Google: Gemini 3.6 Flash
google/gemini-3.6-flash
Access Gemini 3.6 Flash 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-3.6-flash"
}).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-3.6-flash"
}).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-3.6-flash",
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-3.6-flash",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
Gemini 3.6 Flash is Google's workhorse Flash-tier model, released as the successor to Gemini 3.5 Flash. It's built for running AI agents in production, with improvements in coding precision, computer use, and multimodal understanding.
In Google's own benchmarks, it scores 83.0% on OSWorld-Verified (up from 78.4% for 3.5 Flash), 49% on DeepSWE (up from 37%), 63.9% on MLE-Bench (up from 49.7%), and 58.7% on SWE-Bench Pro. It also produces 17% fewer output tokens than 3.5 Flash on comparable tasks.
It accepts text, image, video, audio, and PDF input with a 1M token context window, supports function calling and a built-in computer-use tool, and has a March 2026 knowledge cutoff. At $1.50 per million input tokens and $7.50 per million output tokens, it's cheaper on output than 3.5 Flash's $9 rate.
Context Window 1M
tokens
Max Output 66K
tokens
Input Cost $1.5
per million tokens
Output Cost $7.5
per million tokens
Input text, image, video, audio, pdf
modalities
Tool Use Yes
Knowledge Cutoff Mar 2026
Release Date Jul 21, 2026
Output Speed 301
tokens / sec
Latency 11.80s
time to first token
Model Playground
Try Gemini 3.6 Flash instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How Gemini 3.6 Flash performs on standard evaluations.
| Benchmark | Score |
|---|---|
| GPQA Diamond Graduate-level science Q&A | 92.8% |
| Humanity's Last Exam Cross-domain reasoning | 38.3% |
| SciCode Scientific programming | 52.7% |
| LCR Long-context reasoning | 69.7% |
Scores sourced from Artificial Analysis.
Find other Google models →
Gemini 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.
ImageGemini 3.1 Flash Lite Image
Gemini 3.1 Flash Lite Image is Google DeepMind's fastest and lowest-cost image generation and editing model in the Gemini 3.1 family, also known as Nano Banana 2 Lite. It generates a 1K-resolution image in about 4 seconds, roughly 2.7x faster than Gemini 3.1 Flash Image, at $0.0336 per image. It keeps the character consistency, precise natural-language editing, and real-world knowledge of the Nano Banana family while trading some peak quality for speed. It supports text-to-image generation, image editing, and multi-image composition through the same API used by other Gemini image models, with a discrete set of 14 aspect ratios at 1K resolution. It's built for high-volume, latency-sensitive use cases such as rapid prototyping, interactive applications, and batch image generation, where cost and turnaround time matter more than maximum fidelity.
ChatGemini 3.5 Flash
Gemini 3.5 Flash is Google DeepMind's frontier-speed model that combines Flash-tier latency and cost with near-Pro-level reasoning, announced at Google I/O 2026. It processes output 4x faster than comparable frontier models while outperforming Gemini 3.1 Pro on coding and agentic benchmarks — 76.2% on Terminal-Bench 2.1, 83.6% on MCP Atlas, and 84.2% on CharXiv Reasoning. It's purpose-built for agentic workflows: orchestrating multi-step tool use, long-context document analysis, and iterative code generation. With a 1M token context window and full multimodal input support (text, image, audio, video, PDF), it handles complex real-world tasks at scale. At $1.50 per million input tokens and $9.00 per million output tokens, it's the best choice for developers who need frontier intelligence without frontier latency or cost.
Frequently Asked Questions
You can access Gemini 3.6 Flash 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 3.6 Flash 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 | $1.5 |
| Output | $7.5 |
Gemini 3.6 Flash was created by Google and released on Jul 21, 2026.
Gemini 3.6 Flash supports a context window of 1M tokens. For reference, that is roughly equivalent to 2,097 pages of text.
Gemini 3.6 Flash can generate up to 66K tokens in a single response.
Gemini 3.6 Flash has a knowledge cutoff date of Mar 2026. This means the model was trained on data available up to that date.
Gemini 3.6 Flash accepts the following input types: text, image, video, audio, pdf. It produces: text.
Yes, Gemini 3.6 Flash supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
Gemini 3.6 Flash scores 50.1 on the Artificial Analysis Intelligence Index, outperforming 96% of tracked models. On coding, it scores 69.2 (outperforms 89% of models).
Yes — the Gemini 3.6 Flash 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 3.6 Flash to your app without worrying about API keys or setup.
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