Gemini 2.5 Flash Lite
Gemini 2.5 Flash Lite is the fastest and most affordable model in the Gemini 2.5 family, with configurable thinking, a context window of 1.0M tokens, and benchmark improvements over 2.0 Flash-Lite across coding, math, and science, at a price designed for high-throughput agentic pipelines.
View API reference- Input and output price
- Prices from: Input $0.10, Output $0.40, Per 1M tokens
- 24h uptime
- Loading AI Gateway uptime
import { streamText } from 'ai'
const result = streamText({ model: 'google/gemini-2.5-flash-lite', prompt: 'Why is the sky blue?'})Copy link to headingPlayground
Try out Gemini 2.5 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 2.5 Flash Lite
Gemini 2.5 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.
Copy link to headingUptime24 hours
Direct request success rate on AI Gateway and per-provider. Visit the docs for more info.
Copy link to headingThroughput24 hours
P50 throughput on live AI Gateway traffic, in tokens per second (TPS). Visit the docs for more info.
Copy link to headingLatency24 hours
P50 time to first token (TTFT) on live AI Gateway traffic, in milliseconds. View the docs for more info.
Getting started
Call Gemini 2.5 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.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'google/gemini-2.5-flash-lite', prompt: 'Why is the sky blue?', });
console.log(result.text);}
main().catch(console.error);Top-level parameters
The same Gemini 2.5 Flash Lite request in each API format AI Gateway supports.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'google/gemini-2.5-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-2.5-flash-lite. AI Gateway routes the request to an available provider. |
maxOutputTokens | number | No | Hard cap on generated tokens. Gemini 2.5 Flash Lite supports up to 65,536 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 |
| — | 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.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'google/gemini-2.5-flash-lite', 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.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'google/gemini-2.5-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.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'google/gemini-2.5-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.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'google/gemini-2.5-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.
import { generateText, tool } from 'ai';import { z } from 'zod';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'google/gemini-2.5-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 |
|---|
| 1M | 1.9 s | 412 tps | $0.75/M | $3.75/M | Read$0.08/M | $14/K+1 more | +3 |  | 09/02/2026 | |||||
| 1M | 0.8 s | 280 tps | $0.75/M | $3.75/M | Read$0.08/M | $14/K+1 more | +3 | | 08/13/2026 | |||||
| 1M | 0.5 s | 302 tps | $0.30/M | $2.50/M | Read$0.03/M | $14/K+1 more | +3 | | 07/21/2026 | |||||
| 1M | 1.8 s | 192 tps | $1.50/M | $9/M | Read$0.15/M | $14/K+1 more | +3 | | 05/19/2026 | |||||
| 1M | 0.6 s | 257 tps | $0.25/M | $1.50/M | Read$0.03/M | $14/K+1 more | +3 | | 05/07/2026 | |||||
| 1M | 0.6 s | 197 tps | $0.50/M+1 more | $3/M+1 more | Read$0.05/M | $14/K+1 more | +3 | | 12/17/2025 |
Copy link to headingAbout Gemini 2.5 Flash Lite
Gemini 2.5 Flash Lite is the efficiency tier of the Gemini 2.5 family, released June 17, 2025 alongside 2.5 Flash and 2.5 Pro going to general availability. It runs faster and costs less than any other 2.5 model while outperforming 2.0 Flash-Lite on benchmarks that matter for real-world developer tasks: coding, mathematics, scientific reasoning, and instruction following.
Configurable thinking is the feature that most distinguishes Gemini 2.5 Flash Lite from 2.0 Flash-Lite. At inference time, you set a thinking level (minimal, low, medium, or high) to allocate more deliberation to harder problems without switching endpoints. This is the same thinking mechanism available in 2.5 Flash and 2.5 Pro, scaled down to the lite budget. For tasks that occasionally need more reasoning depth than a pure speed-first model provides, the thinking toggle avoids the cost jump of routing to a full reasoning model.
For teams running 2.0 Flash-Lite in production and evaluating a 2.5 upgrade path, Gemini 2.5 Flash Lite is the migration-friendly choice: better benchmark performance, thinking capability, and latency that matches or beats the previous generation.
Copy link to headingWhat To Consider When Choosing a Provider
- Configuration: Applications using the thinking feature should benchmark total token cost under realistic thinking budgets, as thinking tokens contribute to output costs.
- 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 2.5 Flash Lite
Best for
- High-volume agentic pipelines needing occasional reasoning: The thinking toggle allows selective deliberation on harder steps without paying full 2.5 Flash prices for every call in the pipeline
- Migrating from 2.0 Flash-Lite: Benchmark improvements across coding and math mean the upgrade delivers measurable quality gains on common developer tasks at comparable cost
- Latency-sensitive applications within the 2.5 family: When 2.5 Flash or 2.5 Pro latency is too high for the user experience, Flash-Lite provides 2.5-generation quality at the fastest 2.5 response times
- Translation, classification, and data extraction at scale: Strong instruction following and fast response make it a reliable workhorse for structured-output production tasks
Consider alternatives when
- Maximum reasoning depth is required: 2.5 Flash or 2.5 Pro with uncapped thinking budget is more appropriate for the most complex multi-step problems
- Image generation is needed: Gemini 2.5 Flash Lite does not generate images. Gemini models with native image output are available in the 2.5 Flash Image and 3.x families
- Your workload is pure annotation/extraction without reasoning: For text-output-only extraction at maximum cost efficiency, 2.0 Flash-Lite's lower price floor may be preferable
Copy link to headingConclusion
Gemini 2.5 Flash Lite closes the gap between 2.0 Flash-Lite and the full 2.5 Flash tier. It delivers better benchmark performance and thinking capability at the same latency profile teams already depend on. For 2.0 Flash-Lite users, it's the natural upgrade.
Copy link to headingFrequently Asked Questions
What thinking levels does Gemini 2.5 Flash Lite support?
Four levels: minimal, low, medium, and high. You set the level per request. Thinking tokens are added to output token count, so higher thinking levels increase both quality and cost.
How does Gemini 2.5 Flash Lite compare to 2.0 Flash-Lite in benchmark performance?
Gemini 2.5 Flash Lite shows cross-category benchmark improvements in coding, mathematics, science, and reasoning over 2.0 Flash-Lite.
Does Gemini 2.5 Flash Lite support image and audio inputs?
Yes, the model accepts multimodal inputs including images, audio, and documents alongside text, within the context window of 1.0M tokens.
What is the latency profile compared to 2.5 Flash?
Gemini 2.5 Flash Lite is the fastest and lowest-latency model in the 2.5 family. It provides 2.5-generation capability with first-token times lower than 2.5 Flash or 2.5 Pro.
When does it make sense to use thinking in Gemini 2.5 Flash Lite?
When a subset of requests in a pipeline hits problems that need more deliberation, such as math problems, multi-step instructions, or ambiguous classification. Setting thinking to minimal for routine requests and medium for flagged hard ones keeps average cost low.
How do I use Gemini 2.5 Flash Lite on AI Gateway?
Use the identifier
google/gemini-2.5-flash-litewith any supported interface. Set thinking level via provider options in the AI SDK or via request parameters in direct API calls.