-lite',
    prompt: 'Why is the sky blue?',
  });

  console.log(result.text);
}

main().catch(console.error);

Top-level parameters

The same Gemini 3.5 Flash Lite 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-3.5-flash-lite',
    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-3.5-flash-lite`. AI Gateway routes the request to an available provider.
`maxOutputTokens`	`number`	No	Hard cap on generated tokens. Gemini 3.5 Flash Lite 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-3.5-flash-lite',
    prompt: 'Why is the sky blue?',
    providerOptions: {
      gateway: {
        only: ['google', 'vertex'],
      },
    },
  });

  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-3.5-flash-lite',
    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-3.5-flash-lite',
    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-3.5-flash-lite',
    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-3.5-flash-lite',
    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

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-3-flash

0.6 s

197 tps

$0.50/M+1 more

$3/M+1 more

Read$0.05/M

$14/K+1 more

12/17/2025

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 3.5 Flash Lite

Gemini 3.5 Flash Lite is the efficiency tier of the Gemini 3.5 family, released on July 21, 2026. Google built it for scaling agentic systems rather than for peak single-turn quality. Across thinking levels, Gemini 3.5 Flash Lite outperforms Gemini 3.1 Flash-Lite, and the practical effect is that a Flash-Lite model can now sit inside an agent graph instead of serving only one-shot classification.

The coding and agentic gains are wide. Gemini 3.5 Flash Lite scores 54% on Terminal-Bench 2.1 against 31% for Gemini 3.1 Flash-Lite, 72.2% on GDM-MRCR v2 for long context against 60.1%, and 1140 on GDPval-AA v2 for real-world task execution against 642. On several agentic and coding evaluations it also passes Gemini 3 Flash, including 54.2% on SWE-Bench Pro against 49.6% and 74.0% on OSWorld-Verified against 65.1%. Teams running workloads on the 2.5 or 3 Flash tiers have a cheaper option that scores higher on those tasks.

Subagent execution is the pattern Google highlights. A master agent on a heavier model, such as google/gemini-3.6-flash, decomposes a task and hands scoped pieces to Gemini 3.5 Flash Lite: read this document set, extract these fields, translate this batch, parse this JSON into a schema. Each step stays cheap and fast, so you can fan out widely without aggregate token cost dictating the architecture. Computer use ships as a built-in tool, so a scoped step can include driving a browser rather than only reading text.

Gemini 3.5 Flash Lite accepts multimodal input within a context window of 1M tokens and returns text, up to 65K tokens. Check the Specs table on this page for the current modality list. Running Gemini 3.5 Flash Lite through AI Gateway adds usage and cost tracking, automatic retries, and provider failover on one API surface, which is what keeps a wide fan-out of subagent calls observable.

Copy link to headingWhat To Consider When Choosing a Provider

Configuration: Gemini 3.5 Flash Lite defaults to the minimal thinking level. Keep it there for high-volume extraction, classification, and translation, where latency and per-token cost decide whether the workload is viable at all. Move to low or a higher level when a subagent has to plan across several steps and stopping early would cost you a retry. Thinking tokens count toward output tokens, so the level you set changes what each request costs. Measure total spend under the thinking levels you actually plan to run.
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 3.5 Flash Lite

Best for

Scoped Subagent Execution: A heavier master agent hands off bounded extraction, translation, or parsing steps
High-Volume Document Processing: Per-token cost decides how many documents a pipeline can afford to process
Agentic Search Pipelines: Query planning and result triage run across many parallel calls
Structured JSON Parsing: Schema-conformant output arrives from the cheapest tier in the Gemini 3.5 family
Long-Context Extraction: GDM-MRCR v2 results improved substantially over Gemini 3.1 Flash-Lite

