Gemini 2.5 Flash-Lite
Gemini 2.5 Flash-Lite is the lightweight, fastest, and most budget-friendly multimodal model in Google's Gemini 2.5 family, built for ultra-low-latency, cost-sensitive workloads.
input · usd / 1m tok
$0.10
output · usd / 1m tok
$0.40
context window
1mtokens
modalities · in → out
takes text, image, audio and video in, gives text out
pricing
base api ratesusd per 1m tokens
- input
- $0.10
- output
- $0.40
- cache read
- $0.01
- audio input
- $0.30
providers
Google hosts Gemini 2.5 Flash-Lite. Impossibl routes your request intelligently and handles failover automatically when a provider is down.
- Google
specifications
takes text, image, audio and video in, gives text outgoogle/gemini-2.5-flash-lite
- creatorGoogle
- model typeText generation
- context window1,000,000 tokens
- maximum outputnot published
- input
takes text, image, audio and video in, gives text out
- outputtext
- tool callingnot verified
- releasedjul 22, 2025
- endpoint
/v1/chat/completions
unknown means the public catalog does not establish that fact. a model's documented capabilities can differ from what a particular api supports. see the model documentation for integration details.
try it · your prompt, this model, live
# responses API
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["IMPOSSIBL_API_KEY"],
base_url="https://api.impossibl.com/v1",
)
resp = client.responses.create(
model="google/gemini-2.5-flash-lite",
input="",
)
print(resp.output_text)
edit the prompt, then run
benchmarks
intelligence index
6.7#87 of 90 models on the gateway
- omniscience-58.8#84 of 86
- gpqa47.4%#86 of 88
- humanity’s last exam3.7%#86 of 89
- MMMU-Pro54.0%
- GPQA47.4%
- LiveCodeBench40.0%
- AIME 202535.3%
- AA-LCR32.0%
- IFBench31.5%
- Tau219.0%
- Humanity’s Last Exam3.7%
- Terminal-Bench Hard2.3%
- CritPt0.0%
- Intelligence Index6.67
- Omniscience Index-58.78
- intelligence index (API)6.7
each configuration is listed separately; these evaluations describe the tested model configuration, and performance and costs can differ on impossibl. unmeasured scores are omitted and api values keep their source units. last checked 2026-09-11.
compare Gemini 2.5 Flash-Lite with other models
frontier labs and open weights, best-ranked firstusd / 1m tok · in / out
- anthropic/claude-fable-5-1$10.00 / $50.00compare →
- openai/gpt-6-astra$10.00 / $50.00compare →
- meta/muse-spark-1.3$1.25 / $4.25compare →
- zai/glm-5.3$1.40 / $4.40compare →
frequently asked questions
What is Gemini 2.5 Flash-Lite?
Gemini 2.5 Flash-Lite is the lightweight, fastest, and most budget-friendly multimodal model in Google's Gemini 2.5 family, built for ultra-low-latency, cost-sensitive workloads. It trades some capability for throughput, with faster token generation than Gemini 2.5 Flash while keeping the family's optional reasoning mode.
How much does Gemini 2.5 Flash-Lite cost?
Gemini 2.5 Flash-Lite costs $0.10 per million input tokens and $0.40 per million output tokens through the Impossibl API. Cache reads cost $0.01 per million tokens. Audio input costs $0.30 per million audio input tokens. All rates are in USD.
What is the context length of Gemini 2.5 Flash-Lite?
Gemini 2.5 Flash-Lite has a 1,000,000-token context window through the Impossibl API.
What inputs and outputs does Gemini 2.5 Flash-Lite support?
Gemini 2.5 Flash-Lite accepts text, image, audio, and video as input. Gemini 2.5 Flash-Lite returns text.
How do I use Gemini 2.5 Flash-Lite through an API?
Gemini 2.5 Flash-Lite is available through the Impossibl API. Use google/gemini-2.5-flash-lite as the model identifier with /v1/chat/completions. The quickstart explains how to create an API key and send your first request.
When was Gemini 2.5 Flash-Lite released?
Google released Gemini 2.5 Flash-Lite on July 22, 2025.
this page describes Gemini 2.5 Flash-Lite as listed by the impossibl public api. prices and availability come from that catalog, refreshed here every five minutes. this is an impossibl offering, not a comparison of independent hosting-provider quotes. model descriptions and release dates, where available, use the creator sources linked under the answers above.
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