Google: Gemini 3.5 Flash-Lite
Try Gemini 3.5 Flash-Lite for free in your browser, and add it to your app for free with Puter.js AI API.
Try it free Add to your app// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "google/gemini-3.5-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-3.5-flash-lite"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
Model Card
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.
Context Window 1M
tokens
Max Output 66K
tokens
Input Cost $0.3
per million tokens
Output Cost $2.5
per million tokens
Input text, image, video, audio, pdf
modalities
Tool Use Yes
Knowledge Cutoff Jan 2025
Release Date Jul 21, 2026
Output Speed 394
tokens / sec
Latency 4.67s
time to first token
Try Gemini 3.5 Flash-Lite for free
Try Gemini 3.5 Flash-Lite instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How Gemini 3.5 Flash-Lite performs on standard evaluations.
| Benchmark | Score |
|---|---|
| GPQA Diamond Graduate-level science Q&A | 83.8% |
| Humanity's Last Exam Cross-domain reasoning | 18.8% |
| SciCode Scientific programming | 41.3% |
| LCR Long-context reasoning | 76.0% |
Scores sourced from Artificial Analysis.
Find other Google models →
Gemini 4 Argon
Gemini 4 Argon is Google's flagship Gemini 4 model, announced by Google DeepMind on September 30, 2026. It is aimed at long, multi-step software engineering, legal and finance work, and defensive cybersecurity. Google reports 77.9% on DeepSWE v1.1 (Claude Opus 5.5 74.2%, GPT-6 Astra 74.1%) and 19.6% on Harvey's Legal Agent Benchmark. It trails Opus 5.5 on Terminal-bench 4.0 (57.4% vs 66.4%) and GPT-6 Astra on FrontierSWE v2 (55.0% vs 65.5%). Vals.ai places it first on the Vals Index at 68.9%. Introductory pricing is $2 per million input tokens and $10 per million output tokens, rising to $4 and $20 later. It suits teams running coding agents or document-heavy professional workflows.
ImageGemini Nano Banana 2.1
Gemini Nano Banana 2.1 is Google's image generation and conversational editing model, released in October 2026 as an update to Nano Banana 2 (Gemini 3.1 Flash Image). It keeps Flash-tier speed and cost while improving visual quality, prompt adherence, text rendering, and multi-turn character consistency. It accepts text, image, video, and PDF input and returns images and text. Output is available at 1K, 2K, and 4K, with fixes for tiling artifacts on wide aspect ratios such as 1:8 and 8:1 at 2K and 4K. It can combine up to 14 reference images, and supports search grounding and configurable thinking levels. A 1K image costs $0.0336. Choose it for infographics, text-heavy graphics, and multi-step editing where the same characters must stay consistent across turns.
ChatGemini 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.
Frequently Asked Questions
You can access Gemini 3.5 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.
Gemini 3.5 Flash-Lite is free to try with a Puter account. Every account includes a free AI allowance, and you can chat with it in the playground on this page. You can upgrade your account anytime for a larger allowance.
Gemini 3.5 Flash-Lite is free to integrate using the Puter.js AI API. With the User-Pays Model, you can add AI to your app for $0, since users cover their own AI usage through their Puter account.
| Price per 1M tokens | |
|---|---|
| Input | $0.3 |
| Output | $2.5 |
Gemini 3.5 Flash-Lite was created by Google and released on Jul 21, 2026.
Gemini 3.5 Flash-Lite supports a context window of 1M tokens. For reference, that is roughly equivalent to 2,097 pages of text.
Gemini 3.5 Flash-Lite can generate up to 66K tokens in a single response.
Gemini 3.5 Flash-Lite has a knowledge cutoff date of Jan 2025. This means the model was trained on data available up to that date.
Gemini 3.5 Flash-Lite accepts the following input types: text, image, video, audio, pdf. It produces: text.
Yes, Gemini 3.5 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 3.5 Flash-Lite scores 22.2 on the Artificial Analysis Intelligence Index, outperforming 69% of tracked models. On coding, it scores 49.3 (outperforms 55% of models).
Yes — the Gemini 3.5 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.
Add Gemini 3.5 Flash-Lite to your app for free
Developers can integrate Gemini 3.5 Flash-Lite for free using the Puter.js AI API.
With the User-Pays Model, each user covers their own AI usage instead of the developer.