Consider alternatives when

Master Agent Reasoning: google/gemini-3.6-flash handles the planning and hard coding steps in a multi-agent setup
Sustained Multi-Step Analysis: google/gemini-3.1-pro-preview carries more reasoning depth for difficult problems
Native Image Output: google/gemini-3.1-flash-lite-image generates and edits images at a comparable tier
Semantic Retrieval Workloads: A dedicated embedding model like google/gemini-embedding-2 fits search and clustering better

Copy link to headingConclusion

Gemini 3.5 Flash Lite makes the Flash-Lite tier viable inside agent architectures. It beats Gemini 3.1 Flash-Lite across thinking levels and passes Gemini 3 Flash on several agentic and coding evaluations, so scoped subagents, document pipelines, and high-throughput extraction can run on the cheapest tier in the Gemini 3.5 family.

Copy link to headingFrequently Asked Questions

What changed in Gemini 3.5 Flash Lite compared to Gemini 3.1 Flash-Lite?
Gemini 3.5 Flash Lite outperforms Gemini 3.1 Flash-Lite across thinking levels, most visibly on agentic work. It scores 54% on Terminal-Bench 2.1 against 31%, 72.2% on GDM-MRCR v2 against 60.1%, and 1140 on GDPval-AA v2 against 642. Computer use also ships as a built-in tool.
Why is Gemini 3.5 Flash Lite described as a subagent model?
The upgraded agentic capability lets it handle a scoped part of a larger task reliably. A master agent on a heavier model decomposes the work, then dispatches bounded steps such as field extraction, batch translation, or JSON parsing to Gemini 3.5 Flash Lite, where per-step cost stays low enough to fan out widely.
How do thinking levels affect cost on Gemini 3.5 Flash Lite?
Gemini 3.5 Flash Lite defaults to minimal. Higher levels add reasoning compute that counts toward output tokens, so each step up raises per-request cost. Use minimal and low for high-volume, latency-sensitive execution, and higher levels for multi-step subagent workloads.
Does Gemini 3.5 Flash Lite beat Gemini 3 Flash on any tasks?
Yes, on several agentic and coding evaluations. Gemini 3.5 Flash Lite scores 54.2% on SWE-Bench Pro against 49.6% for Gemini 3 Flash, and 74.0% on OSWorld-Verified against 65.1%. Compare on your own workload before you move production traffic between tiers.
Does Gemini 3.5 Flash Lite support computer use?
Yes, as a built-in tool. That lets a subagent step drive a browser or an application surface instead of only processing text, which is what makes scoped delegation practical at this tier.
Which inputs does Gemini 3.5 Flash Lite accept?
Gemini 3.5 Flash Lite accepts multimodal input within a context window of 1M tokens and returns text, up to 65K tokens. See the Specs table on this page for the current modality list.
How much does Gemini 3.5 Flash Lite cost on AI Gateway?
Current rates are $0.3 per million input tokens and $2.50 per million output tokens, listed on this page and subject to provider updates. AI Gateway reflects provider pricing with no markup and charges no platform fee on inference.
How do I call Gemini 3.5 Flash Lite on AI Gateway?
Set the model to google/gemini-3.5-flash-lite using the AI SDK, the OpenAI-compatible Chat Completions endpoint, the Responses API, or another supported interface. AI Gateway handles provider routing, retries, and failover automatically.
When should I use google/gemini-3.6-flash instead?
Use google/gemini-3.6-flash for the planning layer, hard coding tasks, and multimodal knowledge work. Use Gemini 3.5 Flash Lite for the high-volume steps underneath it, where the number of calls rather than the difficulty of any one call drives your bill.
How does Zero Data Retention work with Gemini 3.5 Flash Lite through AI Gateway?
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.

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

Agent Stack

Core Platform

Tools

Learn

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Gemini 3.5 Flash Lite

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 3.5 Flash Lite

Copy link to headingWhat To Consider When Choosing a Provider

Copy link to headingWhen to Use Gemini 3.5 Flash Lite

Best for

Consider alternatives when

Copy link to headingConclusion

Copy link to headingFrequently Asked Questions