gemini-2.5-flash-lite-preview-09-2025

completions

byopenrouter

gemini-2.5-flash-lite-preview-09-2025

completions

Published by: googleProvider:

openrouter

Gemini 2.5 Flash-Lite is a lightweight reasoning model in the Gemini 2.5 family, optimized for ultra-low latency and cost efficiency. It offers improved throughput, faster token generation, and better performance across common benchmarks compared to earlier Flash models. By default, "thinking" (i.e. multi-pass reasoning) is disabled to prioritize speed, but developers can enable it via the [Reasoning API parameter](https://openrouter.ai/docs/use-cases/reasoning-tokens) to selectively trade off cost for intelligence.

Released

Sep 8, 2025

Knowledge

Jan 1, 2025

Context

1048576

Input

$0.1 / 1M tokens

Output

$0.4 / 1M tokens

Capabilities: tools, reasoning

Accepts: text, image

Returns: text

Released Sep 8, 2025Knowledge Cutoff: Jan 1, 2025

Context: 1048576 Input: $0.1 / 1M tokensOutput: $0.4 / 1M tokensCapabilities: tools, reasoningAccepts: text, imageReturns: text

Access gemini-2.5-flash-lite-preview-09-2025 through LangDB AI Gateway

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Integrate with google's gemini-2.5-flash-lite-preview-09-2025 and 250+ other models through a unified API. Monitor usage, control costs, and enhance security.

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99.9% Uptime

10,000+Monthly Requests

Benchmark Results for gemini-2.5-flash-lite-preview-09-2025

Category Performance Scores:

Maths: Score 68.70 (Top 35% - Rank #113)
Finance: Score 58.30 (Top 31% - Rank #100)
Science: Score 67.61 (Top 39% - Rank #126)
Writing: Score 59.40 (Top 24% - Rank #78)
Academia: Score 59.40 (Top 32% - Rank #104)
Marketing: Score 62.02 (Top 19% - Rank #62)
Programming: Score 36.50 (Top 41% - Rank #133)

Overall Performance: 58.845079365079364 average score across all categories

Detailed Benchmark Scores:

Benchmark	Score	Percentile	Domain
HLE	6.60	Top 36%	General Knowledge
GPQA	70.90	Top 36%	STEM (Physics, Chemistry, Biology)
SciCode	28.70	Top 57%	Scientific
MMLU-Pro	80.80	Top 33%	General Knowledge
LiveCodeBench	68.80	Top 23%	Programming
AA Math Index	68.70	Top 35%	Mathematics
AA Coding Index	36.50	Top 41%	Programming
AAII	47.90	Top 27%	General

GPQA Score: 70.90 - Graduate-level reasoning benchmark

Model Comparison:

Provider: openrouter

Model Type: completions

Context Size: 1048576 tokens

Comparing against 322 models in the database

Performance Analytics for gemini-2.5-flash-lite-preview-09-2025

Usage Statistics (Last 1 Days):

Total Requests: 1 API calls
Average TPS: 6603.13 tokens per second
Average Response Time: 2389.30ms
Average Time to First Token: 955.90ms
Total Cost: $0.00
Average Request Cost: $0.0020

Daily Performance Breakdown:

Date	Requests	TPS	Response Time	TTFT	Cost
11/11/2025	1	6603.13	2389.30ms	955.90ms	$0.00

Performance Summary:

Model: gemini-2.5-flash-lite-preview-09-2025 by openrouter

Monitoring Period: 11/11/2025 to 11/18/2025

Average Daily Requests: 1

Peak Daily Requests: 1

Statistics

View Full Statistics

Request Volume

Daily API requests

Performance (TPS)

Tokens per second

6603.13 tokens/s

Category Scores

Benchmark Tests

View Other Benchmarks

HLE

6.6

General Knowledge

GPQA

70.9

STEM (Physics, Chemistry, Biology)

SciCode

28.7

Scientific

MMLU-Pro

80.8

General Knowledge

LiveCodeBench

68.8

Programming

AA Math Index

68.7

Mathematics

AA Coding Index

36.5

Programming

AAII

47.9

General

Metric	HLE	GPQA	SciCode	MMLU-Pro	LiveCodeBench	AA Math Index	AA Coding Index	AAII
Score	6.6	70.9	28.7	80.8	68.8	68.7	36.5	47.9

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Code Examples

Integration samples and API usage

Code Samples for gemini-2.5-flash-lite-preview-09-2025

Python SDK Example:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.us-east-1.langdb.ai//projects/<your_project_id>",
    api_key="<your_api_key>"
)

response = client.chat.completions.create(
    model="gemini-2.5-flash-lite-preview-09-2025",
    messages=[
        {"role": "user", "content": "Hello, how are you?"}
    ]
)

print(response.choices[0].message.content)

TypeScript SDK Example:

import OpenAI from 'openai';

const client = new OpenAI({
    baseURL: "https://api.us-east-1.langdb.ai//projects/<your_project_id>",
    apiKey: "<your_api_key>"
});

const response = await client.chat.completions.create({
    model: "gemini-2.5-flash-lite-preview-09-2025",
    messages: [
        { role: "user", content: "Hello, how are you?" }
    ]
});

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

cURL Example:

curl -X POST "https://api.us-east-1.langdb.ai//projects/<your_project_id>/v1/chat/completions" \
  -H "Authorization: Bearer <your_api_key>" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-2.5-flash-lite-preview-09-2025",
    "messages": [
        {"role": "user", "content": "Hello, how are you?"}
    ]
  }'

Model: gemini-2.5-flash-lite-preview-09-2025

Provider: openrouter

API Endpoint: $https://api.us-east-1.langdb.ai/

Create API Key

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gemini-2.5-flash-lite-preview-09-2025 by openrouter - AI Model Details, Pricing, and Performance Metrics

gemini-2.5-flash-lite-preview-09-2025

gemini-2.5-flash-lite-preview-09-2025

Access gemini-2.5-flash-lite-preview-09-2025 through LangDB AI Gateway

Statistics

Category Scores

Benchmark Tests

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Code Examples

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