Google: Gemini 2.5 Flash-Lite
google/gemini-2.5-flash-lite
Access Gemini 2.5 Flash-Lite from Google using Puter.js AI API.
Get Started// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "google/gemini-2.5-flash-lite"
}).then(response => {
document.body.innerHTML = response.message.content;
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain quantum computing in simple terms", {
model: "google/gemini-2.5-flash-lite"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
# pip install openai
from openai import OpenAI
client = OpenAI(
base_url="https://api.puter.com/puterai/openai/v1/",
api_key="YOUR_PUTER_AUTH_TOKEN",
)
response = client.chat.completions.create(
model="google/gemini-2.5-flash-lite",
messages=[
{"role": "user", "content": "Explain quantum computing in simple terms"}
],
)
print(response.choices[0].message.content)
curl https://api.puter.com/puterai/openai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_PUTER_AUTH_TOKEN" \
-d '{
"model": "google/gemini-2.5-flash-lite",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
Gemini 2.5 Flash-Lite is Google's cost-optimized version of 2.5 Flash, designed for high-volume tasks like classification, translation, and intelligent routing. It delivers efficient performance for cost-sensitive, high-scale operations.
Context Window 1M
tokens
Max Output 66K
tokens
Input Cost $0.1
per million tokens
Output Cost $0.4
per million tokens
Input text, image, audio, video, pdf
modalities
Tool Use Yes
Knowledge Cutoff Jan 2025
Release Date Jun 17, 2025
Model Playground
Try Gemini 2.5 Flash-Lite instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How Gemini 2.5 Flash-Lite performs on standard evaluations.
| Benchmark | Score |
|---|---|
| GPQA Diamond Graduate-level science Q&A | 47.4% |
| Humanity's Last Exam Cross-domain reasoning | 3.7% |
| LiveCodeBench Recent coding problems | 40.0% |
| SciCode Scientific programming | 17.7% |
| MATH-500 Competition math | 92.6% |
| AIME 2024 Advanced math exam | 50.0% |
| AIME 2025 Advanced math exam | 35.3% |
| IFBench Instruction following | 31.5% |
| LCR Long-context reasoning | 32.0% |
| Terminal-Bench Hard Agentic terminal tasks | 2.3% |
| τ²-Bench Tool use / agents | 19.0% |
Scores sourced from Artificial Analysis.
Find other Google models →
Gemini 3.8 Flash
Gemini 3.8 Flash is Google's workhorse Flash-tier model, released September 2, 2026, three weeks after Gemini 3.7 Flash. It's built for long-horizon software engineering, agentic workflows, and multi-step reasoning in professional domains. Google reports it outperforms 3.7 Flash and other frontier models on DeepSWE v1.1 for autonomous engineering tasks, on Vals Finance Agent V2, and on Harvey's Legal Agent Benchmark. It scores 54.9% on HLE-Verified, and completes more than three times as many tasks as 3.7 Flash in Google's long-running, document-heavy workflow evaluations. It accepts text, image, video, audio, and PDF input with a 1M token context window and 64K token output limit, supports function calling and iterative tool use, and has a March 2026 knowledge cutoff. Priced at $0.75 per million input tokens and $3.75 per million output tokens through 2026, it targets teams running coding agents or document-heavy enterprise workflows.
ChatGemini 3.7 Flash
Gemini 3.7 Flash is Google's workhorse Flash-tier model, released August 13, 2026, three weeks after Gemini 3.6 Flash. It's built for coding and agentic workflows, targeting software engineering, web development, and knowledge-dense domains like finance and law. Google reports gains over Gemini 3.6 Flash on several benchmarks. DeepSWE v1.1 rose from 49.0% to 65.3%, FrontierCode 1.1 from 34.4% to 43.6%, and AutomationBench from 17.0% to 30.4%. On FrontierCode 1.1 it scores above Claude Sonnet 5 (42.7%) and GPT-5.6 Terra (41.3%), though GPT-5.6 Terra edges it out on Terminal-bench 2.1 (87.4% vs 85.8%). It accepts text, image, video, audio, and PDF input with a 1M token context window and 64K token output limit. It supports function calling, search as a tool, and computer use, and has a March 2026 knowledge cutoff. It's priced at roughly half of Gemini 3.6 Flash's rate, fitting teams running coding agents or high-volume document processing.
ChatGemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite is Google's fastest and most cost-efficient model in the Gemini 3.5 series, built for high-throughput, low-latency workloads. It scores 54% on Terminal-Bench 2.1 and 72.2% on GDM-MRCR v2, up from 31% and 60.1% for Gemini 3.1 Flash-Lite. It also outperforms the larger Gemini 3 Flash on SWE-Bench Pro (54.2% vs. 49.6%) and OSWorld-Verified (74.0% vs. 65.1%), while generating output at roughly 350 tokens per second. It supports text, image, video, audio, and PDF input with a 1M token context window, configurable thinking levels, and function calling, including computer use as a built-in tool. It's suited for agentic search, document processing, and other high-volume tasks where throughput and cost matter more than maximum reasoning depth.
Frequently Asked Questions
You can access Gemini 2.5 Flash-Lite by Google through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.
Yes, it is free if you're using it through Puter.js. With the User-Pays Model, you can add Gemini 2.5 Flash-Lite to your app at no cost — your users pay for their own AI usage directly, making it completely free for you as a developer.
| Price per 1M tokens | |
|---|---|
| Input | $0.1 |
| Output | $0.4 |
Gemini 2.5 Flash-Lite was created by Google and released on Jun 17, 2025.
Gemini 2.5 Flash-Lite supports a context window of 1M tokens. For reference, that is roughly equivalent to 2,097 pages of text.
Gemini 2.5 Flash-Lite can generate up to 66K tokens in a single response.
Gemini 2.5 Flash-Lite has a knowledge cutoff date of Jan 2025. This means the model was trained on data available up to that date.
Gemini 2.5 Flash-Lite accepts the following input types: text, image, audio, video, pdf. It produces: text.
Yes, Gemini 2.5 Flash-Lite supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
Gemini 2.5 Flash-Lite scores 6.7 on the Artificial Analysis Intelligence Index, outperforming 20% of tracked models. On math, it scores 35.3 (outperforms 35% of models).
Yes — the Gemini 2.5 Flash-Lite API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.
Get started with Puter.js
Add Gemini 2.5 Flash-Lite to your app without worrying about API keys or setup.
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